ECG Beat Classification Using Cross-Wavelet And LVQ

Abstract

This paper describes an automatic classification system based on combination of cross-wavelet and Learning Vector Quantization (LVQ) for the purpose of automatic heartbeat detection. The feature extractor is based on cross-wavelet approach, using the time frequency information. The ANN classifier uses a Learning Vector Quantization (LVQ) method which classifies the ECG beats into two categories: normal beats and abnormal beats. The ECG (electrocardiogram) signals in the MIT-BIH arrhythmia database are adopted as reference data. Total 98530 heart beats are used for testing the above classifier. The total classification accuracy (TCA) was about 91.66%.