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Classifying Normal and Abnormal Heartbeats From a Noisy ECG

Classifying Normal and Abnormal Heartbeats From a Noisy ECG. Eric Peterson ECE 539. Outline. Filtering – Some Basics Beat Detection – Failed MLP Beat Classification – Works…Sometimes SVM Beat Classification – Similar Results Conclusion – More Pre-Processing Needed. Filtering – High-Pass.

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Classifying Normal and Abnormal Heartbeats From a Noisy ECG

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  1. Classifying Normal and Abnormal Heartbeats From a Noisy ECG Eric Peterson ECE 539

  2. Outline • Filtering – Some Basics • Beat Detection – Failed • MLP Beat Classification – Works…Sometimes • SVM Beat Classification – Similar Results • Conclusion – More Pre-Processing Needed

  3. Filtering – High-Pass

  4. Filtering – Band-Pass

  5. Beat Detection • Supplied the Filtered Signal • Overwhelmed the ANN • SNR does not matter • FAILURE!!! • Pan-Tompkins • Overwhelmed again • May not actually be linearly seperable • Modifications requred

  6. MLP Beat Classification • Used annotations to focus on beats only • Annotations of either normal or abnormal beats • Attempted many parameter variations • Best classification rate: 95.8824% • Confusion Matrix: 159 2 8 4 • Results were dominated by the normal beats • Failed with a SNR<24dB

  7. MLP Beat Classification

  8. SVM Beat Classification • RBF kernel did not work • Similar results to MLP • Still seems dominated by the normal beats • Failed at <24dB SNR

  9. SVM Beat Classification

  10. Conclusion • More Pre-Processing is needed!!! • Possibility of better filtering? • Further analysis of the signal • Feed the neural nets with important values • Templates were used in many previous papers • Not ideal for many types of abnormal beats

  11. Questions? http://www.metamemes.com

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