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

Richard Redina1, Jakub Hejc2, Fabian Theurl3, Tomas Novotny4, Irena Andrsova4, Katerina Hnatkova5, Zdenek Starek6, Marina Filipenska7, Marek Malik5, Axel Bauer3
1Brno University of Technology; International Clinic Research Centre, St. Anna's University Hospital, Brno, 2International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic; Department of Pediatric, Children's Hospital, The University Hospital Brno, Brno, Czech Republic, 3University Clinic of Internal Medicine III, Medical University of Innsbruck, 4Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, 5Imperial College, 6Department of Internal Medicine, Cardioangiology, St. Anne's University Hospital in Brno, 7Brno University of Technology

Abstract

Advancements in clinical diagnosis of heart disease are driven by technological innovations and signal processing developments. ECG segmentation, particularly QRS com- plex detection, plays a crucial role in cardiac cycle anal- ysis. 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 extrac- tion, and RPN-based QRS detection were employed. Our method achieved a QRS detection F1 score of up to 99 %, highlighting its high reliability. Further- more, QRS complex delineation exhibited deviations typ- ically within 8 ms. The results were verified using a pub- licly available Lobachevsky University Electrocardiogra- phy Database (LUDB), with validation yielding F1 score of 91.59 % for qrs detection and RMSE of 10.38 ms for QRS complex delineation. The optimized RPN architecture for QRS complex detec- tion presents a promising solution for efficient ECG anal- ysis.