基于变权重奇异谱分析的心律不齐识别方法.

Many existing arrhythmia researches focus on the separation of different frequency characteristic components in the ECG signal. However, the contribution of different subsequences to the final target decision-making is lack of research and analysis. In order to enhance the impact of high-contribution subsequences on the classifier, a recognition method combining variable weight singular spectrum analysis and deep learning is proposed. Multiple subsequences are obtained through singular spectrum analysis. The Gini coefficient under the random forest is calculated by the singular value of each sequence and used as the weight. The sequence samples with variable weights are used to train the neural network model, which can mine useful information more efficiently and further improve the recognition accuracy. The accuracy rate of final arrhythmia recognition is 98. 35%, and Macro-Fl is 97. 95%. Compared with the traditional fixed weight, the proposed recognition method of variable weight has a significant improvement in various performance indicators. [ABSTRACT FROM AUTHOR]