An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator
- 30 June 2020
- journal article
- research article
- Published by International Information and Engineering Technology Association in Traitement du Signal
- Vol. 37 (3), 511-518
- https://doi.org/10.18280/ts.370319
Abstract
The exact discovery of R-peak becomes very much crucial while extracting prominent features from Electrocardiogram (ECG) signal. However, identification of R-peaks precisely becomes more challenging due to contamination of noise and fragmented QRS complexes. This paper presents an improved method of marking R-peaks. Initially, an efficient Fourier Decomposition Methodology (FDM) is used for removing noise. The accuracy of finding R-peaks can be improved by enhancing the QRS complexes using Teager Energy Operator. Hilbert Transform and Zero Cross Detector (ZCD) are used for marking the R-peaks. The MIT-BIH arrhythmia database is used for validating the proposed scheme and attained 99.97% accuracy, 99.98% of sensitivity and 99.98% of positive predictivity. The findings proved that proposed method is superior as compared to the proven techniques in the literature.Keywords
This publication has 20 references indexed in Scilit:
- ECG signal analysis using modified S‐transformHealthcare Technology Letters, 2017
- R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy EnvelopeJournal of Healthcare Engineering, 2017
- Effective QRS-Detector Based on Hilbert Transform and Adaptive ThresholdingIFMBE Proceedings (IFMBE), 2016
- A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiencyMeasurement, 2016
- Multiresolution wavelet‐based QRS complex detection algorithm suited to several abnormal morphologiesIET Signal Processing, 2014
- QRS detection using S-Transform and Shannon energyComputer Methods and Programs in Biomedicine, 2014
- Fractional zero-phase filtering based on the Riemann–Liouville integralSignal Processing, 2014
- Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate EnvelopeCircuits, Systems, and Signal Processing, 2013
- QRS detection based on wavelet coefficientsComputer Methods and Programs in Biomedicine, 2012
- Combining Algorithms in Automatic Detection of QRS Complexes in ECG SignalsIEEE Transactions on Information Technology in Biomedicine, 2006