College of Artificial Intelligence, North China University of Science and Technology, China.
Department of Computer Science, University of Sheffield, UK.
Comput Math Methods Med. 2021 Apr 30;2021:6649970. doi: 10.1155/2021/6649970. eCollection 2021.
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%.
基于卷积神经网络（CNN）方法，本文提出了一种改进的 ResNet-18 模型，通过适当的模型训练和参数调整，对心电图（ECG）信号进行心跳分类。由于模型的独特残差结构，所使用的 CNN 分层结构可以加深，以实现更好的分类性能。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，该模型具有更高的准确性（96.50%），特别是对于室性异位心跳类别，其灵敏度为 93.83%，精度为 97.44%。
