Robust R-Peak Detection in Noisy ECGs via Strip-Attention YOLO with Multilead Fusion

The R-peak in electrocardiogram (ECG) signals is a critical physiological marker for the diagnosis of cardiovascular diseases. Although various R-peak detection methods have been proposed, their performance is often hindered by noise, especially in dynamic ECG monitoring. Furthermore, the potential of harnessing complementary information from 12-lead ECG signals has not been fully exploited. To address these challenges, this study conceptualized 12-lead ECG data as two-dimensional images and employed YOLOv5 as the model's backbone for R-peak detection, effectively transforming a signal segmentation task into an object detection task in images. Specifically, considering the characteristics of consistent R-peak positions across different leads, we proposed a strip attention mechanism to treat horizontal or vertical strips as tokens for computing inter- and intra-strip attention, enhancing the model's ability to capture R-peak positional information and likelihood. Additionally, a one-dimensional Manhattan distance-based NMS algorithm was used to minimize redundant detection frames, thereby enhancing model performance. The proposed model was rigorously evaluated on two publicly available datasets, INCART and LUDB, under varying noise conditions. On the INCART dataset, the model achieved F1 scores of 99.97%, 99.86%, 99.63%, and 98.00% at noise levels of Original, SNR = 10 dB, SNR = 5 dB, and SNR = 0 dB, respectively. Similarly, on the LUDB dataset, the F1 scores were 99.89%, 100%, 100%, and 99.86% for the corresponding noise levels. Extensive testing across multiple datasets and noise scenarios demonstrated that the proposed model outperformed existing state-of-the-art methods in terms of accuracy, noise robustness, and generalization capability.