Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

中文翻译：

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基于改进的 ResNet-18 模型的 ECG 心跳分类

本文基于卷积神经网络 (CNN) 方法，通过适当的模型训练和参数调整，提出了一种改进的 ResNet-18 模型，用于心电图 (ECG) 信号的心跳分类。由于模型独特的残差结构，可以加深利用的CNN分层结构，以达到更好的分类性能。将所提出的模型应用于 MIT-BIH 心律失常数据库的结果表明，与其他最先进的分类模型相比，该模型实现了更高的准确度（96.50%），而专门针对室性异位心跳类别，其灵敏度为93.83%，精度为 97.44%。