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ECG Analysis
for the Human Identification
By Tsu-Wang Shen
Department of Biomedical Engineering
University of Wisconsin - Madison
Problem Description

By using the neural network
technologies, my goal is tried to
discover the essential features from the
only “one-lead” resting ECG signals to
identify human. Once the first goal is
achieved, to minimize the number of
features in order to apply in real world
applications.
Project Outline
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Goal: looking for if ECG analysis is a secure, fast,
easily applied, and low-cost method to identify
people
Build an ECG database.
Pre-process ECG and feature extraction
Design a system to identify people by using only
one-lead ECG.
Use the database to train the ANN system.
After the training is done, the system is tested
for the correct classified rate.
People have their own identical heart beat
System Diagram
Feature extraction
ECG database
Template match
Candidates
ECG signals from
sensors
Pre-process
(LP/HP Filtering,
and normal beat
selection)
Pre-screen
Identification
Decision
based neural
network
(DBNN)
Pre-process
Remove the interference:
(ECG signal frequency range: 0.01-250 Hz)
 Baseline wander filter
 Power line interference cancellation
 Highpass filter
 Detect Normal beats
 In this project, the beats is judged by
physicians (MIT/BIH database).
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Template match results
Candidates
ECG feature Extraction
The problem of feature extraction
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The feature extraction plays a key role of this
project.
Normal ECG vs. Abnormal ECG
A person’s ECG signal may not have all the
components, such as P wave and T wave.
The selected features should be less correlation
between each other. That makes the features
have less redundant information.
Heart beats change slightly all the time, so it is
very hard to set observation points.
Decision Based Neural Network
Result of Recognition
MAXNET
1
w11
x1
2
w21 wn1 w12
x2
xn
x1
L
w22 wn2
x2
xn
w13
x1
w23 wn3
x2
xn
x1, x2, … , and xn are features of ECG signals.
DBNN Structure
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Train the system in advance.
This is a supervised neural network.
Reinforced learning is applied for the
correct class neuron.
Anti-reinforced learning is applied for
the misclassified neurons.
Pick the maximum value from all the
class outputs as the final result.
Conclusion
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It is possible to identify people by use only
one-lead ECG.
Pre-processing and pre-screening are
important to limit the possible candidates.
In this project, all ECG signals are in the ideal
condition. (Normal ECG signals, Noise
removed totally.)
Need more ECG database in the future.