A Critical Review of Various Peak Detection Techniques of ECG Signals

Desh Deepak Gautam*, V. K. Giri**
* Research Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
Periodicity:July - September'2016
DOI : https://doi.org/10.26634/jdp.4.3.8145

Abstract

ECG is an important signal which is most commonly used for the diagnosing of various heart diseases. The analysis of an ECG signal includes preprocessing and feature extraction. Signal processing of an ECG wave, which includes noise reduction and R-Peak detection of the signal, is one of the most important part for its analysis. The presented paper discusses several techniques of noise reduction and R-Peak detection which were proved effective in last few decades. Efficiency of various methods can be defined in terms of detection error rate. Latest research has shown very effective results with error rate less than 0.3%.

Keywords

Signal Processing, R-Peak Detection, Hilbert Transform, SVM.

How to Cite this Article?

Gautam, D.D., and Giri, V.K. (2016). A Critical Review of Various Peak Detection Techniques of ECG Signals. i-manager’s Journal on Digital Signal Processing, 4(3), 27-36. https://doi.org/10.26634/jdp.4.3.8145

References

[1]. Jeongwhan Lee, et al., (1996). “A Simple Real-time QRS th detection algorithm”. Proceeding of the 18 Annual Conf. of the IEEE.
[2]. A. Wrublcwski, et al., (1989). “Real Time Early Detection of R-Wave of ECG Signal”. IEEE Engineering in Medicine and Biology Society, pp. 38–39.
[3]. M. Ibrahim Sezan, (1990). “A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction”. Computer Vision, Graphics and Image Processing, Vol. 49, No. 1, pp. 36-51.
[4]. A.M. Bianchi, L. Mainardi, E. Petrucci, and M.G. Signorini, (1993). “Time- Variant Power Spectrum Analysis for the Detection of Transient Episodes in HRV Signal I”. IEEE Trans.Biomed.Eng., Vol. 40, No. 2, pp. 136–144.
[5]. R.S. Anand and V. Kumar, (1995). “Efficient and Reliable th Detection of QRS Segment in ECG Signals”. 14 Conference of the Biomedical Engineering Society of India, Vol. No. 10, pp. 56–57.
[6]. W. Zhang, X. Wang, L. Ge, and Z. Zhang, (2005). “Noise Reduction in ECG Signal Based on Adaptive Wavelet Transform”. Proc. IEEE/EMBS Annual Conference, pp. 2699–2702.
[7]. G. Vijaya, V. Kumar, and H.K. Verma, (2007). “Artificial Neural Network Based Wave Complex Detection in Electrocardiograms”. International Journal of Systems Science, Vol. 28, No. 2, pp. 125-132.
[8]. D. Widjaja, S. Vandeput, J. Taelman, M.A.K.A. Braeken, A. Otte, R. H. Van Den Bergh, and S. Van Huffel, (2010). “Accurate R Peak Detection and Advanced Preprocessing of Normal ECG for Heart Rate Variability Analysis”. Computers in Cardiology, pp. 533-536.
[9]. Jo Lynn Tan, (2010). “Signal Analysis and Classification of Digital Communication Signal using Time- Frequency Analysis Techniques in the Multipath Fading Environment”. 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010). pp. 320–323.
[10]. G. Ranganathan, et al., (2010). “Signal Processing of Heart Rate Variability using Wavelet Transform for Mental Stress Measurement”. Journal of Theoretical and Applied Info.Tech, pp. 124-129.
[11]. P. Sasikala, (2010). “Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform”. International Journal of Advanced Computer Science and Applications, Vol. 1, No. 6, pp. 48–53.
[12]. Y. Li and X. Chen, (2011). “A Robust R-Wave Detection algorithm in ECG Signal”. Proc.Intl.Conf. on Transportation, Mechanical, and Electrical Engineering, pp. 2433–2436.
[13]. H. Rabbani, M.P. Mahjoob, E. Farahabadi, and A. Farahabadi, (2011). “R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding”. J.Med. Signals Sens, Vol. 1, No. 2, pp. 91–98.
[14]. Z.S. Wang and J.D.Z. Chen, (2011). “Robust ECG R-R Wave Peak Detection Using Evolutionary-Programmingbased Fuzzy Inference System (EPFIS) and It % -Application to Assessing Brain-Gut Interaction”. Advances in Medical Signal and Information Processing, pp. 265–274.
[15]. H. Kew and D. Jeong, (2011). “Variable Threshold Method for ECG R-peak Detection”. J.Med.Syst., Vol. 35, No. 5, pp. 1085–1094.
[16]. D. Sadhukhan and M. Mitra, (2012). “R-peak Detection Algorithm for ECG using Double Difference and RR Interval Processing”. Procedia Technol., Vol. 4, pp. 873–877.
[17]. F. Scholkmann, J. Boss, and M. Wolf, (2012). “An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals”. Algorithms, Vol. 5, No. 4, pp. 588–603.
[18]. S.K. Salih, S.A. Aljunid, A. Yahya, and K. Ghailan, (2012). "A Novel Approach for Detecting QRS Complex of ECG Signal”. IJCSI, Vol. 9, No. 6, pp. 205–215.
[19]. T.R. G. Nair, A.P. Geetha, and M. Asharani, (2013). “adaptive Wavelet Technique”. Vol. 1902, No. 7, pp. 429–435.
[20]. H. Lin, S. Liang, Y. Ho, and Y. Lin, (2013). “Discrete- Wavelet-Transform-Based Noise Reduction and R Wave Detection for ECG Signals”. Healthcom, pp. 355–360.
[21]. I. Nouira, A. Ben Abdallah, M.H. Bedoui, and M. Dogui, (2013). “A Robust R Peak Detection Algorithm Using Wavelet Transform for Heart Rate Variability Studies”. Intl.J.Elec.Engg and Informatics, Vol. 5, No. 3, pp. 270–284.
[22]. C. Networks, N. Singh, and S. Ayub, (2013). “Design of Digital IIR Filter for Noise Reduction in ECG Signal”. CICN, IEEE.
[23]. R. Alonso, A. J. Méndez, and X. A. Vila, (2013). “A comparison of three QRS Detection Algorithms over a Public Database”. Procedia Technol., Vol. 9, pp. 1159–1165.
[24]. Indu Saini, et al., (2014). “P- and T-wave Delineation in ECG Signals using Support Vector Machine”. IEEE Journal of Research, Vol. 59, No. 5, pp. 37–41.
[25]. P. Trivedi and S. Ayub, (2014). “Detection of R Peak in Electrocardiogram”. IJCA, Vol. 97, No. 20, pp. 10–13.
[26]. J.S. Dhir and N.K. Panag, (2014). “ECG Analysis and R Peak Detection Using Filters and Wavelet Transform”. IJIRC & E, Vol. 2, No. 2, pp. 2883–2890.
[27]. H.M.R.A. Trivedi and K. C. S. Shukla, (2014). “R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network”. J.Inst.Eng. India Scr.B, Vol. 95, No.1, pp. 63–71.
[28]. S.M. Sabrigiriraj, (2014). “A New LMS Based Noise Removal and DWT Based R-peak Detection in ECG Signal for Biotelemetry Applications”. National Academy Science Letters, Vol. 37, No. 4, pp. 341–349.
[29]. A.T. Bhatti, (2015). “R-Peak Detection in ECG Signal Compression for Heartbeat Rate Patients at 1KHz using High Order Statistic Algorithm”. JMEST, Vol. 2, No. 9, pp. 2509- 2515.
[30]. S. Bensegueni, and A. Bennia, (2015). “R-Peak Detection using Wavelet Transforms”. U.P.B. Sci. Bull. Series, Vol. 77, No. 3.
[31]. W.K. L. Ee, H.Y. Oon, and K.S.P. Ark, (2015). “Smart ECG Monitoring Patch with Built-in R -Peak Detection for Long- Term HRV Analysis”. Annals of Biomedical Engineering, Vol. 44, No. 7, pp. 2292-2301.
[32]. T. Chanwimalueang, W. Von Rosenberg, and D.P. Mandic, (2015). “Enabling R-peak Detection in Wearable ECG : Combining Matched Filtering and Hilbert Transform”. IEEE Conf. on DSP, pp. 134–138.
[33]. H. He, Z. Wang, and Y. Tan, (2015). “Noise Reduction of ECG Signals through Genetic Optimized Wavelet Threshold Filtering”. IEEE Conf. on CIVEMSA, pp. 0–5.
[34]. G. Chen, X. Wang, and W. Wan, (2015). “An ECG RWave Detection Algorithm Based on Adaptive Threshold”. IEEE Conf. on ICSSC, pp. 145–149.
[35]. S.S. Mehta and N.S. Lingayat, (2016). “SVM based QRS Detection in Electrocardiogram using Signal Entropy”. IETE Journal of Research, Vol. 54, No.3, pp. 231-240.
[36]. K.D. Rao, (1997). “DWT Based Detection of R-peaks and Data Compression of ECG Signals”. IETE J. Res., Vol. 43, No. 5, pp. 345-349.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.