A ConvLSTM Framework for Conduction Disturbance Classification and Risk Identification

Conduction disturbances (CD) are cardiac abnormalities characterised by disruptions in the cardiac electrical conduction system. Accurately identifying the risk score (RS) for CD in electrocardiogram (ECG) signals is essential for early diagnosis and effective treatment of cardiac disease. The RS offers a quantitative assessment of diagnostic certainty by analysing cardiac axis deviation (CAD) and ECG morphological alterations. Current methods mostly focus on the diagnosis of cardiovascular diseases (CVDs), frequently overlooking the intricate ECG features vital for thorough risk assessment. This paper proposes a novel application for CD classification and associated risk identification using a hybrid Convolutional Long Short-Term Memory (CLSTM) network. The model integrates convolutional layers with various filter sizes to capture multiscale spatial features and LSTM layers to address temporal dependencies. The network concludes with a fully connected layer utilising softmax activation to classify RS over four levels and CD into seven categories. The model was trained using the PTB-XL dataset, achieving an overall accuracy and F1-score of 85.8% and 81% for CD classification and 99.6% and 96% for RS prediction, respectively. By demonstrating the model's proficiency in capturing intricate ECG features, these findings highlight its potential to enhance cardiac diagnosis.