School of Intelligent Technology and EngineeringChongqing University of Science and Technology Chongqing 401331 China.
Chongqing Vocational Institute of Engineering Chongqing 402260 China.
IEEE J Transl Eng Health Med. 2022 Dec 28;11:116-125. doi: 10.1109/JTEHM.2022.3232791. eCollection 2023.
Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods. Clinical and Translational Impact Statement- The proposed ECG classification method provides an effective solution for ECG interrogation to quickly diagnose the presence of arrhythmia in a patient from the ECG signal. It can increase the efficiency of the interrogating physician by aiding diagnosis.
心电图（ECG）信号通常用于诊断心脏状况。然而，现有的大多数 ECG 诊断方法仅使用时域信息，导致 ECG 信号频域中一些明显的病变信息未被充分利用。因此，我们提出了一种通过卷积神经网络（CNN）融合 ECG 信号的时频域信息的方法。首先，我们采用多尺度小波分解对 ECG 信号进行滤波；然后，利用 R 波定位对每个单独的心搏周期进行分段；然后，通过快速傅里叶变换提取该心搏周期的频域信息。最后，将时间信息与频域信息拼接并输入神经网络进行分类。实验结果表明，与现有方法相比，该方法对 ECG 单导联的识别准确率（99.43%）最高。
临床和转化影响声明- 所提出的 ECG 分类方法为 ECG 询问提供了一种有效的解决方案，可从 ECG 信号中快速诊断患者是否存在心律失常。它可以通过辅助诊断来提高询问医生的效率。
