Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

中文翻译：

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使用卷积神经网络通过心电信号分类研究心律失常

心血管疾病 (CVD) 是当今死亡的主要原因。目前识别疾病的方法是分析心电图（ECG），这是一种记录心脏活动的医学监测技术。遗憾的是，找专家分析大量的心电数据，耗费了太多的医疗资源。因此，基于机器学习识别心电特征的方法逐渐流行起来。然而，这些典型方法存在一些缺点，需要人工识别特征，模型复杂，训练时间长。本文提出了一种鲁棒且高效的 12 层深度一维卷积神经网络，用于对 MIT-BIH 心律失常数据库中的五个微类心跳类型进行分类。心跳特征的五种类型被分类，实验中采用小波自适应阈值去噪方法。与BP神经网络、随机森林等CNN网络相比，结果表明本文提出的模型在准确率、灵敏度、鲁棒性和抗噪能力方面具有更好的性能。其准确的分类有效地节省了医疗资源，对临床实践产生了积极的影响。