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.