Why the study?
Correct recognition of R-peaks is extremely important in ECG applications for detecting abnormalities such as arrhythmia and ventricular hypertrophy.
Does the SQRS method improve R-peak detection sensitivity in ECG signals?
Does the SQRS method improve R-peak detection sensitivity in ECG signals?
The proposed SQRS method using squared window variance transform achieves 99.6% sensitivity for R-peak detection on the MIT-BIH arrhythmia database.
May aid arrhythmia detection in noisy ECGs; leaves open prospective validation before clinical adoption.
In ECG applications, the correct recognition of R-peaks is extremely important for detecting abnormalities, such as arrhythmia and ventricular hypertrophy. In this work, a novel ECG enhancement and R-peak detection method based on window variability is presented, and abbreviated as SQRS. Firstly, the ECG signal corrupted by various high or low-frequency noises is denoised by moving-average filtering. Secondly, the window variance transform technique is used to enhance the QRS complex and suppress the other components in the ECG, such as P/T waves and noise. Finally, the signal, converted by window variance transform, is applied to generate the R-peaks candidates, and the decision rules, including amplitude and kurtosis adaptive thresholds, are applied to determine the R-peaks. A special squared window variance transform (SWVT) is proposed to measure the signal variability in a certain time window, and this technique reduces false detection rate caused by the various types of interference presented in ECG signals. For the MIT-BIH arrhythmia database, the sensitivity of R-peak detection can reach 99.6% using the proposed method.
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Lu et al. (2021) studied this question.
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