Wireless Electrode for Electrocardiogram (ECG) Signal · Clear ECG signals received using this...

Wireless Electrode for Electrocardiogram (ECG) Signal

By

LEUNGSze-wing

A Thesis Submitted in Partial Fulfillment of the Requirements

For the Degree of Master Philosophy

In

Electronic Engineering

• The Chinese University of Hong Kong

July, 1999

The Chinese University of Hong Kong holds the copyright ofthis thesis. Any person(s) intending

to use a part or whole of the materials in the thesis in a proposed publication must seek copyright

release from the Dean of the Graduate School.

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Acknowledgement

I owe Prof. Y.T. Zhang, my supervisor, for his guidance, patience and kindness during

the course of research in the past two years.

I would like to thank Mr. S.M. Chu, our Senior Laboratory Superintendent, for his

valuable advice and kind kelp. Gratitude should be sent to Mr. S.Y. Cheung, Mr. W.L.

Chu, K.F. Yuen and Peter AuYeung for their help in hardware and software

implementations. I also owe people in the Information Engineering Department,

CUHK. Mr. Ma Yi-guang helped me a lot in the wireless aspects of this work. Dr.

Albert Sung and Mr. K.K. Leung gave me inspirations and supports.

I would like to thank all my colleagues in the Biomedical Engineering Laboratory,

Electronic Engineering Department, CUHK who gave me supports and help.

Lastly, I would like to thank my family members and my friends for their kind supports

and care.

ii

Abstract

A novel scheme of ECG telemetric monitoring with a single electrode assembly via

concentric electrodes was investigated. It is called the "Wireless Electrode".

Digitization with a kind of oversampling converter, the Sigma-Delta converter, was

under study especially for its wireless application. Prototypes for telemetry application

applying this technique with the concentric electrode were implemented. Simulation

and Experimental results were obtained.

Heart Diseases are prevalent nowadays. This is especially true in the developed

countries. Easy and prompt diagnosing and detecting heart diseases are essential.

Electrocardiogram (ECG) monitoring is a usual diagnostic tool for this purpose. Two

or more electrodes with linking wires are usually required to acquire the signal. With

telemetric monitoring, amount of wires has been reduced. In order to reduce further

the amount of nuisance from the electrodes and wires, a novel scheme of applying a

single electrode assembly with concentric electrode and radio telemetry is devised.

Signals were picked up from concentric electrode in this scheme. Therefore, a single

electrode assembly in electrode size can be achieved. Radio telemetry was applied for

wirelessly monitoring of the ECG signal. No interconnecting wires are then required

for monitoring. Digital transmission was implemented with a kind of oversampling

converter. The kind of converter is named as Sigma-Delta Converter. This kind of

converter is simple in analogue circuitry. The circuit simplicity is essential in

telemetry device that can reduce the size of the patient-wom transmitter. In this work, a

first-order Sigma-Delta converter was investigated and it was built with simple

operational amplifier (opamp) and digital circuits.

The converter has basically one-bit binary output. Signals are reconstructed by either

iii

analogue or digital filtering from the binary stream.

The 1-bit binary stream was sent via telemetry in this work. Bit Errors were generated

in the communication process. The situation was simulated. AWGN (Additive White

Gaussian Noise) was applied simulating the occurrence of bit errors. It was observed

that the performance of the first-order Sigma-Delta converter could be better than an

ideal 8-Bit converter in some cases under this situation. Clear ECG signals received

using this scheme were observed in the experimental results.

Therefore, the application of a “Wireless Electrode “ scheme in ECG monitoring is

feasible and the digitization with the simple Sigma-Delta converter can be a useful

component in the digital transmission of ECG signal in the telemetric application.

iv

摘要：

本論文探討一名為“無線電極〃的創新無線心電訊號監護工具。這工具是通過同心電

極而達成的。此外’我們也探討了一種名為Sigma-Delta的Oversampling模-數(A-D)轉

換器在心電訊號模-數轉換上的應用。我們在其中製作了利用以上技術加上同心電極在

無線監護上應用的雛形線路°并且，當中我們也取得了實驗及計算機模擬的結果。

心職病在現今的社會是一個十分普遍的。這情况在已發展國家尤甚。一種對心赋病方

便、及時的診斷工具是必須的°心電圖便是其中一個常用的診斷工具。一般而言，量

度心電圖時須用兩個或以上的電極。其中也須用上不少連接電線。若在無線監護的情

况下，電線的數量已有所減低。為了更加減低電極及其中連接線數量，一個利用單一

封裝電極加上無線傳输的創新的方案因而產生。

心電訊號在這方案中是由一同心電極取得的。一電極大小、單一封裝的監護工具因此

能達成。再加上採用了無線傳输的技術’監護過程中是不須任何連接電線的。另外，

我們在數字傳輸方面採用了 Signm-Delta的模-數轉換方法。這方法的好處在於其在模

擬電路上的簡單性° 一電路簡單的無線監護器件是有利的。因為這樣便能讓病人身掛

的監護無線發射器微形化。在這個研究中，我們探討了一階的Sigma-Del ta模-數轉換

器（First-order Sigma-Delta Converter) ,并且利用了簡單的運算放大器及數字電路來實現

此模-數轉換器。此模-數轉換器的輸出是單位元的。其數-模（D-A)轉換可透過模擬或

數字濾波器達成的。

模-數轉換器的單位元輸出在這研究中是利用無線方式傳播出去的。在這個傳輸過程

中，位元的語差 ( B i t E r r o r s )是會產生的°我們利用了計算機來模擬這過程。其中我們

應用了 AWGN(AMtive White Gaussian Noise)來模擬Bit Errors的產生的情况。從中我

們發現在這種應用環境下在某些條件下一階的Sigma-Del t a模-數轉換器的表現能較一

理想的八位元模-數轉換器為佳°此外，我們亦能在實驗結果中得到清晰接收得來的心

電訊號。

總括而言，“無線電極“在心電監護的應用上是可行的。而簡單的Sigma-De l t a模-數

轉換器能成為‘c^�電訊號數字無線傳輸的一實用的組成部份。

V

Contents ACKNOWLEDGEMENT II

ABSTRACT III

m^- V

CONTENTS VI

CHAPTER 1 INTRODUCTION 1

1.1 OBJECTIVES 1

1.2 PREVALENCE OF HEART DlSEASES 1

1.3 IMPORTANCE OFECG MONITORING 2

1.4 WlRELESS ELECTRODE 2

1.5 ANALOGUE-TO-DlGITAL CONVERTERS 3

1.6 ORGANIZATION OF THESIS 4

CHAPTER 2 LITERATURE REVIEW 5

2.1 TELEMETRY 5

2.1.1 Definitions of "Telemetry “ j

2.1.2 Advantages ofTelemetry 6

2.1.3 History of Telemetry 7

2.1.4 Special Considerations on Telemetry System 10

2 .2 SlGMA-DELTA CONVERTER 12

2.2.7 Conventional Digitizing Circuitry ]2

2.2.2 Single, Dual-Slope A/D Converters 13

Single-Slope A/D Converter j5

Dual-Slope Converter.

vi

2.2.3 Successive Approximation (SAR) 17

2.2.4 Flash Converters 18

2.2.5 Sigma-Delta Converter 18

2.3 CONCLUSION 20

CHAPTER 3 WIRELESS ELECTRODE 21

3.1 "SlNGLE ELECTRODE" MEASUREMENT 21

3.2 V S E (VIRTUAL SlNGLE ELECTRODE) 21

Concentric Electrode 21

3.3 W E (WlRELESS ELECTRODE) 24

3.4 Discuss iON 29

CHAPTER 4 SIGMA-DELTA CONVERTER FOR ECG SIGNALS 30

4.1 MOTIVATIONS 30

4.2 BASEBAND APPLICATION 31

4.2.1 Simulation Results J J

4.2.2 Experimental Results 48

4.3 WlRELESS APPLICATION 58

4.3.1 General Description 55

4.3.2 Simulation Results 59

4.3.3 Scenario I (Analogue Decoding) 70

4.3.4 Scenario II (Digital Decoding) 73

4.4 DISCUSSION AND CONCLUSION 76

CHAPTER 5 CONCLUSION AND FUTURE WORK 77

5.1 GENERAL CONCLUSION 7 7

5.2 FUTURE WORK

vii

BIBLIOGRAPHY 79

LIST OFABBREVIATIONS 85

viii

Chapter 1 Introduction

1.1 Objectives In this research, we would like to investigate the application of a "Wireless Electrode"

with only single electrode assembly for easy and prompt monitoring of patients'

Electrocardiogram (ECG). In the mean time, the Sigma-Delta converter is investigated

for its application to digitizing ECG signal due to its simplicity in analogue circuitry

that promotes the miniaturization of Telemetry devices.

1.2 Prevalence of Heart Diseases In Hong Kong, heart diseases ranked the second killer in the ten leading causes of

death in the past 30 years [l][2]. In 1996，17% and 12% of total deaths in urban and

rural areas of Mainland China were reported to be due to heart trouble [3]. Heart

disease belongs to the class of circulatory system disease group. In the whole world

about 30% (15.3 million) were due to circulatory system. Most deaths from circulatory

diseases were coronary heart disease (7.2 million), cerebrovascular disease (4.6

million), other heart diseases (3 million) [4][5].

While deaths due to circulatory diseases declined from 51% to 46% of total deaths in

the developed world during the period 1985-1997, they increased from 16% to 24% of

total deaths in the developing world [5]. From these facts, we can see that the

importance of prompt and easy diagnosis and detection of heart diseases in order to

ease the situation of high death rate due to heart problems.

1

1.3 Importance of ECG Monitoring One of the useful diagnostic tools for heart disease is the Electrocardiogram (ECG).

Analyses of them are common diagnostic procedures in modern healthcare and

monitoring of the ECG and heat rate in intensive care is providing additional

information [6]. Originally, ECG is monitored with bulky wires running between

patient and machines. With advance of wireless technologies, the wires can be

eliminated and this wireless monitoring method is called the ECG telemetry which

offer various advantages over the original wired version. Details on Telemetry will be

given in Chapter 2.

1.4 Wireless Electrode Two or more electrodes are usually required in performing ECG telemetry, with wires

connecting the electrodes to the transmitter transmitting the ECG signal to the air.

Special assemblies of the electrodes have been made eliminating the interconnecting

wires [7]-[10]. Although the interconnecting wires are removed, two or more

disposable electrodes are still needed to acquire the ECG signals. In order to further

reduce the complexity of measurement setup, we would like to ask a question:

� Is it possible that by using a single electrode assembly, ECG signal can be

picked upfor diagnosis and detection ofheart diseases?

(2j And is it possible to apply this scheme in telemetry application making it a single

"WirelessElectrode"?

In this research, we would like to investigate on the above questions in more detail.

2

1.5 Analogue-to-Digital Converters Digital transmission, nowadays, is usually used in telemetry devices. With digital

transmissions, Analogue-to-Digital Converters (A/D converters) are required for

transforming the analogue signal into binary form. There are different kinds of A/D

converters available, each has its own strengths and weaknesses. Most often, the

sampling rate is about 2 times the signal bandwidth and the Successive Approximation

converter is used for ECG telemetry. Another type of AfD converters operates with a

much higher sampling rate that is called the Oversampling converters. One example is

the Sigma-Delta converter. It gives 1-bit binary output and it is an attractive converter

since it is composed of simple and robust analogue circuitry that is especially

beneficial to IC fabrication process. It is also renown for its capability of offering

high-resolution A/D conversions. To achieve high resolution, complex Digital Signal

Processing (DSP) units should be incorporated. It can be illustrated in Figure 1.1.

/ l-Bit Binary Output

Input Signal g / U ^ ^ Sigma-Delta Converter _ / ^ DSP Unit B ‘

"“^1- _ n _ J ^ Multi-Bits High Resolution

Binary Output

Figure 1.1 Sigma-Delta Converter Block Diagram

Usually, these DSP units are fabricated within a single chip assembly. In this research,

we would to investigate whether it is possible to separate the two parts in a telemetry

application. The first part, the simple Sigma-Delta converter with 1-bit output, is

3

placed in the transmitter, while another part, the DSP unit, is replaced by a computer in

the remote monitoring station. It is depicted in Figure 1.2.

y ^ Sigma-Delta Converter _ Transmitting _

(l-BitBinary Output) _ Circuit |

i i M i — t t W M t t t o s i iiteMiiMWiJ Patient-Worn Transmitter

X w —— � ‘ Computer

Receiver J H ~ ^ Replacing the

Circuit | ^ ^ M DSP Unit

™

Remote Monitoring Station

Figure 1.2 Sigma-Delta Converter with Computer replacing the DSP unit

Is this scheme feasible? Is there any degradation of performance of the Sigma-Delta

converter? These questions will be taken into account in this research as well.

1.6 Organization of Thesis This chapter forms an introductory part of thesis. Main objectives and themes are

illustrated in this chapter. In Chapter 2’ literature review on several main topics will be

given. The main topics are Telemetry, and Sigma-Delta converter. In Chapter 3, 4’ the

implementation of the wireless electrode and the application of Sigma-Delta converter

in digitizing ECG signal will be discussed respectively. Conclusions, with future

research work discussed, will be presented in Chapter 5, the last chapter. Bibliography

and a brief list of abbreviations will be given lastly.

4

Chapter 2 Literature Review In Chapter 1，we have seen that heart diseases are prevalent in the world nowadays.

Early detection of heart diseases is important and ECG (Electrocardiogram) is one

useful diagnostic tool in modern health care. Patients under ECG monitoring will have

wires running between them and the ECG machines. These wires cause nuisance to the

patients making them staying still during the monitoring period. Telemetry technique

is therefore applied to ease this situation. With ECG telemetry, patients can be mobile

and this offers advantages over the "wired" version ofECG monitoring. In this chapter,

literature review on several topics will be presented that are closely related to

Telemetry. The first one will be on the topic of Telemetry itself, its definition, history

and components. Second, since telemetry nowadays is usually digital in nature,

digitization or analogue-to-digital conversion is a common process. In Chapter 1，we

have introduced the Sigma-Delta converter. K is one of the digitization methods. There

are also other types like Single-Slope, Dual-Slope, and Successive-Approximation

converters. These converters, along with the Sigma-Delta type will be discussed in this

chapter.

t

2.1 Telemetry In this section, the topic "Telemetry" will be reviewed. Definitions, Advantages, and

Compositions of Telemetry will be presented hereafter.

2.1.1 Definitions of “Telemetry”

"Biotelemetry or obtaining biologic information from afar has made incredible

advances over the past 50 years" by Harold Sandler, "Biotelemetry: Its First 50 Years",

Proceedings of the 3rd International Symposium on Biotelemetry, ppl , 1976.

5

By the above quote, it was shown that biotelemetry can be equivalent to "obtaining

biologic information from afar. And in [17], another author, Kimmich gave a thorough

account on the definitions of "biotelemetry". He stated that the main criterion for the

definition of "Telemetry" is "assessment of control of biological variables from

patients, subjects, or animals, with relatively little restraint of the patient/animal

leading to undisturbed and distortion-free measurement of the physiological variables.

Therefore, the distance may not be definitive element for the definition of "Telemetry"

Telemetry is a broad term and can be equivalent to various alternatives like ambulatory

monitoring, wireless monitoring, radiotracking, telediagnosis [17，pp85]. It can be

applied on human or animals. When it is utilized on humans, it is sometimes called the

medical telemetry, [20，ppl85].

It was suggested in [18] that the goals of biotelemetry include the capability for

monitoring humans and animals with minimum restraint and to provide faithful

reproduction of the transmitted data. By adopting this meaning of "Biotelemetry",

even wired application like transtelephonic transmission of ECG signals is also

considered to be one kind of Biotelemetry.

In view of these broad definitions of "Telemetry" therefore, in this thesis, we would

like to restrict the definition of "Telemetry" to radiotelemetry or Wireless Telemetry,

where the information is transmitted wirelessly in between the patients (human) and

the monitoring stations for display or further processing, [21, p l l 3 ] .

2.1.2 Advantages of Telemetry

Though the broad meanings of "Telemetry", it is advantageous of applying telemetry.

Optimal interference reduction and freedom of movement of the patient are retained

[50][32]. This enables patient to move around a wider area while still being monitored,

the need for lead wires between patient and monitor eliminated [51]. Patient is totally

6

isolated electrically and has ambulatory freedom, which allows continuous,

uninterrupted monitoring of physiological parameters [20]. Kuiper [21] added that the

application telemetry also leads to the reduction of psychological effects on the

patient.

2.1.3 History of Telemetry

Telemetry or Biotelemetry has been widely used since the invention of the transistors

in the 1950's, [20, ppl85]. Jeutter [18] also dated the history of biotelemetry back to

the 1950's Sandler, [23，pp2], on the other hand, suggest an even earlier birthday of

biotelemetry by the 1920's which began with the transmission of heart sounds and a

marine radio link in 1921 by Winters. This event was also reported by Mackay, [22,

pp l l ] . From then on, physiological parameters from either animals or humans are

transmitted via biotelemetry [23][19].

The physiological parameters of interest are usually the Electrocardiogram (ECG),

Electroencephalogram (EEG), Electromyogram (EMG), Acidity (pH), temperature

and pressure [18]. These signals vary greatly in terms of amplitude and frequency

spectrum. For instance, the ECG signals basically range from 0.1 to about 100 Hz

while for temperature, from 0 to 1 Hz. Amplitudes can be small in order of |iV as in the

case o f E E G [21 ,ppl l2] .

A biotelemetry system includes the sensor, transmitter, transmission medium, receiver

and a recipient [23’ pp4] which can be a displaying device or a recorder or a PC [21’

pp l l3 ] . This configuration constitutes a one-way communication or a simplex

communication. This configuration is defined in [21, p p l l 4 ] as the feature of medical

telemetry. However, other researchers [20, ppl87][24] suggested and demonstrated

duplex alternatives of telemetry system respectively.

The first part is the Sensor, it is consisted of a transducer and amplifiers. The

7

transducer is to bridge the gap between the physiological variable of interest and

electric circuits for picking the signals. Amplifiers are usually required to make minute

signal large and meanwhile enhance the signal quality (like increase S/N ratio).

Different configurations and features of amplifying circuitry existed due to the

differences in the physiological variables being measured.

Sensors for ECG telemetry are simple, it consisted of electrodes, and amplifiers. For

ECG measurements, various combinations of electrodes can be applied to give

different useful information on patient's heart activity. Each electrode combination is

called a "Lead". Basically, each lead consists of a pair of electrodes. For instance, in

standard 12-lead wired ECG measurements, Lead I is the electrode-pair of right and

left upper limbs.

For a single lead ECG telemetry system, which is the simplest one, at least requires

two electrodes. However, as stated Chapter 1，we have investigated the feasibility of

single electrode assembly measurements for ECG telemetry and this will be accounted

for in later section of this chapter. As for multi-lead ECG telemetry [24], more

electrodes are required and more amplifiers may be required for amplifying each lead

signal.

Following the amplifiers, signals may undergo further processing such as modulation

before being transmitted wirelessly. Modulation serves two main purposes: 1. for

multiplexing more than one signal; 2. for enhancing the signal quality when delivered

wirelessly. Firstly, for the sake of multiplexing, signals should be modulated somehow

before transmission. Two categories of modulation or multiplexing schemes exist.

They are the Frequency Division Multiplex (FDM), and the Time Division Multiplex

(TDM) [26’ pp279][18, ppl7-24]. For FDM, signals are separated by frequencies of

modulating oscillators. They are called the Subcarrier Oscillators (SCO's). Examples

can be found in [7][27] On the other hand, signals are separated in time intervals. It

8

involves sampling signals at designated time intervals [19, ppl8] and Pulse

Modulation Methods are used in coding the sampled signals before transmission.

Jeutter [19，ppl8-19] listed 5 of them: PAM (Pulse Amplitude Modulation), PDM

(Pulse Duration Modulation), PPM (Pulse Position Modulation), PFM (Pulse

Frequency Modulation), and PCM (Pulse Code Modulation). Details of these

modulation methods can be found in [28]. Examples with TDM or Pulse Modulation

can be found in [25][29][30]. PCM, being one of the key elements of digital

communication is usually used for telemetry system for its higher performance among

the other types of pulse modulation [19，pp21][28, pp444]. Examples with PCM can be

found in [25][29][31][32]. It can also be viewed in the commercial product

information in [33][34].

One of the methods of producing PCM of analog signals is the Sigma-Delta

Modulation, which is one of the main investigations of this research as stated in

Chapter 1. More details on PCM will be given in the following section on Sigma-Delta

Modulation.

For carrier modulation methods, usually FM (Frequency Modulation) and AM

(Amplitude Modulation) will be employed. On the other hand, with PCM, digital

modulation schemes can be incorporated. Common techniques like ASK (Amplitude

Shift Keying), FSK (Frequency Shift Keying), and PSK (Phase Shift Keying).

On the receiver side, signals are demodulated and recovered. Display devices or

recording devices are used as the final output. With availability of computers (PC's or

workstations), display and recording functions can be provided at the same time. With

network capabilities of computers nowadays, signals can be transmitted to very distant

places. An example of a world-wide network ECG monitoring system is illustrated in

[35]. In this system, patients can acquire their ECG's at home through PC-based

acquisition units and data can be sent through the worldwide web for display and

9

record. Fueled by the advance and accessible network technologies, the networked

recipients of telemetry system are getting more and more common.

2.1.4 Special Considerations on Telemetry System

special considerations should be paid on designing a biotelemetry system. In [23, p5],

11 categories or dimensions within a biotelemetry are listed and some of them are

given below:

1. Size

2. Range

3. Operating Frequency

Firstly, the size of transmitters of telemetry devices can be varied a lot, they can range

from pocket size to those which can be swallowed [22, p p l l ] or implanted inside

subjects' bodies. Mackay [22] gave an example of miniature telemetry system which

can be used on cockroaches. Other examples of small telemetry devices for

implantation can be viewed in [36]. The sizes of the telemetry devices are always

striking and no one would anticipate a big telemetry device that violates the main

element of being telemetry: reducing restrains on subjects, as stated in section 2.11 •

Jeutter [18, p p l l ] said that miniature is one the cornerstones of modern biotelemetry

design and construction which shows us that how important to design miniature

telemetry devices rather than some bulky devices.

Considering the range of operation of telemetry device, Scanlon [20，ppl86] divided

the ranges of telemetry system into 3 categories as shown in Table 2.1.

10

Range Transmission Aspects Application

Less than 30cm ^ e a k Inductive Coupling To interrogate

instrumentation circuits wom

by bed-bound or ambulatory

patients.

Less than 3m Transmitted power in |iW Situations requiring

order; Simple RF circuits; unattended monitoring by

Direct Modulation bedside Controllers

More than 30m i_5mW transmission power Useful in hospital wards and

may be need to secure signal in the home,

quality; more complex RF

circuits

Table 2.1 Categories of Telemetry Devices according to their ranges

Not only the ranges fall into different categories, the operating frequencies also can be

divided into several usual bands. Scanlon [20, ppl86-187] listed 4 main bands: LF

(300-500kHz), VHF (174-216 MHz), UHF (418-470 MHz, 902-928 MHz, 2.4-2.48

GHz). Kuiper [21] also listed out some bands for telemetry at that time. Examples in

the UHF band are like [7][37][38]. Example operating at even higher frequencies at

2.4GHz is by Grumley et al. [24]. Examples of lower operation frequencies in the

VHF band are [29][30][38][39][40]. Size and Operating Frequency are interconnected

as higher operating frequency can employ smaller antenna and thus reducing the size

of the telemetry device as increasing the operating frequencies making compact

radiators become more efficient [20, ppl87].

11

2.2 Sigma-Delta Converter In previous section, we have taken in account various aspects of Telemetry system.

PCM, being one of the pulse modulation methods used in telemetry, is receiving more

concern in nowadays telemetry. In PCM, signal samples are represented by a set of

binary numbers. Each sample will be assigned to one of 2^ amplitude levels, where R is

the number of binary digits used to represent each sample. This assigning process is

called quantization. The whole process comprising Sampling and Quantization can be

called "Digitization". There are various methods in achieving digitization and

corresponding circuitry is called the Analogue-to-Digital (A/D) Converter. These

methods will be discussed in the following sections.

2.2.1 Conventional Digitizing Circuitry

Conventional digitizing circuitry is usually Nyquist-rate converters. Li sampling an

bandlimited analogue signal, the frequency of taking samples, by Nyquist Sampling

Theorem, should be at least 2 times the bandwidth. If an A/D converter sampled

signals withjust 2 times the signal bandwidth (Nyquist Rate), it is named as Nyquist-

rate converters. In contrast, there are converters that operated with sampling rate in

much excess of the Nyquist rate and they are called the Oversampling Converters.

Quantization process actually adds noise to the original signal since each signal

sample is represented by a limited set of binary numbers. This quantization noise

power is known that decreases by 6dBît [41’ ppl26] assuming the noise is white and

with a uniform probability density function (pdf). This noise power also relate to the

sampling frequency, it is found that doubling of the sampling frequency decreases the

total in-band noise by 3dB, increasing resolution by only 0.5 bit [42, pp5].

In following 2.211 to 2.213 sections, some common Nyquist converter techniques will

12

be presented. The Sigma-Delta Modulation, being one member of Oversampling

techniques, will be presented in section 2.22.

2.2.2 Single, Dual-Slope AfD Converters

Single-Slope AfD Converter

The single-slope converter works with the principle of finding how long for a ramp to

equal an input signal at a comparator [43]. The situation can be depicted by the

following figure':

_ l L _ Start Conversion i ^ •

Enable V|N ^ r>ômparator ，^ •

VR ^ X ~ Latch — ^ N ^ ^ ) _ > O u t p u t

^ " # — • Ramp � � t Y n — r V|N

Generator / j\ • ^ ^ …

^ °^^ ^__^ Reset �

PCM Output cioc| j i n n n n a

•

Figure 2.1 Block Diagram of a Single-Slope A/D Converter

A conversion is started by asserting the Start Conversion Signal, the latch will be

enabled and set to high until the comparator outputs a "HIGH" resetting it. The

comparator output normally is “LOW，，until the ramp signal V^ gets just higher than

the input signal Vj^. The time interval � w i l l be counter by the Output Counter with

reference to the Clock applied. If this converter is to have N-bit resolution, it can take

‘Figure modified from [44’ pp649, Figure 8.3-6] and [45, pp625, Figure 9.54].

13

maximum 2 � T period of time for the conversion where T is the clock period. The

advantages of Single-Slope converter is its simplicity but it is subject to error in the

ramp generator and it is unipolar [44]. In [45], it is stated that it is not suitable for high

accuracy application due to the severe requirements on the stability and accuracy of

the capacitor (inside the ramp generator) and comparator required. Another

shortcoming is that it requires, in worst case, long conversion time o f 2 " r [ 4 4 ] . We can

see that this maximum conversion time rises exponentially with the number of bits N

provided by the converter. Thus, it also limits the signal bandwidth to be lower [43，

ppl03][44, pp651]. Since the conversion time 2"rshouW be at least less than 2 times

the reciprocal of the signal bandwidth BW. That is, it can be written as

2 ^ r > _ l _ (2.2.1) 2BW ^

With Tequal to lîs, Table 2.2 illustrates the relationship of N and 5评似从(Maximum

Bandwidth Supported).

N/bits BWMAx/ Hz

1 250000 2 - i25noo

2 62500

4 31250 ,

5 15625

6 7812.5

1 3906.3

8 1953.1

2 976.6

m 488.3

U 244J

Table 2.2 Maximum Signal Bandwidth {BW^^^) versus Number of bits (7V)

With Clock Period (7)equa l to lîs

From (2.2.1), we can observe that the fiW^^ decreases with the increase in number of

14

bits, the resolution, provided by the converter. Therefore, there exists trade-off

between resolution N and the maximum Signal Bandwidth allowed BWÂx provided

that the clock period T kept the same.

Dual-Slope Converter

The Dual-Slope converter elimination of the dependence of the conversion process on

the linearity and accuracy of the ramp generator. A dual-slope A/D converter integrates

the input signal V,N for a fixed period of time as determined by a clock-stepped counter

and the resulting integral is returned to zero by integrating a reference signal of

polarity opposite that of Vj^ [46]. Vj^ is therefore restricted to be positive [44, pp650].

In [44], the analysis on the converter also takes into account the threshold of the

comparator. The voltage of the integrator in the conversion process is depicted in

Figure 2.2. In phase I of conversion, where V,^ is integrated for a fixed period oftime,

the integrator voltage VjJt) given by [44].

y . . ( t ) = KV,,t + V,, { t ,<t<N^^^T) (2.2.2)

ViAt)[--N^,T=KN^^^TV,,+V,, (2.2.3)

and the time NJT taken by the opposite reference signal to discharge the integrator

voltage back to V", is given by [44].

fv ) K . = ^ r , ^ (2.2.4)

V ref

15

Vin,it) A

KN^fTV,^+V,,

4 ^ 4 >4 • ^

time NrrfT K,,,T

Figure 2.2 Integrator Voltage V,,,,(r) versus Time of a Dual-Slope Converter

From (2.2.4), it can be observed the merit of the Dual-Slope Converter of which N^, is

not a function of the threshold of the comparator, slope of integrator, or the clock rate

[44] rendering it an accurate conversion method. In [45], it is reported that the dual-

slope conversion can achieve very good accuracy without putting extreme

requirements on component stability. The capacitor inside the integrator need not to be

particularly stable and drifts or scale errors in the comparator can be cancelled out by

beginning and ending each conversion cycle at the same voltage or same slope. In

addition, it is mentioned that there is high tolerance in clock frequency stability

problem. Though it is a very accurate method of conversion, it requires relatively long

conversion time, 2(2") T, in the worse case [44].

Multiple-Slope converters, also exist which employ more than two ramp

measurements for each input signal [43]. One example is the quad-slope integration

method introduced by Analog Devices, Inc. [43].

16

2.2.3 Successive Approximation (SAR)

An 7V-bit successive approximation converter converts an analogue input in N clock

cycles [44]. Therefore, the conversion time is equal to N T, instead of 2^ T as in the

case of Single-Slope Converters. Figure 2.3^ illustrates the building blocks of a

successive approximation converter.

_ ^ ^ s ^ Comparator V,N® • + ^ ^

~ ~ ^ ^ i L ^ Digital

D/A 4 Output ^ ^ 0 ^ Clock

Converter � Control Vrei' # • �� Register

T T

i • �� Start and End of Conversion

� 十 r 1 r

^ ~ ^ V ^ ^ ‘

PCM Output

Figure 2.3 Building Blocks of a Successive Approximation Converter

The converter operates with successively testing each bit of resolution from the Most

Significant Bit (MSB) to the Least Significant Bit (LSB). The operation is illustrated

in [44]. It operates with all bits inside the register being set to "LOW" initially. Starting

with the MSB bit, each bit in turn will be set provisionally to "HIGH". This tentative

bit pattem will be converted to its analogue voltage via the D/A converter inside. If the

D/A output is lower than the input signal V,N, that bit will be set to "LOW", otherwise,

its value kept. After N such steps, the whole length of PCM output {N bits long) will

2 Figure 2.3 is modified from [44’ pp652, figure 8.3-9] with slight modification.

17

have been tested against Vj^ and this final register output will be A/D converted value

for VjN- The Successive Approximation Converters are relatively accurate and fast,

with typical conversion time ranging from l|as to 50îs, at 8 to 12 bits of resolution

[45]. As N increases, the requirement of the comparator to distinguish between almost

identical signals from the D/A converter output and V,N must increase [44].

2.2.4 Flash Converters

Flash A/D converter or Parallel PJD converter is the fastest method of A/D conversion.

For an iV-bit converter, it operates with 2"-1 comparators each fed with V,N and with a

specific fraction of the reference voltage. For an A -bit Flash Converter, the iih

comparator output value c, will be given, for instance, by

c , = logicl i f V , " > / ( # ) ( , . , [ i , 2 " _ i ] ) (2.2.5)

logicO otherwise

These comparator outputs c-s are then inputted to an encoder with produce the PCM

output. The advantage of Flash Converters is certainly their high conversion speed

(106 to 500x106 conversions per second). However, they require vast number of

comparators (2"-l) with consume chip area much.

2.2.5 Sigma-Delta Converter

The Sigma-Delta Converters is one of the examples of the oversampling converters.

They operate with sampling frequency much higher than the Nyquist rate. This is a

distinct difference from the previous converters that usually operated around the

Nyquist rate. The basic structure of Sigma-Delta Converter is given in Figure 2.4.

18

V,"(0 *-^<|y> I •丨-BitADC ~ ~ ~ ~ • 。 , ）

i L

l -Bi tDAC ^

Figure 2.4 A Basic Structure of a first-order Sigma-Delta Converter

Usually a simplified linear model [47, pp42, Fig. 10] is used for analyzing the

converter and it is shown in Figure 2.5.

ê[n]

' " [ V " < ^ ^ <^~"^"“"] X _

z—i <

Figure 2.5 Simplified Linear Model of Sigma-Delta Converter

The 1-bit ADC (Analogue-to-Digital Converter) in Figure 2.4 has been modeled as a

linear adder with the integrator output and quantization noise e[n] as inputs in Figure

2.5. The relationship among v , Jn] , vûAn] ’ and e[n] in the Z-domain is given by

youTiz)=V,,{z) + { l - z - ' ) E { z ) (2.2.6)

where V,j,{z), V ^ , ( z ) , E(z) are the Z - transform of v,^[n], v^^^[n], and e[n] respectively

19

With the assumption that e[n] is white with a uniform pdf within the interval

[ - ^ , + ^ ] a n d the theoretical Signal-to-Noise Ratio (SNR) is calculated and given

in [48] as

^ J? 1 / A \ 2

S M ? = f A � ( 3 ) ( — ) 3 w i t h h i p u t s i g n a l S = l A � - (2.2.7) 2 71 2 \ 2 y

where R = f^ / ( 2 . f ^)，f�is the bandwidth of 5 . From (2.2.7)，the SNR increase in

R\ Therefore, the SNR will be improved by approximately 9dB when the sampling

frequency is increased two times. Compared to the case of an N-Bit PCM Analogue-to

Digital Converter, the SNR will be improved by 6dB when number of bits provided is

increased by 1 • Or in other words, for a first-order Sigma-Delta converter, for every

double of the sampling frequency, there will be an effective increase of bit resolution

of 1.5 bits.

2.3 Conclusion In this chapter, the topics on telemetry and analogue-to-digital converters are reviewed.

The main theme of this research, the Wireless Electrode, is based on telemetry and the

application of Sigma-Delta Converter. The details of the Wireless Electrode will be

presented in the following chapters.

2 0

Chapter 3 Wireless Electrode

3.1 "Single Electrode” Measurement "Single Electrode", it is meant for one single electrode assembly for picking ECG

signal. It can be a concentric electrode or any other measurement that involve two

contact points on the body surface. However, they are packaged as a single entity that

looks as if a single electrode. We may refer them as a VSE (Virtual Single Electrode).

ECG telemetry with VSE is investigated and this wireless application as a whole is

called the W E (Wireless Electrode). In the following sections, the above topics will

be discussed in more detail. Section 3.2 will be on the VSE and 3.3 on the WE.

3.2 VSE (Virtual Single Electrode)

Concentric Electrode

Lu [14] reported that Fattorusso is the first one using the concentric ring electrodes for

recording bioelectric activity in 1949. It is consisted of a ring conductor and a center •

dot with the aim of studying myocardial infarcts and arrhythmias related to bundle

branch blocks. This concentric ring sensor consisting of a ring conductor and a center

dot constitutes a bipolar sensor as depicted by Figure 3.1. Such bipolar measurement

provides more localized information [14]. This configuration is also used by He [15]

and Manning [16]. Lu himself suggested the Tripolar Concentric Ring Sensor, which

consists o f two concentric rings and a dot at the center [14] as illustrated in Figure 3.2.

21

( ^ V ^ 1 V o y r = V , - V ,

Figure 3.1 A Bipolar Sensor

® :: V j J OUT = ( V o - V J - ( V ^ - V c )

= ( V o + V c ) - 2 V ^ Figure 3.2 A Tripolar Sensor

By using these concentric electrode configurations, VSE's can be constructed.

In [15] and [56], the concentric electrode is fabricated with conductive AgCl. In our

laboratory, previous researchers have made some concentric electrodes with some

kind of conductive rubber or fabric (Figure 3.3). We have tried using them to pick up

ECG signal (Figure 3.4). This excerpt of ECG signal was picked up by placing the

concentric electrode on the left lower ribs of a subject. The noise with higher

frequency is mainly 50 Hz interference. This may be attributed to the finite and

relatively big resistance of the conductive rubber of about 50 to 100 ohms. Electrode

impedance imbalance can be easily generated that enhance the 50 Hz interference. In

addition，the conductive rubber seemed to generate strong DC voltages when placed

22

with an electrolyte. We have placed two stripes of the conductive rubber over a sponge

soaked with tape water simulating the body fluid. Relatively strong DC offsets of

several tens of milli-volts to a hundred milli-volts can be measured from a digital

voltmeter. Therefore, ECG preamplifiers can be easily saturated.

‘ ^ ^ , l ^ ^ ^ f f ^ p ~ ~ S — c - e

. : J ^ ^ W _ ——

^ m = ' -_ ^ ^ ’ ? 使憑 ^ ^ ^

_ W ^ ^ ^ ^ : " " 4 i ^ i ^ i _ 1 ¾ ¾ . - . . ； . ， . : 』《 « • r : : r

Figure 3.3 Concentric Electrodes with Conductive Rubber and Conventional

Disposable Ag/AgCl Electrodes

5| 1 1 1 1 — , ,

4 -

3 -

� : | | i j j | | i ^ ^ ^ i l l ^ l P i ® ^ -3- lfl'|"'

-4 -

-5 L. 1 1 1 1 I I I � ^ ^ ^ 2 2.5 3 i^ 4 45

t i m e / s

Figure 3.4 ECG Signal Picked Up by Concentric Electrode

23

3.3 WE (Wireless Electrode) The application of radio telemetry to the VSE is called the WE (Wireless Electrode).

Some researchers have also the idea of making a wireless ECG monitoring device in a

single package [57, ppl98].

Conventional modulation like the FM and AM can be applied for transmitting the

ECG signal. As the electrode assembly should be as small as possible, small battery

cells should be used. Smaller cells often have smaller capacity. Coin-sized Lithium

cells have about a 100 mAh. Lower transmission power and efficient power

amplification will therefore be welcomed so that the device can be used for longer time.

This benefit is essentially important for long-term monitoring that frequency battery

replacement is not desirable. A prototype with FM is shown in Figure 3.5. There was a

Single-Transistor Crystal Oscillator and RF power was transmitted from the loop

antenna. It can be built even smaller if surface mount components were used. The

transmission power can be adjusted by the biasing circuit of the oscillator and the

efficiency of the antenna system.

^ ^ ^ ^ w ^ m m w i ^ ^ .

《 ^ ^ ^ ^ ^ ^ ^ 警參， 5 _ ? ^ ^ ^ ^ £ 着場 $ ^ 1 € !

^ ^ ^ ¾

__雜‘ . ^ $ f e ^ ; ^ _ ^ s 5 i l ' ^ ^ 义蒙觀 . ；

^ ^ ^ w - ： ^ ^ ^ 麵 • ^ ^ ^ i S ^ i t i f ^ ^ g a Figure 3.5 A Prototype ofWireless Electrode

2 4

The transmitted power at about 61 MHz can easily fallen down to about —100 dBm

when the current drain was about several milli-amperes. A typical transmitted power

measured by a spectrum analyzer is shown in Figure 3.6. By using a crystal oscillator,

the carrier frequency can be very stable and accurate. LC oscillator, on the contrary,

drifts greatly with the changes in the external environment such as placing the

oscillator to the vicinity of the human torso.

H ^ ^ H H ^HBHHHBHI^^^^^^BHH'liiQ^H ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^H iî-^ÎBI|^^^^J

^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ m i m g j ^ ^ ^ K ^ ^ ^ ^ B ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ B ^ ^ ^ ^ WP j M^ ^ ' ^ ^ ^ H • ^ ^ ^ J ^ ^ ^ ^ ^ J j j ^ ^ ^ H H H ^ ^ ^ H I H j | P ? ^

WESmB^Smm ^^^^^^^J jj ^ jj | ^^^^ j ^^^J^^^^^^^J^J^^ jj ^^^^^^^^^^^^^^^^^ iy PI| ft ^^^^^^^^1 ^ ^ H P S S S 9 | i n p Q H | E X E ! ! S l ^ p r o S i 9 | E f i l i i f l ^ ^ H

^ ^ ^ ^ ^ ^ ^ ^ ^ ^ g g ^ M

H t f i H

Figure 3.6 Transmitted Power Spectrum by the Prototype

However, the frequency deviation allowed by a crystal oscillator is quite limited.

Multiplier stages may be used for both increasing the carrier frequency and the

frequency deviation.

With this prototype, analogue FM was used. As discussed in previous chapters, digital

transmission of signal is more favorable nowadays. Another prototype with digital

transmission was applied. Digitization is performed by a first-order Sigma-Delta

25

converter that will be further discussed in the next chapter. It is operated with 土 3V

draining less than 10mA from supplies. The maximum transmission power was

measured to be about -60dBm that is safe with operation with other medical

equipment and also to the human body. Two photographs of the received signal of this

prototype with concentric electrode are shown in Figure 3.7. The two signals were

picked up at slightly different location on a subject mid-ribs. Ectopics can be noted in

both excerpts of signal.

| « v l-.-...-�…―.，謹：卿 ” » - 1 - 丁 . .， .一 1 — . . . . 「」释 , _ AC PMi I , I —V n*!t' i :

r I J i i 卜.一,一«,州、‘1_；、..‘,、伙‘,.,.糊‘.、《...，…力、杯,...,.....v..,k-..' ~«'~— •«>"•’ >"' "4-'" -~-…—…，«• •_—__ i~-~-.^. ^- . .. ^ —.十一一-一 —.—~>~— • .1 ._._•- • ,ii , 4 ‘ j + * 1

’，丨 4» .. � I

T + i

• .-....w .. .. .,.|v .. -.WNV*VV l.WV.« s-%-VV M,.Vi»».WVV , w , -v«wv ••«" —•••",•••"•~".T .... «..^...*...~ ••• "V,..V^ t" - ‘•••••—~—一一一...》—一_».~». , I- --• - -~»~~"~_"|«_-...„»,.,,„,_“,.,„ _ ...

I j

，.*—叶.和.„_«_.产„,.,麥._’卜《«卜^ (,,+^4^ .,.^. ‘‘々 +,+ -.令+..+-*«|»和寸,卜个,产"-f - ^.^..¢^-,.4,4..^. 4. ,.^,..^ * . 4 - ( ^ , ^ t^î' •-* ^-^- .^..^-^.^.. -t-».-irr^|,_.J |,卜者)•,

•产 _一终 ,。一』 ^ * 1 ^ _ » 1 1 1 1 ^ ,产 1 j | ^ B i p ^ S f ^ l | j i i l [ S i M i _ _ i i ! ! ! p J j

..—.———「卜 "「+."•...〜‘十--..._.-一+---4-"**• """**““ ~*"- <~*"‘.力•》.. -••• S«.-W»V. ^ ^ ^ ^ " ^ “ •—*-•—、•样-* ~~称一~^一 '~一~*~.~* .-"' •'..."**一 _.l._. *一 "*""^"*^'^ _ — ‘™ I •“ -^-"-"'"".讲- WVA<Sh -W W. ..... I . . _______ r ^ I

,. I

Figure 3.7 ECG Signals Picked Up from a Wireless Electrode Prototype with

Sigma-Delta Converter and Concentric Electrode

From the received signals shown above, we can see clear signals obtained. Since the.

main purpose obtaining the signals is for monitoring purpose, we only concerned with

whether signals are existed or not rather than the waveforms of the signals. Important

parameters like the heart rate, heart rate variability can be obtained. From these

parameters, the patient heart conditions can be detected, whether the heart is beating

too fast, or too low, or beating in an uncontrolled manner. For instance, from the above

received signals, we can notice that the subject has occasional ectopic and by counting

the frequency of the occurrence, the subject heart condition can be evaluated and

monitored. Another sets of experiment were done where the original signal and

26

reconstructed signal were shown together. The original signal was the input signal to

the Sigma-Delta Converter. Two reconstructed signals were presented. One of them

was the reconstructed signal without going through the wireless link; the other one was

the one with the wireless link. Ln Figure 3.8, the original signals were obtained from a

signal generator with Sinewave output. The signal frequencies were 17，6，2, and 0.14

Hz as shown in Figure 3.8a，3.8b，3.8c and 3.8d respectively. The uppermost traces,

labeled p, were the reconstructed signals without wireless link. The middle were the

original signals, labeled a. The lowest ones were the reconstructed signals with the

wireless link, labeled y. r , i . V i i i J . . . _ . . , , , . . . , . , I . : . - > . . ... 10 .. ： ‘ • i -L：!. .lHlZ'i ^ / r t J L 6 ^ , . , . . _ . , . . . . , , 5 L l f l - . : : F P - l : : r j i : ; i ^ : : -：； ,

'•• h^ "iv i;Hj:!|" .:H,:;HÎ,|/: I . J||.— /.:I f^p!”0"iv r.Hv=!iV • i.Ho = p;oV r riiâ-^/'； […hll:! iV.; r[ilO [I!； r.li. ： i ： Oi;JtlJJ ;i:: f.” [II； ^lij . I

6 * C " • • •-- - • . — . . . j. M.^^yi *Mjy. —_ . • ,- — .— . . — . • - i^Vj^4h - . .~j • •——*•— , y "^^^^^»- -~~ • " " — " " " 一 . _ ••-_ _• —-—— — — ! • • - . .-_y. — _ _ ^ _ _ _ _ _ _ _ _ _ _ i

^ ！爪 t / 1 、！八 , T j f ； "Y.../ — V"-""[/.. \ "i/ - . .. 1 ^bptTK7^^|7^;^"^p^ 一—t— j~ \ — > _X y^— ‘ — — f — : ‘

^ s j ^ \ ^ ^ v f I j i r r i • ^%^^^^^^^^^^^m^: 0 ® ® f £ f ^ f ^ f f f f ^ :

！ .! r I ！！ j

I ) i t I .一直—j_——I ！ _ J_！ U.：!；：-! q Itl-$EP-1MM'1 lS::':p 3辦‘过._, 1^ __Lfljliflz^-i 'iQ ,1 :::i). Wl;5KVl:_nr7f | r—_-pT[_Wr�—~T^T^l3Wrffn pT^0inv^nTv=iiv CW !0Imy1 T | 5 te/d

00 p;uij Ai： PMlj DC Fil.llj 00 PHI:I A,.. Ki.i) [n； Fj!lfl — - ' -L . — > 一 . 一 ^ — 一 , _ - . _ _ > > f - _ - • I _ — _ ^ . _ , 一 •_•’,_• • „ . I » - . — - ' • 一 . . - 一， . _ 一 . . . ^ ― — f I — • » — - " • • 一 •• •丨,• I — ~ - I 丁一

‘ ：： I

• T t v ^ ^ T ^ ^ i ^ 7 t 5 ^ " t e i ^ ^ . - — — p - ‘ T .•

1 ¾ ¾ ¾ ^ ^ ! ^ ¾ mm^mm^m ‘ j W � A 7 ^ b A / V W t " " ^ x � - ^ � � � ^ “ = ^ ^ . :

* * ~ " — . • • — — 一 _ . • _ •— I I — • 一一 . - - . - _ - . , — . • I ~ " ^ ~ ~ ~ ~ ~ . • , - — * - - ^ •••»- - - • — I• - . — • . I I - — — — • 一 — 一 ~ " — ‘ ' ~ " ~ " ~ " ~ ~

rcO (fi� I J k- • J- L_ • I I [ I , •• L.»»__.—.—L—— ,.l ^ ^ %>>^J 丄一-___1 I - ^ *• - —_-~~J———i ‘ _.~—- *•—

i Figure 3.8 Reconstructed and Original Signals for the Sigma-Delta Converter with

Sine Liput (a-d)

27

For Figure 3.9，the input signal was from an Arbitrary Waveform Generator

{YOKOGAWA AG 1200). It was an ECG waveform and used as a test for the system.

Like Figure 3.8, the uppermost one was the reconstructed signal without wireless link

(P); middle the original (a); lowest one the reconstructed signal with wireless link (y).

“ 込监二] 'i __m:::FF.-!,i'V:i '7 ^ 带[^丽—.'.冗 ;nn7^w]^Ti"" ， � q n :“丨：/ ,i

[11：： Pirl!j AC -'|fW [11. Ftl llj ! I ！ 1 I I J

• »••»-~- III • 1 •• I I~~一一 . . _ — II • _ — ~ - - - ^ ~ I • - • “ • 一一 j

^ _M JL I ll ! L •—• • fc -• —».一-.感— ~ •. •-»»• —»• I I— —ju«.. •-- - • • t~-—+- — • j I

i i | ^ 4 ^ t L | 4 — l j u k i

i ^ ^ H ^ ^ ' 丨 i J 0 1 j B ^ 1 1 jl l j j I I

yv=:M^^=;=;;;i:: =V )vi; 4c Ai<^<>>>^<^<J * A;;;; JwN<* V 不 I

j i 1 1 ！

Figure 3.9 Reconstructed (traces P and y) and Original Signals (trace a ) for the

Sigma-Delta Converter with Generated ECG Input

Lastly, the signal picked up from a real subject with a single electrode assembly is •

presented in Figure 3.10a and 3.10b. For 3.10a，The single electrode assembly was

built with one of the concentric electrode shown in Figure 3.3. It was composed of two

separate conductors half-rings. For 3.10b，another single electrode assembly with two

full rings was used. From these figures, it can be observed that the reconstructed

signals resemble the original one.

28

[ ^ ^ - | i V 7 H 2 M p ~ — m p h w V Vll/d' p T ^ m — T T h W — T H v Z ^ ™ ~ ~ U f ^ M | i ^ rt(J Ptli j | f H . P _ DC R:nfl AO PtlO AC P,Mfl AC ‘ 丨 l < ^ ^ H ^ / ^ i \

Reconstructed without Wireless Link : 1 j Reconstructed without Wireless Link

i ^ p a i ^ ^ ^ p ^ j ^ y _ _ _ f � — . 丄 ^ ^ ^ ^ ^ ^ ^ ^ ^ 働 — / 變 / ^ ^ ； ^ 』 ^ ^ 一舊 . ‘丄

^ : | _ _ [ I _ 1 T _ _ a Original Signal

fS^Wjiiiil^Si^p_f|lii . i p f _ _ f t i p i p j j i i | _ | ！ I \ ~ ^ ~ r~ Reconstructed with Wireless Link I I I

Reconstructed with Wireless Link i _ [ J; ^ i 1-——i ：

y^^^^^[^^ I � { i w . v , v p v " ^ . v v ^、〜——,一—六4、^^^^二幅 a

于 - - , % ^ 1 _ 7 _ 碑 _ ^ ^ | ^ ^ ^ ^ 一 I ~ ~ p 1 ^--~L.—丄—」： Lfft)J—~； L _ i 一 — : _ ( _ & _ y _ — 丄 — _ L ‘

With Two Half Rings Transducer With Two Full Rings Transducer

Figure 3.10Reconstructed and Original Signals for the Sigma-Delta Converter

with Real Subject's ECG Input (with Two Half Rings Transducer (a); with Two Full

Rings Transducer ( b ) ) .

3.4 Discussion

The motivation ofWireless Electrode (WE) with a "Single Electrode" is reducing the

complexity of setup in ECG monitoring. Easy and prompt monitoring capability can

be provided. More local information can be provided by the concentric electrode

assembly [14] and radio telemetry renders patients with ambulatory freedom. With

Sigma-Delta Converter, which has simple analogue circuit complexity, digital sigmiI

transmission can be achieved. More widespread application of the WE can be

anticipated due to its simplicity and robustness.

2 9

Chapter 4 Sigma-Delta Converter

for ECG signals

4.1 Motivations Among various Analogue-to-Digital conversion methods, Sigma-Delta converter is

superior in terms of circuit simplicity and component tolerance in expense of higher

sampling frequency and more complex DSP (Digital Signal Processing). Sigma-Delta

converter is consisted of very simple analogue circuitry. An analogue low-pass filter

can recover the analogue signal. Complex DSP unit is applied for better performance.

The DSP unit is usually fabricated with the simple analogue circuitry, which increase

the circuit complexity. It would be beneficial to have simple circuitry in patient-wom

transmitter. In this research, we would like to investigate the separation of the DSP unit

and the analogue part of the first-order Sigma-Delta converter (Figure 2.4). The

simple analogue part is included in the patient-worn transmitter while a monitoring

computer provides the DSP function. Noise will be induced in the wireless link and

errors in the bit pattem are anticipated. AWGN (Additive White Gaussian Noise) is

used for simulating the situation and simulations are performed.

This following text will be divided into two main sections. In section 4.2, baseband

application of the Sigma-Delta converter will be presented. Simulated data will be

presented with comparison to ideal N-bit converters. Experimental data for a self-

made Sigma-Delta converter will also be included. In section 4.3’ wireless application

will be discussed. Similar to section 4.2, simulation and experimental results will be

presented.

30

4.2 Baseband Application

4.2.1 Simulation Results

4.2.1.1 Simulation Results with a 17Hz Sinewave

A zero mean, pure sine tone of 17Hz of about 48000 points is used for simulation. It is

chosen to be 17Hz since most of the spectral energy of a QRS complex of an ECG

signal is located around 17Hz [53]. The simulation programme for Sigma-Delta

converter is modified from [52]. The sampling frequencies for the converter is chosen

to be 8000, 4000’ 2000, 1000, 500 and 250, assuming that the maximum frequency of

interest is 100 Hz. The input signal of different sampling period is obtained by

decimation. Different amplitudes of the sine tone are tried also. They are 0.1, 0.5’ 0.8

and 1. Two figures of merit are used for evaluation, namely the MSE(Mean Square

Error) and the SNR(Signal-to-Noise Ratio). They are defined as:

M Y,{x[n]-x[n]f

MSE = ^ (4.2.1) M

where x[n], x[n] are the original and recovered signals respectively.

V 2

SNR = ^^)- (4.2.2) MSE

where A is the amplitude of input signal.

And the same signal is applied to two ideal N-bit ADC (Analogue-to-Digital

Converters) for simulation. They are of seven and eight bits resolution respectively.

Same figures of merit are applied to them as well.

For obtaining x[n] in the case of Sigma-Delta converter, a fifth-order digital

31

Butterworth low-pass filter is used. The binary bit pattem is applied as input to the

filter (Figure 4.1) and the output will be x[n]. However, as there is delay (Figure 4.2)

between the original and recovered signals. Certain amount of shifts is incorporated

for compensation.

八 ~ 1 Sigma-Delta Fifth-Order Butterworth X [ / 1 ]

\l 一 ^ —

V Converter Low-Pass Filter X[n] j^MM—iiJ M i — . — W M

Figure 4.1 Simulation Block Diagram for Sigma-Delta Converter

T ime Delay in S igma Del ta Conver te r

1.5 I I I I i I I Original Sigma De ta

1 [h. fi\ rk i \\ . / \ -

V y -1.5i 1 1 1 1 1 1

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 t ime/s

Figure 4.2 Delay in Sigma Delta Converter Recovered Signal

The results are given in Figure 4.3 to 4.8. Figures 4.3 to 4.5 are on the MSE (Mean

Square Error) measure for Sigma-Delta Converter, 7 and 8-Bit Converters. Figures 4.6

3 2

to 4.8 are on the SNR (Signal-to-Noise Ratio). Subscripts in legends (proceeds with

‘」）denotes the amplitudes of the sine tone. For instance, "SD_0.1" stands for the case

of Sigma-Delta Converter with input sine tone of amplitude 0.1.

MSE versus Sampling Frequencies for Sigma-Delta Converter

0

^.. \ 、 -10 - Xw ^ •

^ V N < ^ ^ \

" • ^ ^ 、 - 、

< X ^ � > K . -^SD_0.1 CO O s ^ ��

：5 s i ^ ^ � — — S D _ 0 . 5 S ' ^ ‘ v ^ �� . 4 - SD 0.8

‘ X ^ � � t ^ 务 \ 〜 . ^ 说 ‘ 丨

100 1000 10000

Sampling Frequency M i

Figure 4.3 MSE for the First-Order Sigma-Delta Converter

(SD stands for Sigma-Delta)

33

MSE versus Sampling Frequencies for 7,8 Bit Converter

- 4 5 「

-46 - ^ ^ °

、、. -47 - X - - - - - •* : 2 ；；：：：： - 5 ：；：；：：；炎：：::：:：2 : ：：：：：： ^

X - - - - - - - * “ “系• * * ^ -48 - _<X-8-Bit_0,r'

- ^ - 7-Bit_0.1 -49 - -X-8-Bit_0.5

m - *- 7-Bit_0.5 i -50 - ~l--8-Bit_0.8 S

-斗-7-Bit_0.8 -51 . -o-8Bit_l I

®~- ~~ � - 0 - 7-Bit_l I . . ~~ ~©~ «>__. =——I

-52 - ~- ~- ~e _©_ »— •€) „ X ^ ^ z = = 二 # = = = = :fc = 二 = g -53 - > 一一 ss=容 •* 茶 ^

^ - ^ ^ -54 -

-55 ‘ ‘ 100 1000 10000

Sampling Frequency /Hz

Figure 4.4 MSE for 7，8-Bit Ideal Converter

34

MSE versus Sampling Frequencies

0 �

2。 ^ ^ " ^ ^ > ^ —

X ^ ^ ^ ^ V s ^ —A—8-Bi t

I 30 ^ ^ N? "" • .- 7 Bit

‘ ， H h : : : , : : : : : : : : : : : : : : ^ ^ ^

-50 N ^

jpz 22 S9 _ (SSF> = 漏誦 n(p : : : "4— ; : : ""4" : “ : nl - 6 0 ‘ ‘

100 1000 10000 Sampling Frequency /Hz

Figure 4.5 MSE versus Sampling Frequency for First-Order Sigma-Delta Converter

and 7,8-Bit Converters

3 5

SNR versus Saampling Frequency for Sigma-Delta Converter

50

40 -^. " " t

" ' • — - - •

见，• . : ^ ^ z

";< rz Z „ 2 0 . • ) • y 一 _

：5 ^ 广 X —-SD_0.5 z ^ ‘ ^ y ^ .4-SD_0.8 ^ 10 - j^*y ^ —-sD_i

, : y ^ 一。〔； > ^

'。 z - 2 0 ‘ ‘


Figure 4.6 SNR versus Sampling Frequency for First-Order Sigma-Delta Converter

36

SNR versus Sampling Frequency for 7，8-Bit Converters

6 0 「 .

1

-X-8-B i i J ) . l

50 - 八一一一 fs 一一一 n 一一一 _r> — 一 — " K s = r l ^ ZZ 二 ZZ ^ ^ _ 一一 j ^ 一一一 t ^ h

-权-7-Bil 0.1

r^T,7rT^7:7:Trtl7:7:7fl^7:7:T:t^r:7:Tr| — 40 - -X-8-Bii_().5

龙 * * « - - - - - - • * * _

- * • 7-Bil_().5 CQ 5 ^ 30 - X H H H »< K

S »>»_h-8-Bit_().8

M ^ - ^ -H ^ ^ • + • 7-Bil_0.8|

20 -

~~0-8-Bil_l

10 - - « - 7-Bil_l

0 ‘ ‘


Figure 4.7 SNR versus Sampling Frequency for 7, 8-Bit Converters

*

37

SNR versus Sampling Frequency for SD and 7,8-Bit Converters

60

50 . ^ ^ , ^ 一 «Mi • A •«• 一 - ^ __i__ijfc"" * ‘ *"^ 一 A 一一一 j|i 一一 ~ - ~~A mT^ *"**"" “ “ ^ ^ _""H…”•——> »MM mmm i_i"i J I MM* »«Mi mmmm. "•••_J|iiiw f«M «M» wmmm 丨“" i ^ i _ . •>«» » » >M> m m �

_•"•: : : 二 = z :身:“:•---"‘省‘;“二 •:--‘身‘、-“::--1 省 : : :":“:.身 40 j

^ ^ ^ ^ ^ Q • JWMT m>mm mm mmi m_i_�"__• mmm mtmm mmm, i.mi•imi ~~~« «M>» »•••«• j-^^ mmmm m,^^^t^^^^SZm mmmm mmmm J ^

卜 . … … - : ^ ； ^ ^ ^ —

10 ^ , . . ' ' ^ ] X ' ' ^ ^ y " ^ " A - 8 - B i l

0 ^ ^ ^ ^ ^ " " " ^ ^ ^ B

- 1 0 � ^ > ^

- 2 0 — —

100 1 _ 10000 Sampling Frequency /Hz

Figure 4.8 SNR versus Sampling Frequency for First-Order Sigma-Delta and

7，8-Bit Converters

Discussion

From the above figures, the MSE's for seven and eight-bit converters are nearly

constant with changes in sampling frequencies and input amplitudes and are around

-46 and - 5 3 dB. These are close to the theoretical quantization noise power, of ^

where A is the quantization interval. The above information can be mathematically

presented as:

X = Xq + q^ (4.2.3)

4 / ) = 兽（4.2.4)

PS A = 7 (4.2.5)

where ;t is sample amplitudes of the original signal, x^ is the quantized value ofjc with

38

q[ being the quantization error or quantization noise; FS is the Full Scale level allowed

for conversion and B is the number of bits provided by the converter. The SNR's

change with the input amplitudes as by definition (4.2.2). It is the ratio of average

signal power to the MSE. As MSE's are constant with the sampling frequencies and

input amplitudes, the SNR's are linear with the input signal power as shown in Figure

4.7.

In our case, the above parameters can be shown in Table 4.1.

Parameter Name Numerical Values

FS 2

B 7 and 8

A 2 , 2 and

128 256

£ ( ^ / ) -46.9154 and -52.9360 dB

Table 4.1 Parameters for Simulations

For the case of the first-order Sigma-Delta converter, the MSE's and SNR's vary with

both sampling frequencies and input amplitudes. From Figure 4.5, the performance of

the first-order Sigma-Delta converter can be comparable to the seven or eight bit

converters in terms of MSE with sampling frequency of 8000 Hz. The performance is

improved by about 5 to 12 dB with sampling frequency doubled from the simulation

results. Theoretically, the MSE of the first-order Sigma-Delta converter decreases by

about 9 dB with sampling frequency rises two times.

39

4.2.1.2 Simulation Results with Real ECG signals

For Normal ECG Lead II Signals

A sampled ECG (with 8000 Samples per second) signal from a subject was used as the

input signal. The ECG signal was first low-pass filtered by a second-order digital

Butterworth filter with 100Hz cut-off frequency. Since that excerpt of ECG signal

contained only one beat, we compiled eight copies of the filtered excerpt into one as

the final input signal to the simulation program.

The Original Signal for Simulat ion The Power Spectrum Densi ty of Original Signal , . . 120, ‘ •

1 100i

o> <i> 80 "E 0.5 E E c 60 TO CT

E oj lA LA LA A tA ^ ° 20

_ Q 5 , , , Q i , l U j . . I 1 1

_ ‘ 0 1 2 3 4 0 20 40 60 80 100 t ime /sec f requency/ Hz

The Reconstructed Signal The Power Spect rum Densi ty of Reconstructed Signal . . . 80^ ‘ ‘ ‘

0.8 60

_| 0.6 ,

•E 0.4 • -E 40 ^ I ^ E 0 .2 . £

ou W WjiWW \W 11 u, -0.2 '~" — 0___l ' | . ,——‘ ‘ •

0 1 2 3 4 0 20 40 60 80. 100 t ime/ sec f requency/ Hz

Figure 4.9 The Simulation Results

For convenience, only five out of eight beats of the input signal are shown in Figure

4.9，while the power spectrum density is that of all eight beats. From these results, we

can see that the reconstructed signal shown at the lower left comer resembles the

original signal. This reconstructed signal is obtained by passing the digital output

signal through a digital Butterworth low pass filter.

4 0

ECG Signal with a single PVC (Premature Ventricular Contraction)

The original signal for simulation is shown as Figure 4.10.

1 1 ： 1 1 1 i

j 0.5 - I I -

•S 5 i

I i

。 4 U A / | w J ^ _ i M A w y k \

1

-0.5 V

I I I 1

0 1 2 3 4 5 6 time Is

Figure 4.100riginal Signal with a Single PVC (at time « 2s)

From simulation, the MSE (Mean Square Error) for Sigma Delta converter and a 7，

8-bit ADC is obtained as shown in Table 4.2 and Figure 4.11.

4 1

Sampling MSE (Sigma-Delta) MSE (7-Bit ADC) MSE (8-Bit ADC)

FrequencyfsAIz /dB /dB /dB e ^ ^ ^ ^ ^ ^ ^ B B n n ^ ^ ^ ^ ^ ^ B B S S s ^ ^ ^ ^ B ^ ^ ^ = B s ^ ^ ^ ^ ^ ^ = a ^ ^ ^ ^ ^ ^ » 5 = s o s a « ^ ^ = ^ ^ ^ ^ ^ ^ ^ B S s ^ ^ ^ ^ ^ ^ B s s s a a ^ ^ ^ ^ ^ ^ ^ ^ * ^ ^ ^ ^ ^ ™ ^ ^ ^ ^ ® ® ^ ^ ^ ^ ^ ^ ™ ® ^ ^ ^ ^ ^ ^ ^ ™

8000 -43.2134 -46.8442 -52.9634

4000 -39.4112 -46.8354 -52.9809

2000 -30.8914 -46.8377 -52.9809

1000 -20.4959 -46.8396 -52.9948

500 -15.154 -46.7763 -52.9962

250 -7.653 -46.899 -53.0872 L

Table 4.2 MSE for Sigma Delta and 7, 8-Bit Converters

M SE ( E C G S i g n a l w i i h a S i n g l e P V C )

’ \ ^

\^^^^^^^^ I ~ • ~ S i g m a - D c l i a

^ " " ^ " - ^ - - . ^ ^ ^ - , - 7 _ B “ . . 2 � ^ \ " ^

i .30 ^ ^ C/3 ^V^

^ , ^ ^ ^ m • 魯 • • •

-5 0 -• - - A • A • •

1 0 0 丨000 l(_0 S a m p l i n g F r c q u c n c y / H z

Figure 4.11MSE for Sigma Delta and N-Bit Converters

ECG Signal with Frequent PVC's

Another excerpt of ECG signal is from the Samples ofPhysiologic Signal Databases,

MIT-BIH Database. It is from the data file “ x_119.dat ’’. This record contains many

PVC (Premature Ventricular Contraction) beats [55]. The data file is generated by 4 2

digitizing analogue signal with sampling frequency of 360 Hz. 4096 points are

extracted from the data file and different sampling frequency simulation is performed

by linear interpolation on the original 4096 data points. The original signal is shown in

Figure 4.12

0.75| 1 1 1 1 n—

0.7 - -

0.65 - -I

0 . 6 - -

0.55 - -o> ^

I 0.5 - j I -

' : 帅 ^ | | 1 丨属洲：

1 \ \ | 0.35 - \ -

0.3- ^ I -

0.25 1 ‘ ‘ ‘ ‘ ‘ 0 2 4 6 8 10 12

time/s

Figure 4.12ECG Signal from record “ x_119.dat “ (MIT-BIH Samples Database)

Table 4.3 and give the MSE for the first-order Sigma-Delta converter and 7,8-Bit

ADC.

4 3

Sampling MSE (Sigma- MSE (7-Bit ADC) MSE (8-Bit ADC)

Frequencyfs/Hz Delta) /dB /dB /dB

11520 -52.6059 -47.2013 -52.8395

5760 -48.4669 -47.2009 -52.8396

2880 -40.3138 -47.2004 -52.8392

1440 -32.0938 -47.1991 -52.846

720 -19.1344 -47.2018 -52.8556

360 -6.732 -47.216 -52.8639

Table 4.3 MSE for Sigma Delta and 7, 8-Bit Converters with “ x_119.dat，，

MSE (ECG Signal w i ih Frcqucnt PVC)

0

A

- 1 0 ： .....

- 2 0 丨 ’、

•

DQ I \ 、 ~ • ~ S i g m a - D c l l a W -30 - « - 7 - B i t S \ * X-Bi l

-40 i .X

« . • ~ ^ • -50 ‘ ' • •• . ,_

1 • 1 *‘ • * •*

- 6 0 丨 —

100 1000 10000 100000 , Sampling Frcqucncy /Hz

Figure 4.13MSE for Sigma and 7,8-Bit Converters with “ x_119.dat ”

ECG Signal with Other Ventricular Arrhythmia

Lastly, a data file “x_418.dat”，also from MIT-BIH sample database, is used for

simulation. This is the ''Malignant Ventricular Arrhythmia Database” that contains

ventricular arrhythmia signals. Similar to “x_119.dat，’, 4096 points are extracted for 4 4

simulation. The sampling rate for this data file is 250 Hz, slightly lower than that in

"x_119.dat". The original signal is shown in , the simulation results tabulated in Table

4.4 and graphically shown in Figure 4.15.

0.6 1 1 1 1 1 1 1 r—

� 4 - I I 1 -1 J

。 . “ p i | i I ' i , i , I s r . 0 - i i i i u r i i i U t I ^ L S 丨 1 - - * I i m - J l | | 丨 l|i

I I M 1 i| I 答丨 I I - o s ' 1 1 1 1 1 1 1 L _

0 2 4 6 8 10 12 14 16 time/s

Figure 4.14ECG Signal from record “ x_418.dat “ (MIT-BIH Samples Database)

4 5

Sampling MSE (Sigma- MSE (7-Bit ADC) MSE (8-Bit ADC)

Frequency fs /Hz Delta) /dB /dB /dB

8000 -49.1172 -46.8814 -52.994

4000 -40.7259 -46.8813 -52.9969

2000 -32.1542 -46.8837 -52.9977

1000 -23.4893 -46.8935 -53.0268

500 -14.4829 -46.8811 -52.9954

250 -4.0333 -46.8285 -52.9887

Table 4.4 MSE for Sigma Delta and 7, 8-Bit Converters with “ x_418.dat ”

4 6

MSE ffCG Signal wiOi Ohcr Vcnuicular Arrh>ihnia)

60

-10 ^ v

^ \ « \ ^ ~ •~S igHB-Dc lU i U -30 • ^ V - « - 7-Hl

s ^N>^ |--A--s-ai

•40 • N ^

. X • • • • • •

-60 1 '-100 1000 1 _

Sampling Frcqucncy /Hz

Figure 4.15MSE for Sigma and 7,8-Bit Converters with “ x_418.dat ”

Discussion

In this subsection, different types of real ECG signals were applied to simulation.

Normal and abnormal ECG signals were under study. In the first simulation, normal

ECG signal was applied. The MSE for 7,8-Bit ADC is kept to be constant with respect

to different sampling frequency) and they are close to the theoretical values. MSE for

first-order Sigma-Delta converter decrease with the increase in sampling frequency.

With sampling frequency of 8000Hz, it performs similar to an eight-bit resolution

ADC for an input sine tone of amplitude 0.1.

47

4.2.2 Experimental Results

4.2.2.1 Experiment with Pure Sine Tones

Experimental Setup

A self-built first-order Sigma-Delta converter is used in this experiment. The

components for this converter are mainly quad opamp (LM324), D Flip-Flop

(CD4013). A relaxation oscillator with NAND gates (CD4011) generates the sampling

clock.

Three pure sine tones in-turn are applied to the first-order Sigma-Delta converter.

They are of about 0.2，2 and 20Hz respectively. The converter is run at about 200 Hz

sampling frequency. Input signal and the binary bit stream are digitized by WINDAQ

and data is further analyzed by MATLAB. The sampling rate of WINDAQ is 2500Hz

for each channel and data consumes about 360 kilobytes disk space each. From the

whole excerpt, 131072 (2'^) points are extracted and processed to obtain the Power

Spectral Density (PSD) with a Hanning Window (131072 points).

Results

The PSD's are shown in Figure 4.16 to Figure 4.20. ,

48

Power Spectral Density with f » 0,2 Hz 0 I I ‘ 11 1—1~1 1—I~I~~I ‘ ‘ ’ ‘ I 1—I~I~‘ I ‘ I ‘ I 1—I~~1

八 Original I \ Converter Output|

:4。/il

l:j Y,jftpj|p|p|| -160' ‘ ‘ ‘ ‘

10"' 10° 10' 10' 10' frequency /Hz

Figure 4.16 PSD of Input Signal and Binary Output with f « 0.2 Hz

Power Spectral Density with f » 2 Hz ~~I ~"r~r"r"] i i i i ~ i ~i ~r~ri i ‘ “ " ~~‘ ~~‘ ‘ ‘ | ‘ ‘ "> ‘ ~ ‘ ~‘ ‘ ‘ I

0 - Original -Converter Output

- 2 0 - -

i - 4 � _

I - I : � i i i i i ^ i y A f

!:5Miil__IIIQII - 1 6 o l ‘ ‘ ‘ ‘ I ‘ ‘~~‘~~‘ ‘ ‘ I I I 1 1~I 1 • ~ ‘

1 0 � 10' 10^ io3 frequency/Hz

Figure 4.17PSD of Input Signal and Binary Output with f « 2 Hz

49

Power Spectral Density with f » 2 Hz n 1 1 1 1 1 1 1 ‘ 1 ‘

0 _ Original _ Converter Output

I -50- 1 -

i- wy4ii ‘

-150 - -10� 10'

frequency /Hz

Figure 4.18 Zoomed PSD of Input Signal and Binary Output with f « 2 Hz

Power Spectral Density with f « 20 Hz 0 | . .

Original j -...--•- ConverterOutpu1j

- 2 0 -

-40 - -

i , i . |丨• i I i ^ ^ , I

iiiiSH_ ':'"P(lii

！ 1 - 1 6 o L ‘ — — ‘ 3 ‘ — — ‘ 3 10 10 10 frequency /Hz Figure 4.19PSD of Input Signal and Binary Output with f « 20 Hz

5 0

Power Spectral Density with f ^ 20 Hz 0 | 丨

〜 ——Original j I ——Converter Output

- 2 0 - -

i�

!\

I -4�- I ii -

b'.J^ l W _ % i Y . \、”\ ,。 I -8o(; \ / \ r \ / \ n A j r ^ f V \ A K 4 M v ^ , . M r \ f � -

Mp 1_pff|^t^ -100- ！ ‘ ^ ij y -

5(' i

- 1 2 o ' 20 frequency/Hz 22

Figure 4.20Zoomed PSD of Input Signal and Binary Output with f « 20 Hz

Discussion

In Figure 4.16 to Figure 4.20’ the solid line is for input signal spectrum and the clashed

line is for the binary output. It is observed that input signal is corrupted with noise as

there are distinct peaks in it spectrum which is not expected. The Binary output PSD

contains the Input Signal Spectrum at lower frequencies. We can see that the high

frequency noise of the binary output starts to rise at about 10 Hz. Therefore, for the

sine tone of about 20 Hz, this tone is already in the midst of rising noise in the PSD of

the binary output. Nevertheless, the in-band noise floor is always about -80dB for the

3 cases. The tone spectrum is shown to be about 4 0 d B lower than that of input.

Therefore, giving about 40dB higher than the noise floor. Therefore, signals can be

faithfully recovered from the binary output by means of a low-pass filter that can be

implemented in either analogue or digital form.

51

4.2.2.2 Experiment with Generated ECG Lead II Signal

Experimental Setup

In this part, the experiment was done earlier and another circuit was implemented.

The converter is consisted of a quad operational amplifier LF444 and a CMOS D

flip-flop (4013). The signal was attenuated to about 40mV peak-to-peak to be input to

the circuit. The digital output was fed into a first-order low-pass filter to obtain the

reconstructed signal. In order to allow for further analysis on various signals, we used

the WINDAQ computer acquisition tools to record signals from the circuit to computer

disk. The data obtained were then being imported to MATLAB for further analysis. The

whole setup can be illustrated in Figure 4.21.

ECG Signal 1’0’U’0 一 Sigma-DeltaConverter ^ LPF Reconstructed

^ ^ S 3 ^ WINDAQ ^ MATLAB

Figure 4.21 A Block Diagram for the Experimental Setup

Results

The experimental results obtained are shown in Figure 4.22.

52

The Original Signal 11 I 1"一 1 I 1 1 1

1 丨 I

善�.5_ 丨丨 _ I I A 11 A 1 /••• I 0 - w V j / V . ^ ^ ^ > ^ . V ^ J ^ � ^ ~ ^ A ^ K ’ ^ � E -0 5' ‘ ‘ 1 ‘ 1 1 1

0 0.5 1 1.5 2 2.5 3 3.5 4 time/ sec

The Reconstructed Signal 1 1 1 1 1 1 1

？ 0.6 - ！ -g i! 豈 0.4 I

| o . 2 - I / \ , A i f \ _ 1 0 ^<>-AwJVi i ^ V ^ ^ WsJu Aj 'y^ VÂÂ^VVJ .\— *-''*-AwA/w

_o 2 ‘ 1 1 1 1 1 1 • 0 0.5 1 1.5 2 2.5 3 3.5 4

time/ sec

Figure 4.22 The Original and Reconstructed Signals

We can see from Figure 4.22 that the reconstructed signal resembles the original one.

Objective metrics are applied to evaluate the system performance. The metrics here

used are the percent root mean square difference (PRD) [54] and the percent mean

absolute difference (PAD). They are defined as:

V x[n] - x[n] PAD =^ xlOQ% (4.2.6)

Z^x[n]

PRD= E ^ P ^ x l O O % (4.2.7) ‘

1 i ^ ' w

where x[n] and x[n] are the original and reconstructed signal respectively.

The original and reconstructed signals are first aligned to each other using the first R-

wave of the ECG signal. And to minimize errors due to scaling, each of the signals is

normalized according to their own first R-wave magnitude.

The PRD and PAD were found to be about 23 and 27% respectively. One of the main

5 3

reasons for this error may be due to the excessive low-pass filtering especially to those

higher frequency components in QRS complex.

Also from the output signal, some minute periodic noise occurred. Another

measurement was undertaken with the intention of finding out the sources of the

periodic noise about 12Hz (Figure 4.23). The setup for this time was different from

that of last time. We use the YOKOGAMA signal monitor to obtain some hardcopies

of the input and reconstructed signals.

Periodic Noise in Reconstructed Signal 0.8| 1 1 1 1

0.7 - .. -

0.6 •• I -

0.5 - -

名 0 4 - -a

• � . 3 - . I A _

。2- I \ i \ -

� 1 / I / i

o W U w V V [ / ^wWl^|/^ W*A^ -0.1 1 ‘ ‘ 1

2.5 3 3.5 4 4.5 5 time/s

Figure 4.23 Excerpt from Reconstructed Signal in Time Domain

From Figure 4.23, we can observe that there are some periodic noises in the

reconstructed waveform. They are most prevailed during the ST segment of the ECG

viewed in the time domain. In order to find out the source of the 12Hz noise, another

experiment was set up. This time, a digital oscilloscope manufactured by

YOKOGAMA with hardcopy function monitored the signals. We monitored the

signals and printed some hardcopies out. The results are interesting, the specific 12Hz

periodic noises disappeared in this measurement. The reconstructed signals were very

54

similar to the original one.

The following figures are the recorded results.

- , — ‘ ‘ ，一一 _:�:1 ;' Pitffifff ^^â fe .i

_ _

P i P : i ^ M Figure 4.24The Original (Right) and Reconstructed Signals (Left) with fs=6.21kHz

(fs stands for the sampling frequency of conversion) rTJ .>^�T—-i-——厂-〒*1^ 納沪上.:-1.1^严—4~1-_0^黑爛^：11^ [IC; P,10 』 f l i ' i » / d PH =¾ 厂 J~ ^ i ' 0 6 i f / d 翻 1— _ : 1』 _一 : — 夠 1 i " T M . t < p :

I ； • ！

‘ ； I

一一[_ ： ~~t ：“…- __jlT :_—:1;:[JZ I— ..L. I 「 I — ； i : . ； .丨 ‘ _ ‘ .. • 1 _ - I I I I -t-H-t • ^ ~ 4~ —-«~»~ .+.>_^[ _4" .K+ .++.>^_^ _“》_—— I I I I I I I

' f : : i R i V ; : : : q ri4^yixffi' - - - ^ ^ i i ^ \ j - ^ - ^ i ^ ^ ^ 2 — 4 — . . i � — . t + 4 i j _ [__ . .

————1_丄丄—__丄—.f I ___j__ j

Figure 4.25 Original (Right) and Reconstructed (Left) Signal with fs=42kHz

55

r.jgrpt—TTW^ 一玄十 — 了 t — — j — - t [ - | —

�-2^;—_Aj<€" ——.丹——」 _ : _ _ — I — — _ A ^ I / P

< « ^ 4 j " b » ^ - . . — � p 4 ; ^ i ^ / ^ j " f i 2 I

Figure 4.260riginal (Lower) and Reconstructed (Upper) Signal with fs=42kHz

As the beat rate of Input signal increases, changes to the recovered waveforms were

observed as shown in Figure 4.27 and Figure 4.28.

^^•^J4^_iJVMJ,4_îrt�J^ H-JUN-i!：]：：： i ::;:M CHl = 2V [CH2 = 5F V n n |100rftMd DC P-10 DC p.ill) = = n T:222tfi$ l/^T:4. 504Hz_

• 1 厂二 : 1 「 ' 仁二丨，

j M ^ ^ ^ m m M ^ .\immmmi%l — i I V � ^

; : = [二口 =巾对

iU^^.^-1 八 “ 一 ^ ！ ^ 一一 ^ ‘

Figure 4.27 Original (Lower) and Reconstructed (Upper) Signals

(Beat Rate about two per second) •

56

C H1 =lTV ]C H 2 = W — ― … • — " f F 5 f m M 00 P + 10 DC Pil f l " • “ - ^

i 1T : l 1 i _ J j l l _ U ^ T : 9 . 0 0 9 H : •

i E - d _ c : _ V p

A=_jî2__»^jb�’如 Figure 4.28 Beat Rate about 4 per second

In Figure 4.25 to Figure 4.26, it was noted that the reconstructed signal resembles the

original one but distortions showed when the beat rate rose as shown in Figure 4.27

and Figure 4.28. It is because the reconstructed signal was obtained from a low-pass

filter. Higher frequency components are therefore attenuated. As the beat rate rises, the

frequency contents of ECG signal rises also making more information lost after

passing through the low-pass filter.

57

4.3 Wireless Application

4.3.1 General Description

In previous subsection, we can see that the Sigma-Delta Converter performs better and

better with the increase of sampling frequency. Theoretically, there is a decrease of 9

dB/octave of the total in-band quantization noise. Sampling frequency trades for better

resolution in the case of Sigma-Delta converter. \n a telemetric application, it is

desirable to have simple circuitry so that the whole circuit can be miniaturized. The

Sigma-Delta converter is well suited for this demand. In this subsection, we would like

to investigate the wireless application of the converter. In this investigation, it is

special that the 1-bit output of the converter is not processed digitally to give an N-bit

PCM output immediately as what most of the present Sigma-Delta converter chips do.

The digital signal processing (DSP) unit is instead separated from the 1-bit output. It

will be linked wirelessly to the 1-bit output. The simple 1-bit circuitry is intended to be

placed in the miniature patient-worn transmitter, while the digital processing unit is on

the receiver side that can be a DSP chip or a personal computer. The motivation behind

is that circuit complexity can be reduced in the miniature transmitter as there is no

digital processing unit required. In the following subsections, two scenarios are given.

In the first one, no digital processing is applied. A simple first-order butterworth

opamp low-pass filter recovers the signals. In the second one, binary output data is

picked up and power spectral densities are computed to anticipate the performance if a

digital low-pass filter recovers them by a remote computer or a DSP chip. In addition,

a simulation scenario is presented in the following subsection. It basically studies the

effect of bit errors induced during transmission to the MSE (Mean Square Error) and

58

the SNR of the converter. Comparison between the converter and conventional Ideal

N-Bit is given as well.

4.3.2 Simulation Results

The simulation scenario can be depicted in Figure 4.29.

\ Sigma-Delta / N- ~ _ Recovery Circuit and _

V 4 一 f + t — I • i ^ ^ ^ H ^ H O i ^ J i'lliWiiiiiiiiliilllWiiitMJ

Zero Detector

Sine Tone AWGN

Figure 4.29Block Diagram of Simulation Scenario

A pure 17 Hz sine tone is applied as the input signal. AWGN (Additive White

Gaussian Noise) with different mean powers are applied simulating the occurrence of

bit errors. Simulations are conducted with various signal amplitudes and sampling

frequencies. The input sine tone was firstly digitized by either Sigma-Delta or N-Bit

converter. AWGN was added on the binary bit stream. A zero detector was applied for

recovering bit values from the noisy binary bit stream. Analogue signal was

reconstructed by the recovery circuit and errors were measured between the original

and the reconstructed signal. MSE (4.2.1) and SNR (4.2.2) and BER (Bit Error Rate)

are applied for evaluation. The BER is calculated by counting the number of different

bits between transmitted and received bit pattem divided by the total number of bits in

the simulation. All the simulation is performed with MATLAB. Simulation results with

8-Bit converter are provided for comparison. Results will be shown in the following

figures and a brief discussion will be given hereafter.

5 9

fs=8000

0 _

z z r � -丨0 - ( - • -

/々 /' 一卜 - - • / : 身 - 一

-20 . j Z ^ ^ / 7 • A=1 I 卜丨 • A=0.8

u -30 r A A=0.5

^ / ' ' | - < - A = 0 . 1 丨

-• • • • ^-'"\1 -40 - . S

“ • • » •, / a • k A • ‘

-50 • / II • • • d

-60 ‘ ‘ ‘

0.0001 0.001 0.01 0.1 1 10 100

Noise Power

Figure 4.30MSE versus Noise Power (fs=8000 Hz) with Sigma-Delta converter (SD)

(A stands for the Input Signal Amplitude)

fs=-1(XX)

0 厂

_1� / ¢ ^ :

/,. 一-蜃 *

-lS : / ' 置一

. : i y ' § -20 , f ^Â=1 u ！ b ! - « - • A=0,8

S -25 卜 y ! / * • A=0.5

； / / r*-Aî | -30 : _ J j l .

• I • • ” • • '/

I 'I -35 - i

.^^ -40 j t „ — 4 — 4 _ — i _ — ^ z

I

1

0,0001 0.001 0.01 0.1 1 10 100

Noise Power

Figure 4.31MSE versus Noise Power (fs=4000 Hz) with SD

60

fs=2000

35 •

“ ^ ^ -10 r # - '

力/' -15 “ #

« n 一 = 丨 :o / / -鲁.A=0.8 S -20 : / / 飯 A=0,5 ^ i / / l-葡-A=0.1|

-25 • _ _ . _ • _ • _ _ « ^ ~ ~ - " * | j -3oL__, • - ]

z � -35 ：二本：忠“^:*〜.

I

-40 : 0.0001 0.001 0.01 0.1 1 10 100

Noise Power


ls= ! 0 0 0

0

/ T ^ 5 ^ X ^ . . ..•

z^ • _ _ •

声 - - 一 | 一

- 1 �： / M ~•~A = l

I /!, —鲁-A = 0.8 S -15 r /!|: -^--A = 0.5 ^ ； /|l: 一镯一 A = O J _

；___.__•___.__•_4^ -2 0 I • / . •

•……• • — M. Wk- 臉 »• , / 一一 « * . - .

m- m. m- » » 一 ‘ ,..• • -25 ^ 叙森 • •.... I

0.000 I 0.00 ! 0.0 1 0.1 1 10 I 00 N 0 i s c P 0 w c r


61

fs=5()0

0

2 X ^ z , •

4 《-一 6 / r

/ / / • / ' ，

-8 • / / - ^ A = I f y ' r ' — * ‘ A =0.8 I S , ' / ^ / •..轟.• A =0.5

‘‘“，...—一—-....—一..—.一......y f : • - A = " . '

-12 # • • • " “ ^ ~ ‘ ：！ / /

•' / -1 4 “ • . • • , - A - - • • • ‘ ‘ j

/ /

/ - 1 6 | k 昏 • • • - i *

0.0 00 1 0.00 1 0.0 I 0.1 I 10 1 00 N 0 i s c P 0 w c r


2

1 :

� > f ^ ： ./^,--

I -: A : : = : r : . . = : : r : : : : : . ' : ^ ^ , 二 : “ ‘ I -3 t 昏 * • . y ^ , / • A = 0 . 5

-4 丨- ^ y ^ / - « - A = 0 . 1 • • • • • » /

-5 : / / •

/ -6 /

/ /

7 昏 — — • — — . — — . — — • — — 槭

- 8 ‘

0.0001 0.001 0.01 0.1 1 10 100

N o i s e P o w e r /dB

Figure 4.35 MSE versus Noise Power (fs=250 Hz) with SD

62

fs=8000

40 r

€ : ! 普〜 3 0 11 • • • 1^ 年 \

\ \ \ 20 _ � \ F ^ A = , 1

\ 'j|jL • A = 0 .8 ？ \ i ^ V - - • - -A=0.5 I � 。 - \ O v 一 • A=0.1 ,

0 - 、 """""*" " " “

、、 - 1 0 -

-20 I ‘ ‘ ‘ ‘ ^ ‘ 0.000丨 0.00 1 0,()1 0.1 丨丨0 100

N 0 i s c P 0 w c r

Figure 4.36SNR versus Noise Power (fs=8000 Hz) with SD

fs=4000

40 I

‘V I N、、

30 I ： t t t � � t ^ ^ �

^ A \ ~ * ~ A = 1 \ - m— A = 0.8

20 ！ \ I • • -A- • • A = 0.5 *• • 滕 * 錢、、 \ - • - A = 0.1

. 、、\ ―― 5 \ \ 亏 10 i \ • \ “ : \ , k

\ ^ ^ ^ % ^ 0 ； �� -T:^^=^ 0 \ • A.

\ • _、、

、、乂 -10 � - * -20 ‘ - ^ ‘ ‘ ‘

0 .0001 0 .001 0 .01 0.1 1 10 100

Noise Power

Figure 4.37 SNR versus Noise Power (fs=4000 Hz) with SD

63

f s=2000

35 •

30 1( » » » « • . 、

'�’~~m 25 " A • A •...._

20 • V, 一

X | - ^ A = 1 15 \ |-l-A=0.8

I 10 • *>N ^ i . i A=0.5 ^ 1^ * • • “ ^ ~ ~ ~ ~ m • • ^ K - * - A = 0 . 1

‘ 5 - \ \ > ^ ^ ^ > ^ 一

0 - \ ^ ^ ^ r ^ ,

-5 • 乂、 - 1 0 • 、、匾

~ ~ ~ ~ » •

-15 -

-20 ‘ ‘ ‘ ‘ ^ ‘ 0.0001 0.001 0.01 0.1 1 10 100

Noise Power


fs=1000

2 5

2 0 ：

A .A • A • • • A- - . , ^ � •

丨5 r - - 4 - - - - ‘ - - - 4 - - - 4 - . ” . . ~ < 一 = , \ \ - •• • A=0.8

� \ , * A=0.5 1 0 ： \ \ ！ „ ,

� \ ^ ! - • - A=0.1 I

» 5 ！ ' \

I : v ^ co I、 �:r-"""--^^ 0 昏 — — . — — > - — — • “ — — » - - - , 1 ^ ¾ ¾ •

\ \ \

- 5 丨 \

I \

\ - 1 0 丨 V

I \ \

\ \

-15 : � � � • * •

- 2 0 I • —— ‘ ——

0.0001 0.001 0.01 0.1 1 10 100

Noise Power

Figure 4.39SNR versus Noise Power (fs=1000 Hz) with SD

64

fs=500

60

10 I ^ ^ A = »

• • • • V - 發 - A = 0 . 8

^ K - • … A = 0 . 5

5 \ 1 ^^“'• tfit-- >'•«"•"—• -ihk-- '• --• "ii-' - ij>,, 'j|j * *, . ....^ ^> — • — A=0.1 I : 、 “ ^ 0 � � ^ ^ ^ ^ ^ m - •

5 • . �

g “‘ „ • • , • . • - . • 00 11 • • • • _

\

-10 � \ \

\

^ -15 \ 、

\

、、、 Wh •

-20

.25 L -」-- .-.-. 1 1 . . 1. i. _ .- ... . — t

0.0001 0.001 0.01 0.1 1 10 100

Noise Power

Figure 4.40SNR versus Noise Power (fs :500 Hz) with SD

f s = 2 5 0

:^"=~~^ -—-0 . 0 0 0 L „ ^ ( K 0 ( i | ^ — _ j ^ . o _ i _ • 一 o j r - - _ i o i o o

I . — � • � ^ ~ ^ ^ ^ » _ _ I 、狐 . 〜 . ^ * ^ ~ ^ > •

-5 丨、瞧 m • . . . . . . • . . . . . . “ . . . . . . . “ . . . . . . . . • . . . . . . . . • . . . . . . . . . • . . ‘ . 、 . . . . . •

PQ 5 0 - 1 0 [- •

^ I ! - ^ A = 1 i — .• . . - A = 0 . 8

• 1 5 L. • A = 0 ,5 . m- m- m- m- 戰、 - « - A = 0 . 1

、、

、•^ ‘ 、、 - 2 0 ‘ \ �

• 、、、 I • I

-2 5 N o i s c P 0 w c r


65

Bit Error Rate (BER )

0.45

I 0.40

0.35 I

0 .30

0.25 ai UJ _ m 9

0.20 -

0.15 -

0.10 -

0.05 *

0 . 0 0 • ‘ • . 一 - • - ' • - - • ‘ ‘ ‘

0.0001 0.001 0.01 0.1 1 10 100

Noise Power

Figure 4.42BER (Bit Error Rate) versus Noise Power for Simulation with First-Order

Sigma-Delta Converter

S N R , M S E ve rsus N o isc L c v c l f o r an 8 -B it C o n v e r t e r (八= 1 )

60 ； - 0.45

)K ftM>r- III iiiiM|iiiii III ••丨丨丨丨丨I - • - III |__气-mm>-iKt>^- mmr!t||fmm mmm^/tfrnm- mmmWk丨丨丨丨丨 *->*jfWt _ j �為

» / 40 I \ /

I , 、 ft 1 0 . 3 5

X / 2 0 i I 、 / 」 0 . 3

\ / I � � - ^ 1 � . ” ! - 1：二，二 _ 望 . ^ I , , t ”.2

/ / . 2 0 r - g / • 0 . 1 5

/ / ！ f / 0.1 — ‘

/ / ~ • ~ M S E 1 .40 : / ,

# 4 ~ _ 一 S S R 1 / , ' • 0 . 0 5

• • • • _ 丨 ‘ / • _ ‘ _ .60 >« « ^ « ^ 0 ~ ^ _ SNR

0 . 0 0 0 1 0 . 0 0 1 0 . 0 1 0 . 1 1 ] 0 1 0 0 5 < 一 B E R N 01 i e L c V e 1 ‘

Figure 4.43 SNR, MSE, and BER versus Noise Power for a 8-Bit ADC with A=1

(MSE1, SNR1 are simulation results when there is no noise)

66

S N R , M S E ve r sus N o i s e L e v e l for an 8-B i t C o n v e r t e r (A = 0 . 8 )

60 "] 0 45

X I ^ ® W5 | ^ B M | HM I HHm Mj HNM NM0|MRHR 9H||MMHS M |MMI Ml(||pHMI ^ H Q ^

40 � /

\ y 0 35 ^ /

2 0 ^ , 0 . 3

\ / CQ W / • 0 25 I , 5 ^ ^ ‘ I • MSE1 送 ,, � J g ~ « 1 *SNRI ， r ^ "JSr-- ^ “ - . MSE g _ _ ~| 0.2 " • • .SNR

盧 _ / I . - ^ 'BER I

/ ‘ • 20 Z / - 0.15

/ / 像 / 0.1

f ‘ • " ' • i

‘ 尸 ‘0.05

. . 1 i 1 / , / , 1 , ,

-60 31 >|< ‘ iH m ‘ W' "•••• "-Vl^ - --- - J 0 0 .0001 0 .001 0.01 0 .1 1 10 100

Noisc Lcvcl

Figure 4.44SNR, MSE, and BER versus Noise Power for a 8-Bit ADC with A=0.8

S N R . M S E v e r s u s N o i s c L c v c I f o r an 8 - B i i C o n v c r t c r (A = 0 . 5 )

6 0 n 0 . 4 5 I

/ #• • 0.4

• _ ~«1~~ .~m— _ « | ~ ~ ^ ~ ~ m - __•丨丨丨丨丨丨丨""•"|H, ~ « I 4 0 . 入 f \

\ / j ““ * / 2 0 i " 1 ^ ‘ - 0 . 3

、、 / ea L m J o , < - -“ � « I '.-’ . • M S R 1 % „ I ^ ^ / ^ ~ 麵 - S N R I

• 丨、 / — 一 • _ 5 • _ M S E 1 J S T ^ " * ^ ^ : 0.2 , - ^ -SNR

* _ / | - * W . B K R • ‘

.20 ； Z ！ 1 " 5

/ / / / •“‘ ““二 • J

I • 产 '\ 0 . 0 5

i ^ - ^ • 6 0 if p W • W _.|__m_|_|_| Mtf ••••• -~M^- ‘ 0

0 . 0 0 0 I 0 . 0 0 I 0 . 0 1 0 . 1 I 1 0 1 0 0

N <» i s e 1 . e v e ]

Figure 4.45 SNR, MSE, and BER versus Noise Power for a 8-Bit ADC with A=0.5

67

S N R ， M S E ve rsu s N o i s e L e v e l f o r an 8-B it C o n v e r t e r ( A = 0 . 1 )

40 0.4 5

3 0通一 -H»— ^ ~ "M»~ - M ^ ^ «HM-•«-" <HW~ ^ ~ ^ < ~ J ^ . ,)4

\ / 2" \ ^ '>-35

\ / , 0 . 3 \ / ! \ J ' - - - • (>” S^：； § -10 V 一 / I 推如 MSE I 字- A>. f “ —• -SNR « / " ' - . (1.2 - " ^ -BER

-20 / / - 一 -* f

I / - 0.,5 “ •鎏 /

.40 : / - "•'

: J -50 • 广 • »"5

t ^ ^

-60 ) H ~ 丨丨丨 m ‘ >W I M< ‘ _ «"#»^ - J 0 0.( )0( )1 0 .001 0 .01 0.1 1 10 100

N oisc Lcvcl

Figure 4.46 SNR, MSE, and BER versus Noise Power for a 8-Bit ADC with A=0.1

MSE versus BER ol Sigiiu-Dcha and a 8-Bil Conwrtcr

10

"i j I I f I I ； ^ =

i " i I ： u j| I X Signu-Dcka S k »8-Btt ADC

-30 I “

-40 { *

t

- ' � i I

-60 —^ 1 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

BER

Figure 4.47MSE versus BER of First-Order Sigma-Delta and 8-Bit Converter

68

Discussion

It is observed that all the MSE's plots (Figure 4.30 to Figure 4.35 and Figure 4.43 to

Figure 4.46) rise suddenly at the sixth data point that coincide with the rise of BER (

Figure 4.42 and Figure 4.43-4.46). It is because the MSE's performance is dependent

on the amount of BER. BER, on the other hand, is given by the error function that

changes rapidly beyond some point of error. It can be shown in Figure 4.48.

Bii Kmv Kau: (BI-;R) vcnus fkihc Pt>vwr

a45丨

0.4 ： /

(、” [ / 0.3 L /

«吐， / =.X2 : /

(U5 ^ / 0.1 I /

她1 / 0 * * * * * / (X(XM)1 ().(X)1 0.01 0.1 I 10 1C0

Noise Pb>wr

Figure 4.48 BER Given by Error Function

Mathematically, BER with +1,-1 binary symbols and AWGN is given as (4.3.1)

BER = — f c i ~ j L ^ ) (4.3.1) 2 v 2 c r

where a^ is the noise power and Figure 4.48 is plot according to (4.3.1).

From Figure 4.48，it can be observed that the BER rises rapidly after the fifth data

point that coincides with the simulated BER. For an N-Bit ADC, there are chances that

significant errors can be generated from bit errors if the bit errors occur at the MSB

(Most Significant Bit) of the data sample word. For the case of Sigma-Delta converter,

bit errors tends to be smoothed out due to the fact that signal is recovered by low-pass

filtering in either analogue or digital form. It can be observed that in Figure 4.47 the

MSE's are lower in most cases for the Sigma-Delta converter. However, MSE for the

Sigma-Delta converter rises with the decrease of sampling frequency as discussed

previously in section 4.2.

69

4.3.3 Scenario I (Analogue Decoding)

4.3.3.1 Experimental Setup

y ^ Sigma-Delta Converter _ FM _

I � _ B — | C J Transmitter Side

^ y , ^ ^ , FMReceiverCircuit | First OrderButterworth | j ^ Scope

andComparatorCircuit | ~ ~ ^ ^ctiveFilter | ^

Receiving Side

Figure 4.49Experiment Setup of Scenario I ofWireless Application of Sigma-Delta

Converter

The experimental setup is depicted in Figure 4.49. From the signal generator, sine tone

of about 2 Hz was applied to the self-made first-order Sigma-Delta converter. The

converter output was fed into a FM transmitter (carrier frequency 88-100 MHz). On

the receiving end, a commercial FM receiver demodulated the transmitted signal and a

comparator was used for deciding whether the incoming signal represents "1" or "0".

A first-order low-pass active filter recovered the input signal that was shown on scope.

Brief circuit diagram for the converter is shown in Figure 4.50. It was built with ten

resistors, seven capacitors, one quad opamp, one D Flip-Flop, two NAND gates and

one instrumentation amplifier. Compared to the case with an 8-Bit Successive-

Approximation ADC, an 8-Bit D/A converter and a Register are required which

7 0

consumes more power and components in implementation.

�7/ <

, » i , 0 ra ？广 i «2/ ^

烛 y ^ ^ 1 ~ ^ ' \ > A ^ { r = f i f ^ 口 “ “ c ' f •{ <gffi Bf, v/^,^ • ™ 13 v>- 3 VX s „«1：, I »1> ™ fW2 \ " | \

^ ¾ = ^-^^><l ,,力 1 - n ^ ^ - " k ,严丨‘ ^ ° 功 ^ T j i _ pjiA n ^ ' ^ z � f — — ^ x ° ^ F r i ^

R° 0 <1 mi<aw Rr .iM f 皿 , ^ > r %J^__1|_^__t T° 1 ^ - “ ^ ^ � ^ ™ - r ^ > n ^ . . Y ' V " T V f ^ ^ 彻 '*• ®'» ^ r ^ '% ^ r>^ i| t l » ^

- ^ : . “ “ <m>J - 1 � f ! -

V J ' 5!¾>

R' C' „,„ '% 瓜 .ii qy：! „ q-m *m-* Y^ lh" j * V' ||^ 丨&1 2/ ^

ra�a.'K T LflR aW3 __i® j ^

kziTV^SP>��"•⑩ - i"� r- 1_乂 T a>Q"A CDVIIA 0 vu. 彻

Figure 4,50 Circuit Diagram for the Telemetric Device with Sigma-Delta Converter

Photographs were picked up by a digital camera and will be shown in the next section .

•

71

4.3.3.2Results __^^_ ^Q^Q ^ ^ ^ ^ H Q y | m ^ ^ ^ ^ ^ ^ H ^ ^ ^ ^ ^ ^ ^ M | Q ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ | ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ M ^ ^ ^ ^ ^ ^ ^ m i i i n m i i ^ ^ ^ ^ ^ g

a) b)

c) ‘

Figure 4.51Results of Scenario I (with Decreasing Sampling Frequencies: a) Highest,

c) Lowest)

4.3.3.3 Discussion

From Figure 4.51, it is observed that the noise increases as the sampling frequency

decrease. Better performance can be anticipated if higher order low-pass filter can be

applied for recovering digitized signal.

7 2

4.3.4 Scenario II (Digital Decoding)

4.3.4.1 Experimental Setup

The experimental setup is depicted in Figure 4.52. From the signal generator, sine

tones of about 1.4 and 14 Hz (peak-to-peak voltages of 0.2 and 0.4 respectively) are

applied to the Sigma-Delta converter. The sampling frequency for the Sigma-Delta

converter is about 800 Hz. Similar to the setup of Scenario I，the converter output is

fed into a FM transmitter (carrier frequency 88-100 MHz) and a commercial FM

receiver demodulates the transmitted signal. A comparator is used for deciding

whether the incoming signal represents "1" or "0". The WESlDAQ acquisition unit

with sampling frequency of 40kHz digitizes the binary bits. Data acquired is then

saved to files and further processing was performed by MATLAB. The Power Spectral

Densities of the converter output for 1.4 and 14 Hz signals are shown in subsection

4.3.4.2 (Figure 4.53 and Figure 4.54). The Power Spectral Densities is obtained from

262144 (2!8) point FFT of the acquired data with Hanning window applied.

Y ^ SignalGenerator | Sigma-Delta | FM k

_ ~ ^ Converter 画 ~ Transmitter B

7iSSiS®S�y:Si^ WilW£•••••"i"_]_ _jT""w7rfl | ^ | 5|j|y|jpt|jnj||| ,

v ^ FM Receiver i Comparator | WINDAQ _

H L _ J i z J i J Figure 4.52The Experimental Setup for Wireless Application of Sigma-Delta

Converter

7 3

Another experiment with the same circuit was done. Signals this time were picked up

from a subject with the concentric electrode in real time. Photographs showing two

excerpt of received signal have been shown in Figure 3.7 of Chapter 3.

4.3.4.2Results

Power Spectral Density of Converter Output T 1一1~-‘~"r~i 1 • ‘ ‘ ~ ‘ ~ ^ ‘ ~ ^ ^ ‘ “‘ ‘ ‘ ‘~‘

-15 - -

- 2 0 - i -

-25 - -

1 __ -5�- \ J j 丨 I = i i '丨 -55 - ‘

10� ‘ ‘ - .iQi frequency /Hz

Figure 4.53Power Spectral Density of Converter Output (Signal of about 1.4 Hz)

*

74

Power Spectral Density of Converter Output -15 i i .——.~~I~I i ： i ‘——‘~‘~•

- 2 0 - -_v|_liy - 5 0 ... i 丨丨 -

- 5 5 - j ； -

- 6 o l — — • ~ ^ ‘ ~ • “ “ ^ • ^ • ‘ ^ ~ ~ ‘ ~ ~ ^ 2 10' i o '

f requency/Hz Figure 4.54Power Spectral Density of Converter Output (Signal of about 14 Hz)

4.3.4.3 Discussion From Figure 4.53 and Figure 4.54, input sine tone can be noted in the power spectrum

of the binary output received wirelessly. Noise was intense at the vicinity of the input

tone especially for the case of input frequency about 1.4 Hz. Sources of excessive

noise can be due to the low sampling frequency of the Sigma-Delta converter and

external noise since measurements were made by the side of a notebook computer. »

7 5

4.4 Discussion and Conclusion Due to the relatively lower bandwidth of ECG signals, over-sampling is feasible. By

using one of the over-sampling techniques, namely the delta-sigma modulation, both

the modulating and recovering circuitry are simpler. The analog signal can be

reconstructed from the digital bit stream by simply using a low-pass filter either in

analogue or digital form. The original signal, as shown in the simulation results, can be

recovered by low-pass filtering. This is also verified in the experimental results. It is

noted that the performance of Sigma-Delta converter is related to the sampling

frequency. There is an improvement of 9 dB when sampling frequency is doubled

theoretically. From simulation results in section 0 and 0，the improvements are from 5

to 12 dB among various sampling frequencies, signal amplitudes and signal types.

In wireless application, it is shown from Scenario I (section 4.3.3’ Analogue Decoding)

that the recovered signal was better with higher sampling frequency. However, in

Scenario II (section 4.3.4，Digital Decoding), it is observed strong noise existed and a

good quality signal cannot be recovered. The source of strong noise is attributed to the

low sampling frequency {800 Hz) and external noise from notebook computer nearby

the measurement site.

Nevertheless, good quality results can be achieved by digital decoding. Even with a

first-order low-pass analogue active filter, good quality results can be obtained. It can

•be anticipated that with better filter characteristic provided by digital decoding,

filtering methods, better quality results can be obtained with suitable sampling

frequency.

7 6

Chapter 5 Conclusion and Future

Work

5.1 General Conclusion

With the goal of rendering easy and prompt monitoring of ECG signal, the idea of

Wireless Electrode (WE) is roused. A WE is consisted of a "Single-Electrode" and

radio telemetric circuitry. The "Single-Electrode" is for minimizing the number of

electrodes attached to the body surface while the radio telemetry capability provides

patients with ambulatory freedom.

"Single-Electrode" is indeed a virtual one. Within the "Single-Electrode", two contact

points are still required by means of a concentric electrode assembly. The concentric

electrode scheme provides more localized information on the body surface ECG as

noted by various researchers as discussed in Chapter 3. Typical signals are shown in

this work.

Radio telemetry applies to the "Single-Electrode" making it a WE (Wireless

Electrode). Analogue and digital transmissions can be implemented. In case of digital

transmission, a first-order Sigma-Delta converter is used and results are shown in this

work. The Sigma-Delta converter is attractive due to its simplicity in analogue

circuitry and its being tolerant to component imperfections.

High-resolution output is provided by the Sigma-Delta converter by means of digital

signal processing unit. Sigma-Delta converter originally gives only one bit binary

output with simple analogue circuitry. The digital signal processing unit process on the

one bit stream to give multi-bit output. This unit is usually fabricated with the basic,

77

simple analogue circuitry of the Sigma-Delta converter as a single chip. The inclusion

of the digital processing unit increase the circuit complexity. An idea is to separate the

two by a wireless link. The transmitted single bit binary stream is received and

processed to provide high-quality recovered signal. This scenario is implemented and

tested with the concentric electrode.

5.2 Future Work In this study, the idea of WE is demonstrated. However, the size of device is not fully

minimized. The whole circuit can be further minimized in size by using VLSI or ASIC

technology. The impact of the WE can be exploited more if the size is minimized. The

circuit implemented is on the first-order Sigma-Delta converter architecture that is the

simplest one. Other architectures can be investigated for better performance.

78

Bibliography

[1] Annual Report 1995/96, Department of Health, pp73, Table 14，1996.

[2] HospitalAuthority Statistical Report 1997/98, pp23, Table 1.11’ 1998.

[3] 中國統計年鑒,國家統計局編，中國統計出版社，丁&1>^21-21，21-22，

pp775-775, 1998

[4] http://www.who.int/whr/1998/factse.htm. Fifty facts from The World Health

Report 1998.

[5] http://www.who.int/whr/1998/exsum98e.htm. Executive summary.

[6] Tatsuo Togawa, et al., Biomedical Transducers and Instruments, pp267, CRC

Press, 1997.

[7] Dobromir Dobrev, Ivan Daskalov, "Two-Electrode Telemetric Instrument for

Infant Heart Rate and Apnea Monitoring", Med. Eng. & Phys. 20, pp729-734,

1998.

[8] Masayuki Ohyama, et al., "Active Wireless Electrodes for Surface

Electromyography", 18''' Annual International Conference of the IEEE EMBS,

Amsterdam, pp295-296, 1996.

[9] Bruce C. Towe, "Passive Biotelemetry by Frequency Keying", IEEE Trans.

Biomed. Eng., vol. BME-33, no. 10，pp905-909, October 1986.

[10] J.M. Darnieder, D.C. Jeutter, "Miniature Microcontroller-Based heart Rate

Telemeter", IEEE，pp900-901, 1994.

7 9

http://www.who.int/whr/1998/factse.htm

http://www.who.int/whr/1998/exsum98e.htm

[11] Masaki Kyoso, et al., "Development of Medical Telemetry System By Spread

Spectrum Communication", 18'^ International Conference of the IEEE EMBS,

Amsterdam, pp301-302, 1996.

[12] Masaki Kyoso, et al., "Performance of Spread Spectrum Medical Telemetry

System in a Sharing Frequency Band with Current Telemetry System", IEICE

Trans. Commun., vol. E78-B, no. 6, June 1995.

[13] http://www.cmetele.coiWeeg%20Product.htm. CME Telemetrix Lic, 1998.

[14] Lu C-C, "Pasteless, Active, Concentric Ring Sensors for Directly Obtained

Laplacian Cardiac Electrograms", pp280-283, Proc. ofBSI99.

[15] Bin He, Richard J. Cohen, "Body surface Laplacian ECG Mapping", IEEE Trans.

Biomed. Eng., vol. 39，no. 11, ppll79-1191, November 1992.

[16] R. Manning, et aL, "Concentric Electrodes for Cardiac Mapping", pp88-89. lCf^

Annual International conference IEEE EMBS, 1998.

[17] H.R Kimmich, "Modern Patient Care Using Biotelemetry: Its Potential and

Technical Realization at Present and in the Future", Med. Prog. Technol., 9，

pp85-93, 1982.

[18] D.C. Jeutter, "Telemetry Is Coming of Age", IEEE EMB Magazine, pplO-11,

March 1983.

[19] D.C. Jeutter, “Overview of Biomedical Telemetry Techniques", IEEE EMB

Magazine, ppl7-24, March 1983.

[20] W.G. Scanlon, "Low-Power Radio Telemetry: the Potential for Remote Patient

Monitoring", J. Telemedicine & Telecare, 2, ppl85-191, 1996.

80

[21] J. Kuiper, "Medical Telemetry Systems", IEE Medical Electronics Monographs

1-6’ ppl l l -145, Ed. By B.W. Watson, Peter Peregrinus Ltd., 1971

[22] R.S. Mackay, "Biomedical Telemetry: the Formative Years", IEEE EMB

Magazine, ppll-17, March 1983.

[23] Harold Sandler, "Biotelemetry: Its First 50 Years", Proceedings of the 3'^

International Symposium on Biotelemetry, ppl , 196.

[24] G.C. Grumley, et aL, "On the Design and Assessment of a 2.45 GHz Radio

Telecommand System for Remote Patient Monitoring", Med. Eng. & Phys., 20,

pp750-755, 1998.

[25] A.M. Drewes, et aL, "Seven-Channel Digital Telemetry System for Monitoring

and Direct Computer Capturing of Biologicai Data", J. Biomed. Eng., Vol. 15，

pp435-440, September, 1993.

[26] J.G. Webster, Medical Instrumentation: Application and Design, 3 ^ Ed., Wiley,

1998.

[27] Brian D. McKean and David A.Couch, "Telemetry Implementation System for

Chronically Lnplanted Glucose and Oxygen Sensors", IEEE Trans. Biomed. Eng.,

Vol. 35, No. 7, July 1988.

[28] Ferrel G. Stremler, Introduction to Communication Systems, Chapter 7, 3rd Ed.,

Addison Wesley, 1990.

[29] Francis A. Spelman, et aL, "A System to Acquire and Record Physiological and

Behavioral Data Remotely from Nonhuman Primates", IEEE Trans. Biomed Eng.,

Vol. 38(12), ppll75-, December, 1991

[30] Yuichi Kudo and Kazuo Ueda, "The Measurement of the Electric Activity of the

81

Olfactory Bulb in Free Swimming Carp {Cyprinus Carpio L.) by Underwater

Telemetry Systems", IEEE Trans. Biomed. Eng., Vol.BME-27, No.l2, pp694-,

December, 1980

[31] Jean M. Damieder and D. C. Jeutter, "Miniature Microcontroller-Based Heart

Rate Telemeter Processes Single Precordial Lead", Proceedings of the 16th

Annual International Conference ofthe IEEE EMBS, pp900 -901, vol.2, 1994.

[32] A.C. MeetingVanRijn, et al., "A Wireless Infrared Link for a 16-Channel EEG

Telemetry System", Proceedings of the 16thAnnual International Conference of

the IEEE EMBS, pp906-907 vol.2, 1994

[33] Medical Electronics, pp82-86, June 1997.

[34] Medical Electronics, pp76-80, June 1998.

[35] Farah Magrabi, et al., "A Web-Based Approach for Electrocardiogram Monitoring

in the Home", Intl. J. Med. Info., 54, ppl45-153, 1999.

[36] W.H. Ko, M.R. Neuman, "Implant Biotelemetry and Microelectronics", Science,

Vol. 156, pp351,April, 1967.

[37] Scanlon, et al., "RF performance of a 418 MHz Radio Telemeter Packaged for

Human Vaginal Placement, IEEE Trans Biomed Eng. Vol. 44(5), pp427-430, May,

1997

[38] J. Taylor, et al., "Towards Multi-patient Leadless and Wireless Cardiotocography

Via RF Telemetry", Med. Eng. & Phys. 20, pp764-772, 1998].

[39] Robert Puers, Patrick Wouters, "Adaptable Interface Circuits for Flexible

Monitoring of Temperature and Movement", Analog Integrated Circuits and

Signal Processing, 14’ ppl93-206, 1997

82

[40] Kenneth R. Beerwinkle and Kohn K. Burch, "A Low-Power Combination

Electrocardiogram-Respiration Telemetry Transmitter", pp484-486, IEEE Trans

Biomed Eng, November, 1976

[41] J. G. Proakis, Digital Communications, McGraw-Hill, 1995.

[42] Delta-Sigma Data Converters: Theory, design, and Simulation, Ed. By S.R.

Norsworthy, et aL, ffiEE Press, 1997.

[43] K. M. Daugherty, Analog-to-Digital Conversion: A Practical Approach,

McGraw-Hill, 1995.

[44] R.L. Geiger, et al., VLSI Design Techniques for Analog and Digital Circuits,

McGraw-Hill, 1990.

[45] Paul Horowitz and Winfield Hill, The Art of Electronics, 2"' Ed., Cambridge

University Press, 1989.

[46] Eugene R. Hnatek, A User's Handbook ofD/A and A/D Converters, John Wiley &

Sons, 1976.

[47] R.W. Stewart and E. Pfann, "Oversampling and Sigma-Delta Strategies for Data

Conversion", IEE Electronics & Communication Engineering Journal, pp37-47,

February 1998.

[48] Augusto Marques, et aL, "Optimal Parameters for AZ Modulator Topologies",

IEEE Trans. Circuits and Systems—II: Analog and Digital Signal Processing,

Vol.45, No. 9, September 1998.

[49] G.R. Cooper and C.D. McGillem, Modern Communication and Spread Spectrum,

McGraw-Hill, 1986.

83

[50] Medical Electronics, ppll4-119, October 1997.

[51] Donald Christiansen, Editor, Electronics Engineers ‘ Handbook, 4' Ed., pp30.46,

McGraw-Hill, 1996.

[52] Mitra, Sanjit Kumar "Digital Signal Processing: A Computer-Based Approach",

McGraw-Hill, 1998,

[53] N.V. Thakor, et al., "Design, Implementation and Evaluation of A

Microcomputer-Based Portable Arrhythmia Monitor", Med. & Biol. Eng. &

Comput.,ppl5\-\59, March, 1984.

[54] A. Enis fe t in , et al. "Multichannel ECG Data Compression by Multirate Signal

Processing and Transform Domain Coding Techniques", IEEE Trans. Biomed.

Eng., vol. 40(5), pp495-499, May, 1993.

[55] http://ecg.mit.edu/dbsamples.html. MIT-BIH Database (Samples of Physiologic

Signal Databases).

[56] R. Manning, et al, "Concentric Electrodes for Cardiac Mapping", IEEE EMBS

lffÂnnual International Conference, pp88-89, 1988.

[57] Frederick G. Weiss, "Implications of Silicon Monolithic RFICs for Medical

Instrumentation and Telemetry", Silicon Monolithic Integrated Circuits in RF

Systems, 1998. Digest ofPapers. 1998 Topical Meeting on, Sept. 1998

84

http://ecg.mit.edu/dbsamples.html

List of Abbreviations ADC Analogue-to-Digital Converter: A device that converts an analogue signal

into binary digits

AWGN Additive White Gaussian Noise: One kind of noise that is white and

Gaussian in nature and it is added to signal in concern

BER Bit Error Rate: Probability of a Binary Bit in error

DSP Digital Signal Processing: Processing on digitized signals

ECG Electrocardiogram: Electrical Signal picked from the body surface

MSE Mean Square Error: Mean of Square of Difference (or Error) between

two Signals

SNR Signal-to-Noise Ratio: The Ratio of Signal Power to that of Noise

*

85

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