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Classification of the Pulse Signals
Based on
Self-Organizing Neural Network
for the
Analysis of the Autonomic Nervous System
Present by: Yu Yuan-Chu
Outline
Autonomic Nerve System(ANS)
The test function
The relationship between heart rate & blood pressure
R-R interval variability
Data acquisition
System Architecture
Experimental Results
Clinical procedures
Spectral analysis result
Classification of pulse signal result
Correlation between ECG and Pulse signal
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ANS Test Function
ANS function
The movement of many internal organs
The tempture, blood pressure, heart rate, endocrine and emotion
Opposing the outside pressure
Elements
sympathetic nerves, parasympathetic nerves, and α,βreceptors
ANS test function
Sympathetic:
•
•
•
BP change in the state of supine and standing
The test of the sustained handgrip
Dark-adapted pupil size after parasympathetic blockade
Parasympathetic :
•
•
•
deep breathing
HR response to standing
Pancreatic polypeptide concentration
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The Relationship among
Heart Rate, Blood Pressure and Baroreflex
Arterial pressure increase
Arterial baroreceptors
Firing
Reflex via medullary
cardiovascular center
Sympathetic
outflow to
heart, arterioles,
veins decrease
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Parasympathetic
outflow to
heart decrease
Blood Pressure(BP):
mainly mechanically induced
Heart Rate Variability (HRV):
under baroreflex control via the
vagus nerves
BP and RR oscillations
occurring at respiratory or
Mayer wave(0.1Hz) frequencies
is mediated by a baroreflex
mechanism
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Y-Axis(sec)
Y-Axis (amplitude)
The R-R interval variability
L1
L2
L4
L3
Plasma Epinephrine increase
Activity of
sympathetic nerves
to heart
increase
X-Axis (sec)
(a)
L1
L2
T
L3
T
Activity of
parasympathetic
nerves to heart
decrease
L4
T
T
1 N
 Ln
N n 1
Activity of the sinoaterial node (SA node)
increase
Heart rate increase
i.e.
R-R interval variability decrease
X-Axis(sec)
•HRV derived from the ECG signals
•The relationship between
R-R interval variability
and autonomic nerves
•Sympathetic and Parasympathetic activities regions in
PSD
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Data Acquisition
Hardware
Finapres: Finger arterial pressure utilizes the principle of arterial wall unloading
ECG(12 leads): 12 different potential differences from the body surface
SCXI-1140: signal conditioning module, 8-channel differential amplifier
AT-MIO-16F-5: DAQ board, 200 kHz, its resolution is 12 bits
Software(LabVIEW):
Data acquisition system
Data analysis systemp
• PSD, 3D PSD, baroreflex analysis and ART2 analysis system
(signal conditioning)
SCXI-1140
(Multi-functional I/O card)
AT-MIO-16F-5
CARD
Finapres
RS 232
PC AT-586
ECG
•Hardware Architecture
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•electrodes connected in an leadΠconfiguration
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System Configuration
Main Purpose:
Signal validation between ECG & BP
• Hamming windows, Autoregression, PSD
Improve the analytic results
• Preprocessor, Adaptive Resonance Theorem of Version 2(ART2)
Non-invasive
data acqisition
ECG
Finapres
Preprocessor
Derived
R-R interval
variability
Hamming
Windows
and
Autogression
Derived
R-R interval
variability
Hamming
Windows
and
Autogression
ART2
recognition
sytstem
Signal validation
between
ECG&Finapres
Power Spectral
Density Analysis
Source arterial
pressure
variability data
Recognition pattern
(LTM)
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•R-R intervals from ECG and Pulse signals
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Power Spectral Density Analysis
•Hamming Window(Time Domain)
•Autoregressive spectrum: Linear Predict Coefficients(LPC)
Attenuate the spectral leakage
Describe the signal “parsimoniously”
by a small number of coefficients
•Hamming Window(Freq. Domain)
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ART2
Blood Pressure Parameters
Q-U: the pulse transmission time
V-D: the diastolic shut time
U-P: the systolic ejection time
U-U’: the one cardiac time
P-V: the slow time of ejection
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Self-organizes stable pattern recognition
codes in real-time
Continuous speech recognition and
synthesis, pattern recognition, classification
of noisy data, nonlinear feature detection
Not affected by factors: human fatigue,
emotional states, and habituation
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Clinical Procedures
Six young controlled subjects(23-26 years old) without any clinically
evident disease were examined
Two standard autonomic tests were undertaken:
Rest- All subjects were asked to lie quietly for 5 minutes with spontaneous
breath
Tilting- recorded over 5 minutes following passive tilting to 75 degree
position by the electrically rotating table
Studies were performed between 2:00 PM and 5:00 PM
Temperture
The environment tempture was controlled on 24.1 ° C
Body temperatures of all subjects were at the range of 35 ° C to 38 ° C
•The validation testing
between the ECG and
arterial pulse variability is
97.81＋1.38%
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(a) ECG
(b) Pluse
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Spectral Analysis
(a) ECG/Rest
(b) Pulse/Rest
(a) ECG/Tilt
(a) Pulse/Tilt
Indices
LF
HF
T-test value
Index
LF
HF
Area
︽
﹀
p ＝ 0.001
Area
0.91
0.95
Mean
︽
﹀
p ＝ 0.002
Mean
0.95
0.98
Max
Max
0.57
0.74
︽
﹀
p < 0.001
SD
0.95
0.88
SD
︽
﹀
p ＝ 0.002
•ECG in the state of tilting up, T-test value between LF
and HF
︽: increase significantly,
﹀: decrease not significantly
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•Correlation between ECG and Finapres,
• Index of Area is best for the PSD in the
HRV tests
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Classification of pulse signal result
•48.8%, sitting up 60 degree
•27.8%, deep breathing
•Deep Breathing(Original)
•Deep Breathing(after ART2)
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•Status Distribute Plot
•Sitting up 60 degree(Original)
•Sitting up 60 degree(after ART2)
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Correlation between ECG and Pulse signal
Subject 1
Date：03/19/97
Time：09:55 PM
State : Tilting up
Body
temperature=36.5
Environment
temperature=24.1,
Man, Birthday :
65.5.15, Years :
22
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