1. ECG Analysis for the Human Identification
By Tsu-Wang Shen
Department of Biomedical Engineering
University of Wisconsin - Madison

2. 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.

3. 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.

4. People have their own identical heart beat

5. System Diagram

6. Pre-process Remove the interference:
(ECG signal frequency range: 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).

7. Template match results
Candidates

8. ECG feature Extraction

9. 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.

10. Decision Based Neural Network
x1, x2, … , and xn are features of ECG signals.

11. 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.

12. 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.