Deep Learning End-to-End Approach for Precise QRS Complex Delineation Using Temporal Region-Based Convolutional Neural Networks

Advancements in clinical diagnosis of heart disease are driven by technological innovations and signal processing developments.ECG segmentation, particularly QRS complex detection, plays a crucial role in cardiac cycle analysis.Deep learning has revolutionized automated ECG analysis, enhancing diagnostic accuracy significantly.This paper proposes an optimized Region Proposal Network (RPN) architecture for QRS complex detection, specifically designed for 1D signals.Leveraging a vast ECG dataset, extensive data augmentation, feature extraction, and RPN-based QRS detection were employed.Our method achieved a QRS detection F 1 score of up to 99 %, highlighting its high reliability.Furthermore, QRS complex delineation exhibited deviations typically within 8 ms.The results were verified using a publicly available Lobachevsky University Electrocardiography Database (LUDB), with validation yielding F 1 score of 91.59 % for QRS detection and RMSE of 10.38 ms for QRS complex delineation.The optimized RPN architecture for QRS complex detection presents a promising solution for efficient ECG analysis.