The robust unsupervised modelless AI for R Peaks detection in ECG

This paper represents a novel, model-free method for R-peak detection in electrocardiogram (ECG) signals, leveraging an adaptive outlier scoring technique called the mass-ratio-variance based outlier factor (MOF). Unlike traditional pretrained models, or fixed-threshold methods, this approach dynamically adjusts to incoming signal patterns, enabling robust, and accurate detection of R peaks across diverse, and previously unseen ECG data. The core of the method employs a rapid, overlapping window-based strategy that computes MOF scores to distinguish R-peaks as pronounced outliers within the ECG waveform. The algorithm incorporates Z-score normalization, iterative candidate identification, dynamic refractory period enforcement, and physiological post-processing filters to minimize false positives, and maintain beat-to-beat consistency. Experimental evaluation on the widely used MIT-BIH Arrythmia Database demonstrates that the proposed MOW-ECG algorithm achieves excellent performance with a sensitivity of 96.71%, and a positive predictive value of 99.39%, outperforming several state-of-the-art methods in accuracy, and robustness. The adaptive, and model-free nature of the algorithm make it especially suitable for real-time applications, and continuous monitoring scenarios where pretraining is impractical or infeasible. This work contributes a computationally efficient, and interpretable solution for automated cardiac even detection, highlighting its potential integration into clinical ECG systems for enhanced patient care, and diagnostic reliability.