Attention-Enhanced Convolutional Neural Network for Inter-patient Classification of Premature Ventricular Contraction from ECG

Premature ventricular contraction (PVC) is a common type of cardiac arrhythmia characterized by an early and irregular heartbeat originating from the ventricles.This study presents a dual-path CNN network (WACNN) designed based on wavelet decomposition and attention mechanisms for detecting PVCs from single-lead electrocardiograms (ECGs).All ECG recordings are firstly resampled to 256 Hz.The R-peak positions detected using the Pan-Tompkins method are firstly used to extract a 256 points fixed-duration as training set A, and then used to extract two complete RR intervals as another training set (B).Subsequently, all segments in training set B are resampled to 512 points, resulting in a rescaled training set C. Set B and set C are fed into the model through two separate paths for training.The ratio of the original segment length in B to 512 is considered as a critical infection feature (heart rate feature) for PVC detection in the model.The proposed model is trained on 11 recordings from the MIT-BIH Arrhythmia Database (MITAD) and validated on another 11 recordings, excluding pacemaker data.Testing is conducted on the remaining 22 recordings.On the test dataset, the model achieved a precision of 95.41%, recall of 98.72%, F1 score of 97.04%, and overall accuracy of 99.63%, outperforming most existing methods.