Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010 Wireless ECG...

Prof. Dr. Bingli Jiao

Wireless Communications LabPeking University

Oct. 13, 2010

Wireless ECG System

Outline

1. Necessarity of E-healthcare 2. Development of Wireless Healthcare in China

3. Wireless ECG System in PKU

4. HHT Algorithm for ECG Signal Diagnosis

Population of elder persons In last century, the rate of growth of the elderly population (persons 65 years old and over) has greatly exceeded the growth rate of the population of the country as a whole.

About 1 in 8 Americans were elderly in 1994, but about 1 in 5 would be elderly by the year 2030. The oldest old (persons 85 years old and over) are a small but rapidly growing group, comprising just over 1 percent of the American population in 1994.

This population comprised 3.5 million persons in 1994, 28 times larger than in 1900. From 1960 to 1994, this group increased 274 percent. Overall, the oldest old are projected to be the fastest growing part of the elderly population.

Bingli Jiao @ Peking University 3

1. Necessarity of E-healthcare


Heart disease is the leading killer of the elderly. In 1980, 3 of 4 elderly deaths were due to heart disease, cancer, or stroke. These three major causes of death still were responsible for 7 of every 10 elderly deaths in 1991. Among major disease groups, heart disease is the leading cause of death within the elderly population. The total number of deaths due to heart disease in 1991 was about the same as in 1980, at just 600,000.

Heart disease is the leading killer of the elderly. In 1980, 3 of 4 elderly deaths were due to heart disease, cancer, or stroke. These three major causes of death still were responsible for 7 of every 10 elderly deaths in 1991. Among major disease groups, heart disease is the leading cause of death within the elderly population. The total number of deaths due to heart disease in 1991 was about the same as in 1980, at just 600,000.

The need for personal assistance with everyday activities increases with age. At older ages, the proportion requiring personal assistance ranged from 9 percent for those 65 to 69 years old, to 50 percent for those 85 years old and over

The need for personal assistance with everyday activities increases with age. At older ages, the proportion requiring personal assistance ranged from 9 percent for those 65 to 69 years old, to 50 percent for those 85 years old and over

Most of International Companies have Branches in China for Preparing Wireless Healthcare Products and Marketing

5Bingli Jiao @ Peking University

Forrester says “$34 B Market for Healthcare Unbound Technologies by 2015”

80% is Chronic Care

$0

$10

$20

$30

$40

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

ADL/elder Chronic Acute$US

(billions)

Market prospects


ADL/elder --- Activity daily life / elder care Chronic ---- Chronic disease managementAcute ---- post-hospital monitoring According to Forrester Research company

Bingli Jiao @ Peking University

The Forecast of the Investment to the Chinese Health Occupations in 2010,According to CCW Research Company ( 计世资讯 )

The Investments in China

7


Potentials of wireless health care in China

1. Wireless Environmentsa) The Number of mobile users are more than 0.7 billion in China

(reported by Ministry of Industry and Information Technology on Sept. 2, 2009).

b) 3G and wireless LAN networks cover the most area of country and the cities, respectively.

2. Needs of eHealth Service in Chinaa) Information transferring between hospitals:

Only 5% hospitals are ranked as the top level, but they occupy 64% resources, such as experts and equipments.

b) Individual needs

250 persons per doctor per in China (there are 0.278 million hospitals, and 6.169 million doctors including nurses in China, reported by Ministry of Health on Sept. 8, 2009)

Application Cases in China (1)

Ocamar Company



Ocamar claims that they provide total solution for wireless healthcare system, which supports multi service set identifier (SSID). The networks are divided into two; (1) hospital network and (2) non-hospital network. Access to hospital network needs to pass the Wireless Network Controller (WNC) with “SSID=secure”, while access the non-hospital network with “SSID=guest”

Feya Company



Shenzhen New Element Company



Commercial Cases Summary

1. Marketing: still premature

2. International Company: using funding to feed marketing for future, e.g., Microsoft, IBM, Motorola, developing healthcare information management software, devices, system

3. Domestic Companies: getting into marketing for some field tests, most from startup companies, e.g., Ocamar

4. Some of international companies doing business with medical authorities in Hong Kong, e.g., Vital Aire Company has 1000 patients for home health monitoring, and collects data for hospitals


Outline

1. Development of Wireless Healthcare in China




PKU: Wireless ECG SystemECG: electrocardiogramPSDN: packet switched data networkBS: base station


Mo

bile E

CG

Health

care System

ECG Data Collection Terminal

ECG Diagnosis

Data Center and Web Server

Data Mining

On-line Consultation

Emergency Alarm

ECG Monitoring

Data Management

PKU: Wireless ECG System Service

GPRS Wireless Communication

Module

Function ModulesHealthcare Services


PKU: Wireless ECG System


PKU: Terminal Test


PKU: Test Board


PKU: Monitoring Server


Outline

1. Development of Wireless Healthcare in China




HHT Algorithm for ECG Signal Diagnosis


In 1998, Hilbert-Huang Transformation (HHT) method was proposed for analyzing non-stationary and nonlinear data[1]. The method can be divided into two-step consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis.

10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2X(t)

S(t)

Step 0: obtain the original signal


In the EMD step, the algorithm generates Intrinsic Model Functions(IMF), as follows:(1) Connect all maximum points of x(t) by cubic spline line

(2) Connect all minimum points of x(t) by cubic spline line

(3)Calculate an average line, , and, then, generate a proto mode IMF by

repeat (1), (2) for , we calculate an average line, ,and then another proto mode IMF

.

.

till IMF1 is obtained (with a stopping cretiria) by .

)()()( 11 tmtxth

)(1 tm

)(1 th )(11 tm

)()()( 1)1(111 tmhthtc kkK

)()()( 11111 tmthth


Then one calculate IMF2 starting from the residue

.

which will be used as in above processing procedures.

Finally, the input signal, x(t) can be expressed by

)()()( 11 tctxtr

)(tx

)()()( trtctx nii

10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

S(t)

IMF 1; iteration 0

Step 1: Find the local maximum points

EMD Process (4)


10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

S(t)

IMF 1; iteration 0

Step 2: Construct the envelope of local maximum points

EMD Process (5)


10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

S(t)

IMF 1; iteration 0

Step 3: Find the local minimum points

EMD Process (6)


10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

S(t)

IMF 1; iteration 0

Step 4: Construct the envelope of local minimum points

EMD Process (7)


10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

S(t)

IMF 1; iteration 0

Step 5:compute the mean value defined by the local maximum & minimum envelope

EMD Process (8)


10 20 30 40 50 60 70 80 90 100 110 120-2

-1

0

1

2

10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

S(t)

IMF 1; iteration 0

residue

m1

S(t)-m1=h1 h1

Step 6: The difference between the original signal and the mean value is defined as 1st component h1

EMD Process (9)


10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

residue

h1

IMF 1; iteration 1

Sifting Purpose:●remove the carrier waves●make waveforms much more symmetrical

Sifting process must be repeated many times before achieving these purposes!

EMD Process (10)


EMD Process (11)

10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

IMF 1; iteration 1

residue

h1

Step 1: Find the local maximum points

Repeat Iteration 0 !


10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

IMF 1; iteration 1

residue

h1

Step 2: Construct the envelope of local maximum points

EMD Process (12)


10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

IMF 1; iteration 1

residue

h1

Step 3: Find the local minimum points

EMD Process (13)


10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

IMF 1; iteration 1

residue

h1

Step 4: Construct the envelope of local minimum points

EMD Process (14)


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-1.5

-1

-0.5

0

0.5

1

1.5

10 20 30 40 50 60 70 80 90 100 110 120

-1.5

-1

-0.5

0

0.5

1

1.5

IMF 1; iteration 1

residue

h1

h1-m1=h11

Step 5:After the second cycle, we get the new 1st component h11

EMD Process (15)


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-1

-0.5

0

0.5

1

10 20 30 40 50 60 70 80 90 100 110 120

-1

-0.5

0

0.5

1

h1(k-1)

IMF 1; iteration 8

residue

h1(k-1) -m1k=h1k

m1k

SD<0.1

IMF1

EMD Process (16)2

1, -1 1,

0

2

1, -1

0

| ( ) - ( ) |

| ( ) |

T

k k

t

T

k

t

h t h t

SD

h t


10 20 30 40 50 60 70 80 90 100 110 120-1

-0.5

0

0.5

1

10 20 30 40 50 60 70 80 90 100 110 120-1

-0.5

0

0.5

1

h24

IMF 2; iteration 5

residue

m2k

h2(k-1) –m2k=h2k

SD<0.1

IMF2

2

1, -1 1,

0

2

1, -1

0

| ( ) - ( ) |

| ( ) |

T

k k

t

T

k

t

h t h t

SD

h t

EMD Process (17)


10 20 30 40 50 60 70 80 90 100 110 120-0.2

-0.1

0

0.1

0.2

10 20 30 40 50 60 70 80 90 100 110 120-0.2

-0.1

0

0.1

0.2

IMF 3; iteration 12

residue

m3k

h3k

SD<0.1

IMF3

EMD Process (18)

2

1, -1 1,

0

2

1, -1

0

| ( ) - ( ) |

| ( ) |

T

k k

t

T

k

t

h t h t

SD

h t


10 20 30 40 50 60 70 80 90 100 110 120

-0.15-0.1

-0.050

0.050.1

0.15

10 20 30 40 50 60 70 80 90 100 110 120

-0.15

-0.1-0.05

00.05

0.1

0.15

IMF 4; iteration 16

residue

IMF4

EMD Process (19)


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-0.1

-0.05

0

0.05

0.1

10 20 30 40 50 60 70 80 90 100 110 120

-0.1

-0.05

0

0.05

0.1

S(t)

IMF 5; iteration 11

residue

IMF5

EMD Process (20)


imf1

Empirical Mode Decompositionim

f2im

f3im

f4im

f5im

f6

10 20 30 40 50 60 70 80 90 100 110 120

res.

EMD Result (21)


HHT Algorithm for ECG Signal Diagnosis

Additional example

X(t) == IMF ===


HHT Theory Basis

Hilbert Transform( HT )By omitting the residue, one use Hilbert to find instant frequency


jθ(t)

2 2

suppose the actual singnal s(t) after HT

1 ( )H[s(t)] H[s(t)]

then we get the new analytical signal

Z(t)=s(t)+jH[s(t)]=a(t)e

a(t)= s (t)+H [s(t)]

H[s(t)]θ(t)=arctan

s(t)

then instantanous fre

sd

t

：

quency is defined as:

1 dθ(t)f(t)=

2π dt

)()()( trtxts n

HHT Advantage

1. Data analysis whether from physical measurements or numerical modeling, most likely will have one or more of the following problems: (a) the total data span is too short; (b) the data are non-stationary; and (c) the data represent nonlinear processes.

2. Fourier spectrum defines uniform harmonic components globally, and it can’t tell us when the exact frequency component occur. But Hilbert spectrum is very useful in regrouping the decomposed data in the time-frequency space; it is a local and adaptive method of analysis.

3. The HHT algorithm has proved to be a powerful procedure for analyzing non-stationary and nonlinear data. Since its introduction, many applications have been found, which include analyzing acoustic, biological, ocean, earthquake, climate and mechanical vibration data.


EMD Process (1)

The decomposition termination condition of each IMF:2

1, -1 1,0

2

1, -10

| ( ) - ( ) |

0.2 0.3

| ( ) |

T

k kt

T

kt

h t h t

SD between

h t


Hilbert Spectrum Application (22)

1( ) cos( ) 1 512

161

( ) cos( ) 513 101232

S t t t s

S t t t s

1

12

16f

2

12

32f

(a)The calibration data composed of two different cosine functions (b)The Hilbert Spectrum for the calibration data

1

1

32f

2

1

64f


ECG signals


4

5

5

A major issue:

How to track and analyze QRS waves?


Wavelet Transform Algorithm

Hilbert-HuangTransformAlgorithm

ECG Signal Diagnosis

Data sources from MIT-BIH arrhythmia databaseOfficial link:http://www.physionet.org/physiobank/database/mitdb/ Use MATLAB to convert the binary data source to decimal

Typic ECG signals


0 2 4 6 8 10

-1

-0.5

0

0.5

1

1.5

2

2.5

3106.dat

Time /s

Volta

ge /m

v

By EMD processingIMF 1


0 2 4 6 8 10-1

-0.5

0

0.5

1106.dat after denoising and EMD

Time /s

Volta

ge /m

v

Cut off


0 2 4 6 8 100

0.5

1

1.5

2

2.5

3

3.5

4

Time /s

Vol

tage

/mv

Improvement


MIT DATA

Reference/Beat

HHT Detection/Beat

HHT Detection rate

WT Detection/

Beat

WT Detection

rate

100 371 371 100.00% 371 100.00%

101 342 343 99.71% 340 99.42%

102 366 366 100.00% 364 99.45%

103 355 354 99.72% 354 99.72%

107 353 353 100.00% 352 99.72%

109 433 433 100.00% 424 97.92%

111 348 350 99.43% 346 99.43%

112 428 428 100.00% 426 99.53%

113 289 289 100.00% 289 100.00%

115 316 316 100.00% 316 100.00%

116 395 396 99.75% 395 100.00%

117 251 251 100.00% 251 100.00%

118 365 362 99.18% 360 98.63%

conclusion


R wave tracking algorithms: WT and HHT.

1. In the total number of 13851 QRS waves, the correct capture rate: HHT = 99.80%, and WT = 98.76%.

2. In general, HHT is better than WT, but it is more complex than WT.

3. HHT and WT can be used for monitoring heart rate, abnormal biosignal, and emergency cases related to heart beat.


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Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010 Wireless ECG...

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