ECG Heartbeat Classification Based on an Improved ResNet-18 Model

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%.

Automated summary

This article proposes an improved ResNet-18 model based on a convolutional neural network (CNN) approach for heartbeat classification of electrocardiogram (ECG) signals, achieving high accuracy and particularly high sensitivity and precision for ventricular ectopic heartbeat class.