R-peak Detection Using a Hybrid of Gaussian and Threshold Sensitivity

We present an enhanced R-peak detection technique that incorporates both waveform shape recognition and threshold sensitivity enhancement. Waveform shape recognition was achieved with signal processing and Gaussian curve parameterization; threshold sensitivity was accomplished with the famous Pan-Tompkins algorithm. We tested all 48 records in MIT-BIH Arrhythmia Database to validate the proposed method. Our method achieved 97.41% sensitivity against a tolerance window of 10% averaged R-R interval, which improves the current state-of-the-art Pan-Tompkins algorithm by 1%. More importantly, we demonstrate that our approach outperforms the Pan-Tompkins' algorithm in 81% of the records in MIT-BIH Arrhythmia Database.Clinical relevance: High sensitivity R-peak detection is substantial in various cardiovascular disease diagnosis.