Why the study?
High sensitivity R-peak detection is substantial in various cardiovascular disease diagnosis.
Does a hybrid R-peak detection technique using Gaussian curve parameterization and Pan-Tompkins algorithm improve detection sensitivity compared to the Pan-Tompkins algorithm alone in ECG records?
Does a hybrid R-peak detection technique using Gaussian curve parameterization and Pan-Tompkins algorithm improve detection sensitivity compared to the Pan-Tompkins algorithm alone in ECG records?
A hybrid R-peak detection algorithm combining Gaussian curve parameterization and the Pan-Tompkins algorithm slightly improves sensitivity over the standard Pan-Tompkins algorithm.
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.
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Hsu et al. (2020) studied this question.
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