Multi-Class Cardiovascular Disease Diagnosis using Temporal Convolutional BiLSTM Model

Automating CVD diagnosis is critical for effective cardiac monitoring; however, challenges arise from subtle structural variations in ECG data under different heart conditions. Current methodologies mostly emphasise extracting lead-specific morphological characteristics from multi-lead ECG data, often neglecting the spatial relationships across leads and temporal dependencies of ECG signals. This paper presents a novel temporal convolutional bidirectional LSTM (CBiLSTM) method for the classification of five cardiac ailments. The model includes BiLSTM for capturing past and future bidirectional temporal dependencies and convolutional layers to collect spatial information. The model was trained using the PTB-XL dataset, yielding an overall accuracy and F1-score of 75.8% and 75%, respectively. The findings demonstrate that the proposed strategy outperforms current methods in the classification of CVD and enhances the early detection of cardiac abnormalities.