International Conference on Technological Intelligence and Business Strategies

Authors - Phat Ly Tan, My Nguyen Kieu, Phung Nguyen Thi Kim
Abstract - This paper presents an automated approach for cardiac arrhythmia detection using ECG signals from the CPSC2018 database. The proposed pipeline includes band-pass filtering, normalization, and segmentation of raw ECG recordings, conversion of ECG segments into 2D grayscale images, and multi-label arrhythmia classification using CNN based on a DenseNet architecture. According to the official CPSC2018 labeling scheme, ECG segments are categorized into multiple clinically relevant rhythm types, including normal sinus rhythm and major arrhythmias such as first-degree atrioventricular block, atrial fibrillation, right bundle branch block, left bundle branch block, ventricular ectopic beat, premature atrial contraction, ST-segment elevation, and ST-segment depression. The DenseNet-based architecture combines an oversampling training strategy to alleviate class imbalance. Experimental results on the CPSC2018 database demonstrate the effectiveness of the proposed image-based ECG classification approach, highlighting its potential to assist clinicians in ECG interpretation and early diagnosis of cardiac disorders.