Real time ECG R-peak detection by extremum sampling

In this paper, we propose and validate a novel self-adaptive real-time event-driven ECG R-peak detection algorithm based on extremum sampling. The criteria for algorithm development were low complexity and energy efficiency facilitating wearable and mobile applications. The proposed algorithm consists of three major steps: event-driven identification of the QRS window, detection of ECG signal extremum, and awaiting the end of the current QRS complex. The threshold adaptation mechanism reduces the algorithm sensitivity to varying ECG amplitude range. The algorithm performance has been examined on MIT-BIH Arrhythmia Database recordings achieving the following results: sensitivity and positive prediction rate equal respectively to 99.55% and 99.88% which effected in total false detection rate equal to 0.58%. These results significantly outweigh the others reported in the area of low power R-peak detection algorithms.