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CHAPTER 5

SOFTWARE DEVELOPMENT USING LabVIEW FOR MEDICAL

INSTRUMENTS

5.1 Programming of PCG

The software used for analyzing the PCG signal obtained from the hardware is

LabVIEW. The software acquires the PCG signals from the hardware through NI DAQ.

Once the PCG signals data is obtained it is processed using various stages.

The flow chart to analyze the heart signal is shown in the fig 5.1. It gives the step

by step procedure of the backend operation of the analysis form. The developed program

extracts the wanted information on the signal.

i. Sampling

To convert a signal from continuous time to discrete time, a process called

sampling is used. The value of the signal is measured at certain intervals in time. Each

measurement is referred to as a sample. To convert the acquired signal into a form that

can be treated by a computer has to be digitized, i.e. reduced from an analog form (time-

and value-continuous) to digital (time- and value-discrete). This process is called A/D

conversion or sampling. It consists of two steps:

a. First, the analog signal s(t) is converted to a time-discrete form x(n) by sampling its

value (in the more specific sense of the word) in periodical intervals of duration ts, the

sampling period,.

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Pre-Processing

Feature Extraction

Fig 5.1: Flowchart for Analyzing Heart sounds and Displaying Heart Rate

Start

DAQ

Filter

Baseline Wondering

Removing

Wideband Noise

Suppression

Peak Detection

Envelope Development

Time Interval

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This type of power line interference is eliminated by using a programmable digital

Butterworth band reject filter.

The baseline wondering are suppressed by wavelet transform approach method.

The LabVIEW advanced signal processing tool kit (ASPT) provides WA Detrend Virtual

Instrument (VI) which cab be used to remove the low frequency baseline wandering (or

trend) of a signal. An internal parameter called trend level is required for base line

removal. The trend level is calculated as follows

Where t is the sampling duration and N is the number of sampling points in t time.

After the baseline wandering is suppressed, the PCG signal becomes more

stationary and explicit than the raw PCG signals. The remaining noise is complex

stochastic process with wideband nature. To suppress the wide band noise, the wavelet

Denoise VI module from LabVIEW is used.

This VI first Decomposes the PCG signal into several sub bands by applying the

wavelet transform, and then modifies each wavelet coefficients by applying a threshold

or shrinkage function, and finally reconstructs the denoised signal. In the present study,

we have used undecimated wavelet transform (UWT) sym 5 with single level and soft

thresholding for the wavelet denoising VI block setup. These settings perform perfect

denoising on the original PCG signal, and smoothen the signal without suppressing PCG

features such as peaks of the first, second and so on. For effective feature extraction, we

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apply a wavlet denise VI with UWT sym5 and multiple level on the detrended signal to

make only the peaks of first and second if possible third and fourth signal more distinct.

b. PERFORMING FEATURE EXTRACTION ON PCG SIGNALS:

For the purpose of diagnosis, we often need to extract various features from the

preprocessed PCG data, including the peaks of first and second heart beats.

The multiresolution analysis Express VI used decompose the PCG signal by 6

level, Daubechies 6 (db6) wavelets and then reconstructs the signal with D4,D5 & D6 sub

bands. D4, D5 and D6 sub bands will be kept for reconstruction of PCG signals because

it makes more accurate for detection of heart sounds (beats)

c. Feature Extraction:

After detrending the PCG signal and applying denoising VI, the resulting signal is

applied to the wave peak detection VI. Peak detection is one of the most important time

domain function performed in signal monitoring. Peak detection is the process of finding

the locations and amplitudes of local maxima and minima in a signal that satisfies certain

threshold value. In the present study the waveform detector used records the locations of

the signal peak above some threshold value in an output array in terms of indices.

The peak detector function used has some important features, such as

� The function can process many blocks of data that are part of the same signal.

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� This function retains the internal states and history information from one call to the next.

This VI internally allocates the structure that contains the information on the first block

of data and destroys it on the last block of data.

� The peak location function gives the peak locations at fractional indices. It uses the

quadratic fit algorithm and returns the peak locations as floating point numbers, not as

integers index values.

� The function allows implicit noise reduction while finding the peaks. Using the width

parameter effectively reduces the noise in the input signal when finding the peaks.

� The function performs a quadratic curve fitting to find the peaks. The core of the peak

finding algorithm consists of fitting a parabola to successive groups of points, equal in

number to width. The function uses the coefficients from the fit to determine whether a

peak or valley is present.

� For each set of points, the algorithm performs the least-square quadratic fit, and then

performs a series of tests on the coefficients to see whether they meets the criteria for a

peak. The function checks whether each parabola is at a local maxima, determines the

sign of the quadratic coefficients, which indicate the parabola’s concavity, and finally

checks that the peak is above the designated threshold.

At the output of waveform detector both amplitude and time locations are taken

into considerations for the construction of envelope. The envelope will be constructed for

a sampled value of the input signal.

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To develop the envelope the following notations are considered

MPEAK = Maximum Amplitude of the sampled signal

MT1 = Time of the maximum amplitude --------(2)

THR = Threshold of the signal

TSEC = limits of the signal where peaks are measured

The technique used for PCG feature extraction is an approach which employs

peak or valley detection and to construct envelopes based on the peak values detected

which satisfies certain threshold conditions. The developed envelopes of the PCG give an

approximate representation of instantaneous magnitudes. This is done because the

envelopes are easier to process the PCG while they retain enough information to detect

the cardiac events.

1. Determine the MPEAK and MT1

2. Merge the peaks that are above THR that are very close to MT1 about (20 msec apart) to

the MPEAK value

3. If the peaks are lower than the threshold ignore that and search for the new local maxima

4. Assign systole the envelope that has short intervals which do not vary much over time

5. Assign diastole to the remaining intervals.

6. Assign S1 or S2 labels to each peak so that S1 signals the start of systole and S2 the start

of diastole.

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5.2 Programming of Pulse Oximeter.

Our aim is to design and build an inexpensive, accurate, and user friendly pulse

oximeter that will allow doctors to receive pertinent data, which can be used to provide

medical attention to patients.

The program control elements and the control functions used in programming of

LabVIEW based pulse oximeter are

1. Sequence Structure

2. Extraction Portion of Signal.

3. Amplitude and Level measurements.

4. Curve Fitting.

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Fig 5.6: Flow Chart for signal Capture

Start

Switch ON the RED

LED and wait

Capture RED Sensor

Value

Switch OFF RED LED

Switch ON the IR LED

and wait

Capture the IR Sensor

Value

Switch OFF the IR

LED

End

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i.Sequence Structure:

Determining the execution order of a program by arranging its elements in a

certain sequence is called control flow. In labVIEW there are sequence structures to

obtain control flow within a data flow frame work. A sequence structure is an ordered set

of frames that executes sequentially. A sequence structure executes frame 0, followed by

frame 1, then frame 2, until the last frame executes. Only when the last frame completes

does the data leave the structure. There are two types of sequence structures the flat

sequence structure and the stack sequence structure.

Fig 7a: Flat Sequence Structure Fig 7b: Stack Sequence Structure

The Flat sequence structure is a program control element whose sub diagrams are

organized from left to right, side by side. In my present work flat Sequence Structure is

used because the program code designed for determining of SPO2 and heart rate needs to

execute separately for RED LED and IR LED with necessary delay in-between them. A

Frame is a sub diagram of structure.

ii. Extraction Portion of Signal:

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It is a signal manipulation Sub VI which Extracts portions of data from the input

signals and returns the extracted data. A point or a range of data can be extracted, and the

extracted data can be by time or index. The time and index of the first occurrence of a

value can also be found out.

This sub VI can be configured so that a portion of the acquired signal is extracted

from which the DC valve and AC valve is determined which will be used for processing

for heart rate and SPO2.

iii. Amplitude and Level measurements:

It is a signal analysis Sub VI which is used for measurement of voltages (AC &

DC).

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This sub VI can be configured to measure to measure values such as DC ,RMS

,Peak to Peak, maximum Peak, Minimum Peak and so on.

iv. Curve Fitting:

As the usage of digital measurement instruments during the test and measurement

process increases, acquiring large quantities of data becomes easier. However, the

methods of processing and extracting useful information from the acquired data become a

challenge.

During the test and measurement process, you often see a mathematical

relationship between observed values and independent variables, an independent variable

that results from an inaccurate measuring device. One way to find the mathematical

relationship is curve fitting, which defines an appropriate curve to fit the observed values

and uses a curve function to analyze the relationship between the variables.

The curve fitting performs the following tasks:

� Reduce noise and smooth data

� Find the mathematical relationship or function among variables and use that

function to perform further data processing, such as error compensation, velocity

and acceleration calculation, and so on

� Estimate the variable value between data samples

� Estimate the variable value outside the data sample range

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Different fitting methods can evaluate the input data to find the curve fitting

model parameters. Each method has its own criteria for evaluating the fitting residual in

finding the fitted curve. By understanding the criteria for each method, you can choose

the most appropriate method to apply to the data set and fit the curve. In LabVIEW, you

can apply the Least Square (LS), Least Absolute Residual (LAR), or Bisquare fitting

method to the Linear Fit, Exponential Fit, Power Fit, Gaussian Peak Fit, or Logarithm Fit

VI to find the function f(x).

5.2.1 Theoretical description:

Lab VIEW program is to calculate:

� Percent oxygenation

� Pulse rate

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The percent oxygen in the blood of a patient can be calculated by measuring the

amount of light that is transmitted through the fingertip using a photodiode as a sensor

and two LEDs (red and IR) as the light source. The photodiode detects the transmitted

light and the DAQ device outputs a voltage that corresponds to the amount of light

detected. The final signal appears as the waveform of a pulse. There will be two

waveforms: one signal for the red LED and a second for the IR LED. Once these signals

are generated using the DAQ device, several measurements are made. First, the amount

of time that elapses in between peaks will be determined in order to calculate the pulse

rate. Second, the AC and DC of the voltage signals will be determined in order to

calculate the percent of oxygenation. Once these values are found, the modulation ratio

is calculated. The modulation ratio is essentially the ratio of the magnitude of the red

waveform to that of the IR waveform.

To be able to record and calculate all of these measurements, the code is split into

a series of 5 different events that occur from left to right of our code.

The first event that occurs is turning ON the red LED by generating step voltage

using the DAQ device, this is shown in the first frame of the flat sequence structure used

to execute the developed code.

Then, the second event occurs which includes several steps:

� There is first a 5 second delay before anything happens to ensure that the red light

is turned on

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� The light transmittance is detected by the photodiode and outputs as a voltage

signal for 5 secs using DAQ device input.

� The generated signal is cut into a section containing time between 3 secs to 8

seconds

� The DC (average) value between 3 to 8 secs is calculated

� The residual is obtained.

� The residual's peak to peak (AC) voltage value is found out.

� The time interval in between peaks is found.

� The peak to peak time interval obtained is divided by 60 in order to obtain the

pulse rate.

All the above steps are performed by the sub diagram placed in the second frame

of the flat sequence structure.

The third event turns on the IR LED and turns off the Red LED by generating step

voltage using the DAQ device, this is shown in the third frame of the flat sequence

structure.

The fourth event includes is a repeat of the second step, however all values will be

found for the IR signal instead of the red LED signal. Also, it is unnecessary to find the

peak to peak time interval for the IR signal because the pulse rate should be the same for

both LED signals, and therefore, calculating pulse rate from the red LED only is

sufficient. This is performed by the sub diagram placed in the fourth frame of the flat

sequence structure.

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The fifth event turns off both Red and IR LEDs.

After these values are determined, the modulation ratio can be calculated by using

the equation R = (AC/DC) RED / (AC/DC) IR. Then, the empirical data is referenced in

order to find the percentage of oxygenation. The calibration curve was obtained from the

below graph and corresponding equation:

Y= - 10.291 * X * X – 27.865 * X + 117.93 - - - - - - - - - -( )

5.3 Programming of ECG

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Electrocardiogram (ECG) is a diagnostic tool that measures and records the

electrical activity of the heart in exquisite detail. Interpretation of these details allows

diagnosis of a wide range of heart conditions.

5.3.1 ECG SIGNAL PROCESSING

Generally, the recorded ECG signal is often contaminated by noise and artifacts

that can be within the frequency band of interest and manifest with similar characteristics

as the Preprocessing ECG signals helps to remove contaminants from the ECG signals.

Broadly speaking, ECG contaminants can be classified into the following categories

• power line interference

• electrode pop or contact noise

• patient–electrode motion artifacts

• electromyographic (EMG) noise

• baseline wandering

Among these noises, the power line interference and the baseline wandering are

the most significant and can strongly affect ECG signal analysis. Except for these two

noises, other noises may be wideband and usually a complex stochastic process which

also distort the ECG signal.

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Preprocessing of ECG

Data

Fig 5.8 : Flow Chart for ECG Signal Capture, Process and Display

Start

Acquire ECG Data

through Electrodes

using NI-DAQ

Filter

Baseline Wondering

Removing

Wideband Noise

Suppression

Display ECG data Storing the ECG

data in a file

Stop

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The power line interference is narrow-band noise centered at 60 Hz (or 50 Hz)

with a bandwidth of less than 1 Hz. Usually the ECG signal acquisition hardware can

remove the power line interference. However the baseline wandering and other wideband

noises are not easy to be suppressed by hardware equipments. Instead, the software

scheme is more powerful and feasible for offline ECG signal processing.

5.3.2 Removing Baseline Wandering

Baseline wandering usually comes from respiration at frequencies wandering

between 0.15 and 0.3 Hz, and we can suppress it by a high pass digital filter. We also can

use the wavelet transform to remove baseline wandering by eliminating the trend of the

ECG signal. The Lab VIEW DFDT provides an intuitive and interactive way to design

and implement finite impulse response (FIR) or infinite impulse response (IIR) filters

easily and effectively.

Fig 5.9: Using the WA Detrend VI to remove baseline wandering

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In addition to digital filters, the wavelet transform is also an effective way to

remove signals within specific subbands. The LabVIEW ASPT provides the WA Detrend

VI which can remove the low frequency trend of a signal. We can see that the resulting

ECG signals contain little baseline wandering information but retain the main

characteristics of the original ECG signal. It is also seen that the wavelet transform-based

approach is better because this approach introduces no latency and less distortion than the

digital filter-based approach.

5.3.4 Removing Wideband Noise

After we remove baseline wandering, the resulting ECG signal is more stationary

and explicit than the original signal. However, some other types of noise might still affect

feature extraction of the ECG signal. The noise may be complex stochastic processes

within a wideband, so you cannot remove them by using traditional digital filters. To

remove the wideband noises, we can use the Wavelet Denoise Express VI. This

LabVIEW based higher-level Express VI first decomposes the ECG signal into several

sub-bands by applying the wavelet transform, and then modifies each wavelet coefficient

by applying a threshold or shrinkage function, and finally reconstructs the denoised

signal. The following figure shows an example of applying the un-decimated wavelet

transform (UWT) to the ECG signal.

For the purpose of diagnosis, we often need to extract various features from the

preprocessed ECG data, including QRS intervals, QRS amplitudes, PR intervals, ST

intervals, The detection of the R-peaks and consequently of the QRS complexes in an

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ECG signal provides information about the heart rate, the conduction velocity, the

condition of tissues within the heart as well as various abnormalities.

Fig 5.10: Removing wideband noises from an ECG signal by applying the UWT

It supplies evidence for the diagnoses of cardiac diseases. For this reason, it has

drawn considerable attention in the ECG signal processing field. After extracting the

features by performing QRS complex detection, we can analyze the features with other

methods. For example, we can perform heart rate variability (HRV) analysis on the R-R

interval signal to demonstrate the state of the heart and nerve system.

ECG Signal is analyzed using built-in function of Wavelet Transforms to

calculate the heart rate. The normal value of heart beat lies in the range of 60 to 100

beats/minute. A slower rate than this is called bradycardia (Slow heart) and a higher rate

is called tachycardia (Fast heart). If the cycles are not evenly spaced, an arrhythmia may

be indicated. If the P-R interval is greater than 0.2 seconds, it may suggest blockage of

the AV node. Wavelet is found to be superior to the conventional FFT method in finding

the small abnormalities in ECG signals. The equation to calculate heart rate is given

below:

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Heart Rate = (1/RR Interval) * 60

5.4 Programming of Humidity and Temperature Measurement:

A graphical program is developed in LabVIEW to convert the received voltage

signal to relative humidity. LabVIEW represents the continuous variation of relative

humidity with time graphically. It also keeps record of the relative humidity and

temperature, which can also be stored as numeric value in file and perform as backup.

The software running on the computer is based on the LabVIEW suite. It is a VI

(virtual instrument) that is able to communicate with the measuring circuit and control

the measuring procedure. It is able to record the data acquired in real-time, since the

acquisition frequency is selected to be at a relaxed number of 1000Hz. The LabVIEW

program can afterwards save the measurements into files for later processing. It can also

provide some means of signal processing. For example it can help acquiring maximum

and minimum values automatically for each inhale and exhale which is needed in this

kind of measurements. The measurements are saved in a file format that is easy to open

by typical spreadsheet programs. Also, the LabVIEW suite provides many ways of

drawing the measurements into graphs, thus making it easier to view different

phenomena related to this study.

Special care has been given so that the measuring procedure itself is quite simple

for an operator, where cooperation with a subject is needed. So, the interface is quite

simple in appearance, but nevertheless significant programming has been made on the

LabVIEW so that the result is satisfactory for our purpose in terms of automation and

accuracy.

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1. “A color spectrographic phonocardiography (CSP) applied to the detection and

characterization of heart murmurs: preliminary results”.- Reza Ramezani

Sarbandi, John D Doyle, Mahdi Navidbakhsh, Kamran Hassani, Hassan Torabiyan

2. COLOR SPECTROGRAPHIC PHONOCARDIOGRAPHY FOR THE

DETECTION AND CHARACTERIZATION OF PEDIATRIC HEART

MURMURS: A CASE study- REZA RAMEZANI SARBANDI, KAMRAN

HASSANI, Department of Biomechanics, Science and Research Branch, Islamic

Azad University Tehran, Iran

3. “Novel method of predicting the optimal atrioventricular delay in patients with

complete AV block, normal left ventricular function and an implanted DDD

pacemaker.” Miki Y, Ishikawa T, Matsushita K, Yamakawa Y, Matsumoto K, Sumita

S, Uchino K, Kimura K, Umemura S.- Department of Medical Science and Cardiorenal

Medicine, Yokohama City University School of Medicine, Yokohama, Japan.

4. Sjöholm Johan, “Development of a Phonocardiography Amplifier”,

Master’s Thesis, spring 1999, Helsinki University of Technology

5. Moss and Adams, “Heart diseases in infants, children, and adolescents”,

Williams & Williams, Baltimore, 1995, pp. 135-146

6. Phonocardiography Signal Processing- By Abbas K. Abbas, Rasha Bassam

1. Jackson L.B., “Digital Filters and Signal Processing”,

Kluwer Academic Publishers, Boston, 1986, pp. 153-167