Toward robust AI-based ECG R-peak detection for real-time physiological monitoring in operational environments.

Reliable estimation of heart rate variability (HRV) from electrocardiography (ECG) depends on accurate detection of R-peaks, a task that becomes challenging in operational environments characterized by noise, motion artifacts, and monitoring-grade sensor configurations. While classical detection algorithms perform well under controlled conditions, their robustness is often limited in realistic settings. This study investigates whether artificial intelligence (AI)-based approaches can achieve robust R-peak detection in noisy operational ECG recordings, providing a reliable foundation for downstream HRV analysis. A physiologically constrained annotation framework was developed to generate temporally consistent reference annotations for evaluation of operational ECG recordings, while model training was performed exclusively using the MIT-BIH Arrhythmia Database with noise augmentation from the MIT-BIH Noise Stress Test Database. Two AI-based approaches were evaluated: a bidirectional Long Short-Term Memory (LSTM) network and a Transformer-based time-series foundation model (Mantis-8M) with task-specific output layers. Model performance was evaluated using both the MIT-BIH Arrhythmia Database and a real-world dataset comprising monitoring-grade ECG recordings from 30 participants acquired under operational conditions. The strongest-performing AI-based models achieved near-perfect benchmark performance (F1 ≈ 0.99) and maintained high accuracy under noisy conditions. On operational data, the LSTM model achieved the strongest overall performance (F1 = 0.96), substantially outperforming classical Pan-Tompkins implementations and alternative AI architectures. Additional validation analyses demonstrated consistent performance across participants, low RR interval error, and minimal variability across repeated training runs, supporting the robustness and reproducibility of the proposed approach. The results demonstrate that AI-based R-peak detection preserves physiologically consistent beat-to-beat timing in noisy, monitoring-grade ECG recordings, providing a robust foundation for downstream HRV analysis. The study focuses on robustness in operational, non-clinical environments rather than diagnostic ECG interpretation. These findings support AI-based ECG analysis, trained on physiologically valid benchmark data, as a robust and reproducible foundation for real-time physiological monitoring in complex operational environments.