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ECG Analysis for the Human Identification

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

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ECG Analysis for the Human Identification

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  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: 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).

  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.

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