基于连续小波变换和高阶统计量的心律失常识别算法-《中国医学物理学杂志》

文章信息/Info

Title:: Arrhythmia identification algorithm based on continuous wavelet transform and higher-order statistics

作者:: 李刚1; 高广帅1; 张珍珍2; 巴任伟1; 李春雷1; 刘洲峰1; 1.中原工学院电子信息学院，河南郑州 450007； 2.郑州人民医院郑东院区门诊部，河南郑州 450014

Author(s):: LI Gang1; GAO Guangshuai1; ZHANG Zhenzhen2; BA Renwei1; LI Chunlei1; LIU Zhoufeng1; 1. School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China 2.Outpatient Department, East District Branch of Peoples Hospital of Zhengzhou, Zhengzhou 450014, China

Keywords:: Keywords: arrhythmia identification continuous wavelet transform higher-order statistics long short-term memory RR interval

摘要:: 针对可变持续时间心电图（ECG）数据信号的非平稳性和时序性问题，提出一种基于连续小波变换（CWT）和高阶统计量（HOS）的心律失常识别算法。首先，针对可变持续时间ECG数据中每个样本的数据点数量不同，采用RR间期插值法预处理数据，并通过CWT将信号分解为不同的时频分量，从而使网络能够更好地提取心电信号中的时间和频率特征。其次，针对时序信息利用不充分的问题，提出基于HOS和长短期记忆网络的时序挖掘模块，以捕捉和学习ECG信号中的长期依赖关系，从而有助于识别和理解特定的心律失常类别。通过在公开的ECG数据集MIT-BIN上进行的大量实验，验证所提方法的有效性和优越性。

Abstract:: Abstract: Aiming at the non-stationarity and temporal characteristics of variable-length electrocardiogram (ECG) signals, an arrhythmia identification algorithm is proposed based on continuous wavelet transform and higher-order statistics. Considering the varying number of data points for each sample in variable-length ECG signals, the RR interval interpolation method is employed for data preprocessing, and the signal is decomposed into different time-frequency components using continuous wavelet transform, which enables the network to better extract both temporal and frequency features from the ECG signals. Regarding the issue of insufficient utilization of temporal information, a temporal mining module is introduced based on higher-order statistics and long short-term memory network to capture and learn long-term dependencies in the ECG signals, thereby facilitating the identification and understanding of specific arrhythmia categories. Extensive experiments conducted on the publicly available MIT-BIH ECG database validate the effectiveness and superiority of the proposed method.