关键词: 心电信号, 特征提取, 深度稀疏自编码器, 适应性矩阵估计, 支持向量机

Abstract:

In view of the difficulties in feature extraction of complex waveforms in intelligent analysis model of ECG signals, the loss of source signal features caused by artificial design features, and the shortage of label samples, an ECG feature extraction method based on Deep Sparse Auto-Encoders（DSAEs） is proposed. In the greedy layer-by-layer training of DSAEs, this method uses Adaptive moment estimation（Adam） to optimize the network weights to obtain the optimal parameter combination, and simultaneously extracts the output of the higher-level hidden layer as the ECG’s highly abstract low-dimensional features. Finally, Support Vector Machines（SVM） is used to construct a classification model to complete the ECG feature classification. Using the ECG data of MIT-BIH arrhythmia database for simulation experiments, the results show that the ECG feature extraction method proposed in this paper can effectively extract features hierarchically and improve the accuracy of classification recognition.

Key words: ECG signal, feature extraction, Deep Sparse Auto-Encoders（DSAEs）, Adaptive moment estimation（Adam）, Support Vector Machines（SVM）