Quantum Convolutional Neural Networks for Cardiac Arrhythmia Detection from ECG Signals

Electrocardiography (ECG) is a primary diagnostic tool for identifying cardiac arrhythmias, a critical indicator of heart disease. While ECG signals contain vital information about the patient’s health conditions, their high-resolution waveform nature poses significant challenges in effective feature extraction. Recent advancements in quantum computing provide a transformative opportunity to accelerate research in data-driven predictive modeling for healthcare. This paper investigates a variety of Quantum Convolutional Neural Network (QCNN) architectures for ECG signal classification. By leveraging optimized convolutional structures, adaptable pooling layers, and advanced encoding techniques, our QCNN model is designed to preserve essential ECG characteristics while addressing challenges in dimensionality reduction. Experimental results demonstrate that our approach holds significant potential for enhanced arrhythmia classification. This innovative QCNN model has the potential to advance ECG-based diagnostics for heart disease, offering valuable support for clinical decision-making in cardiac care.