A novel CNN model with dense connectivity and attention mechanism for arrhythmia classification

Abstract

Cardiac arrhythmia is a common cardiovascular disease that can cause sudden death in severe cases. Electro-cardiography (ECG) is the most well-known and widely applied method for heart diseases detection. Computer-aided diagnosis of ECG can help improve physician efficiency and reduce the rate of misdiagnosis of ECG. In this paper, we propose a method for arrhythmia classification based on the dense convolutional network (DenseNet) and efficient channel attention (ECA). Evaluation experiments were performed using the ECG records from the MIT-BIH database. The accuracy, sensitivity, specificity, and F1 values of 99.69%, 97.55%, 99.81%, and 97.72% were achieved for the six types of heartbeats classification, respectively. The experimental results demonstrate the validity and feasibility of the method, which can be used for ECG screening.