School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
Comput Math Methods Med. 2021 Aug 29;2021:6534942. doi: 10.1155/2021/6534942. eCollection 2021.
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.
心电图（ECG）的诊断极其繁重且低效，因此有必要使用计算机辅助诊断 ECG 信号。然而，设计适用于医疗领域的高精度 ECG 算法仍然是一个具有挑战性的问题。本文提出了一种用于分类 ECG 信号的分类方法。首先，使用小波变换对原始数据进行去噪，并使用数据增强技术克服数据集不平衡的问题。其次，提出了一种集成卷积神经网络（CNN）和门控循环单元（GRU）分类器。所提出的网络由卷积层、6 个局部特征提取模块（LFEM）、GRU、密集层和 Softmax 层组成。最后，将处理后的数据输入到 CNN-GRU 网络中，分为五类：非异位搏动、室上性异位搏动、室性异位搏动、融合搏动和未知搏动。该方法使用 MIT-BIH 心律失常数据库进行评估，网络对 5 种 ECG 的平均灵敏度、准确率和 F1 得分为 99.33%、99.61%和 99.42%。所提出方法的评价标准优于其他最先进的方法，该模型可应用于可穿戴设备，实现对 ECG 的高精度监测。
