Hybrid Deep Learning model for ECG-based Arrhythmia Detection

AI technologies can assist doctors and paramedic staff in identifying cardiovascular diseases such as arrhythmia. Over the last decade, an increase in wearable ECG devices has surfaced in the market which has generated huge data sets that can potentially be used for the early detection and classification of arrhythmia. In this work, a hybrid model is proposed for ECG signal analysis to classify SVEB and VEB arrhythmia classes. The proposed model is evaluated on the MIT-BIH arrhythmia database and compared with state-of-the-art approaches. The proposed model outperformed the existing approaches for SVEB and VEB arrhythmia.