Convolutional Block Attention BiLSTM for Arrhythmia Detection

Cardiovascular diseases represent a significant cause of mortality, with millions of electrocardiograms being recorded each year. Therefore, methods of auto- mated diagnosis for electrocardiograms are of particular interest. Since electro- cardiograms have recognizable features and are time-dependent, we propose a model using convolutional layers, convolutional attention, and long short-term memory units. The model is trained and validated on the MIT-BIH Arrhythmia database, and achieves an accuracy of 99.10%, a precision of 99.09%, a specificity of 99.64%, a sensitivity of 95.90%, and an F1 score of 97.47%.