An efficient threshold method for detecting R-peaks in electrocardiogram

Automated R-peak detection in electrocardiogram (ECG) signals is essential to heart rhythm analysis, with applications in heart rate monitoring, heart rate variability (HRV) assessment, arrhythmia diagnosis ets. Its accuracy, however, is highly sensitive to noise and artifacts present in the ECG recording. The study proposes a method and its software implementation for R-peak detection, based on signal smoothing followed by differentiation and the application of a thresholding approach. The method is designed for use in resource-constrained environments, such as portable and embedded monitoring systems. The objective of the study is to develop a computationally efficient and accurate method for the automatic detection of R-peaks in ECG signals, tailored to a personalized patient approach. The primary focus is on robustness to noise and QRS complex morphology, as well as the algorithm’s ability to operate under limited computational resources and in real-time conditions. The proposed method relies on staged ECG signal processing. To validate the approach and compare its effectiveness, existing studies in the field were analysed. The subject of the study involves processing ECG signals and addressing challenges in R-peak detection in ECGs recorded via Holter monitors by constructing a smoothed continuous signal based on discrete data obtained during digitization. The discrete nature of the initial signal complicates differentiation and the precise identification of characteristic points, specifically R-peaks, which play a crucial role in diagnosing cardiac conditions. The scientific novelty of this investigation lies in the use of a second-order piecewise polynomial approximation to represent the ECG signal. This approach enables noise reduction in the signal and represents the discrete signal as a continuous function together with its first derivative, thereby permitting the analytical computation of its derivative. Results involve the detection of R-peaks by analysing the derivative of the smoothed signal: regions with sharp changes characteristic of the QRS complex were identified, and an iterative smoothing scheme was developed, with the number of iterations determined by a proposed stopping criterion. The proposed method was implemented in software and tested on data from the open-access MIT-BIH Arrhythmia Database, which includes over 60 recordings and more than 100,000 annotated R-peaks. The results were compared with studies by other authors using the  metric, based on standard precision and sensitivity metrics. Conclusions: The study proposes an effective and adaptive approach to ECG signal processing, ensuring reliable R-peak detection under conditions of significant noise, baseline drift, and physiological variability across patients. The obtained results demonstrated high performance metrics: up to 99.5% (average above 99.1%),  consistently exceeding 99%, and in some recordings reaching 100%. Thus, the proposed approach is competitive, demonstrating high accuracy in detecting R-peaks in cases of arrhythmias, peak inversions, non-standard QRS complex morphology, and other challenging signal conditions. The study’s results can be applied in ECG analysis practice, particularly in the development of automated diagnostic systems or signal preprocessing before the application of classification methods.