![]() Arrhythmia is very common and can lead to cardiac arrest or even death. CVDs are the number one cause of death worldwide. With the acceleration of the economy, the incidence and mortality of cardiovascular diseases (CVDs) have continued to increase in recent years, and the trend is becoming more and more obvious, especially for young people. 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%. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. ![]() ![]() ![]() 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. ![]()
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