ECG Analysis for the Human Identification

By Tsu-Wang Shen

Department of Biomedical EngineeringUniversity 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 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

ECG database

ECG signals fromsensors Pre-process

(LP/HP Filtering,and normal beat

selection)

Pre-screen

Template match

Decisionbased neural

network(DBNN)

Candidates

Feature extraction

Identification

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).

Template match resultsCandidates

ECG feature Extraction

The problem of feature extraction

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

MAXNET

1

Result of Recognition

x1 x2 xn

w 11 w 21 w n1

2

x2 xn

w 22 w n2

x1

w 12

L

x1 x2

w 13 w 23

xn

w n3

x1, x2, … , and xn are features of ECG signals.

DBNN Structure 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 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.