prof. dr. bingli jiao wireless communications lab peking university oct. 13, 2010 wireless ecg...
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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.
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1. Necessarity of E-healthcare
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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
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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
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ADL/elder --- Activity daily life / elder care Chronic ---- Chronic disease managementAcute ---- post-hospital monitoring According to Forrester Research company
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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
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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
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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
Application Cases in China (2)
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Shenzhen New Element Company
Application Cases in China (3)
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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
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Outline
1. Development of Wireless Healthcare in China
2. Wireless ECG System in PKU
3. HHT Algorithm for ECG Signal Diagnosis
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PKU: Wireless ECG SystemECG: electrocardiogramPSDN: packet switched data networkBS: base station
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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
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PKU: Wireless ECG System
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PKU: Terminal Test
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PKU: Test Board
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PKU: Monitoring Server
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Outline
1. Development of Wireless Healthcare in China
2. Wireless ECG System in PKU
3. HHT Algorithm for ECG Signal Diagnosis
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HHT Algorithm for ECG Signal Diagnosis
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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
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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
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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
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-1
0
1
2
S(t)
IMF 1; iteration 0
Step 1: Find the local maximum points
EMD Process (4)
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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)
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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)
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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)
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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)
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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)
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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)
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EMD Process (11)
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-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 !
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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)
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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)
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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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10 20 30 40 50 60 70 80 90 100 110 120
-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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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
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
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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)
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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
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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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10 20 30 40 50 60 70 80 90 100 110 120
-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)
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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)
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HHT Algorithm for ECG Signal Diagnosis
Additional example
X(t) == IMF ===
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HHT Theory Basis
Hilbert Transform( HT )By omitting the residue, one use Hilbert to find instant frequency
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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.
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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
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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
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ECG signals
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4
5
5
A major issue:
How to track and analyze QRS waves?
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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
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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
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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
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0 2 4 6 8 100
0.5
1
1.5
2
2.5
3
3.5
4
Time /s
Vol
tage
/mv
Improvement
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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
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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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