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An efficient wavelet-based automated R-peaks detection method using Hilbert transform
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DOI：10.1016/j.bbe.2017.02.002.png)
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Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal. (C) 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Keyword:
Electrocardiogram (ECG)
R-peak
Wavelet transform
Hilbert transform
MIT-BIH database
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