Sudden cardiac death is the result of abnormal heart conditions. Therefore, early detection of such abnormal conditions is vital to identify heart problems. Hence, in this paper, we aim to present a new computer-aided diagnosis (CAD) method based on time-frequency analysis of electrocardiogram (ECG) signals and deep neural networks for arrhythmia detection. Time-frequency transforms have the capability of providing spectral information at different times, which is very useful for analyzing non-stationary signals. On the other side, entropy is an attractive measurement from ECG signals which can distinguish different types of them. In this paper, time-frequency spectral entropy is proposed to extract the efficient features from ECG signals. All computed entropies cannot provide separability among different classes, two-directional two-dimensional principal component analysis (2D²PCA) can be used to reduce the dimension of the extracted features. Finally, the convolutional neural network (CNN) classifies the time-frequency features to diagnose the ECG beat signals and detect arrhythmias. The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

心脏骤然死亡是心脏状况异常的结果。因此，尽早发现这种异常状况对于识别心脏问题至关重要。因此，在本文中，我们旨在提出一种基于心电图（ECG）信号的时频分析和深度神经网络进行心律失常检测的新的计算机辅助诊断（CAD）方法。时频变换具有在不同时间提供频谱信息的能力，这对于分析非平稳信号非常有用。另一方面，从ECG信号中可以看出熵是一种吸引人的测量方法，可以区分不同类型的信号。本文提出了时频频谱熵来提取心电信号的有效特征。所有计算出的熵不能提供不同类别之间的可分离性，² PCA）可用于减小提取特征的尺寸。最后，卷积神经网络（CNN）对时频特征进行分类，以诊断ECG搏动信号并检测心律不齐。结果表明，频谱熵可以在不同心电信号搏动之间提供良好的分离，并且所提出的方法优于最近介绍的用于分析心电信号的方法。