Abstract:

Abstract: To complete the ECG signal feature points extraction and the classification of ST segment, we put forward an algorithm based on the discrete wavelet transform, combined with the f derivative and the SVM(Support Vector Machine). The algorithm can accomplish the signal preprocessing, noise elimination, QRS complex detection and extraction of characteristic value, calculating the average ST segment, curve area and the standard deviation, and the simple classification of ST segment by using the SVM combined with the three sets of data. The matlab simulation results show that the wavelet denoising is effective and has no distortion, and completely extract ST segment feature points. The data are downloaded from the MIT-BIT database, the classification results show that cross-validation average accuracy is 80. 70%,the average accuracy of training is 91. 83%, the average testing accuracy was 74. 28%.

Key words: feature point extraction, classification, wavelet transform, support vector machine(SVM)