Abstract
The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.
Ethical approval: The conducted research is not related to either human or animal use.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Conflict of interests: The authors declare no conflict of interest.
References
[1] Rangayyan RM. Biomedical signal analysis: a case-study approach. New York: Wiley-IEEE Press, 2009.Search in Google Scholar
[2] Hall JE, Guyton AC. Textbook of medical physiology. Philadelphia, PA: Saunders/Elsevier, 2011.Search in Google Scholar
[3] Raj S, Ray KC, Shankar O. Development of robust, fast and efficient QRS complex detector: a methodological review. Australas Phys Eng Sci Med 2018;41:581–600.10.1007/s13246-018-0670-7Search in Google Scholar PubMed
[4] Rahul J, Sora M, Sharma LD. An overview on biomedical signal analysis. Int J Recent Technol Eng 2019;7:206–9.Search in Google Scholar
[5] Kashani A, Barold SS. Significance of QRS complex duration in patients with heart failure. J Am Coll Cardiol 2005;46:2183–92.10.1016/j.jacc.2005.01.071Search in Google Scholar PubMed
[6] Curtin AE, Burns KV, Bank AJ, Netoff TI. QRS complex detection and measurement algorithms for multichannel ECGs in cardiac resynchronization therapy patients. IEEE J Transl Eng Health Med 2018;6:1900211.10.1109/JTEHM.2018.2844195Search in Google Scholar PubMed PubMed Central
[7] Thaler MS. The only EKG book you’ll ever need. Philadelphia: Lippincott Williams and Wilkins, 2010.Search in Google Scholar
[8] Arif M. Malagore IA, Afsar FA. Detection and localization of myocardial infarction using k nearest neighbor classifier. J Med Syst 2012;36:279–89.10.1007/s10916-010-9474-3Search in Google Scholar PubMed
[9] Nemati S, Abdala O, Monasterio V, Yim-Yeh S, Malhotra A, Clifford GD. A nonparametric surrogate based test of significance for T-wave alternans detection. IEEE Trans Biomed Eng 2011;58:1356–64.10.1109/TBME.2010.2047859Search in Google Scholar PubMed PubMed Central
[10] Esteban G, Barquero O, Blanco M, Caamano A, Garcia A, Rojo-Alvarez JL. Nonparametric signal processing validation in T-wave alternans detection and estimation. IEEE Trans. Biomed Eng 2014;61:1328–38.Search in Google Scholar
[11] Sharma LD, Sunkaria RK. A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 2016;87:194–204.10.1016/j.measurement.2016.03.015Search in Google Scholar
[12] Dohare AK, Kumar V, Kumar R. An efficient new method for the detection of QRS in electrocardiogram. Comput Electr Eng 2014;40:1717–30.10.1016/j.compeleceng.2013.11.004Search in Google Scholar
[13] Hamdi S, Ben AA, Bedoui MH. Real time QRS complex detection using DFA and regular grammar. Biomed Eng Online 2017;16:31.10.1186/s12938-017-0322-2Search in Google Scholar PubMed PubMed Central
[14] Pandit D, Zhang L, Liu C, Chattopadhyay S, Aslam N, Lim CP. A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput Methods Programs Biomed 2017;144:61–75.10.1016/j.cmpb.2017.02.028Search in Google Scholar PubMed
[15] Yakut O, Bolat ED. An improved QRS complex detection method having low computational load. Biomed Signal Proces 2018;42:230–41.10.1016/j.bspc.2018.02.004Search in Google Scholar
[16] Cesari M, Mehlsen J, Mehlsen AB, Sorensen HBD. A new wavelet-based ECG delineator for the evaluation of the ventricular innervation. IEEE J Transl Eng Health Med 2017;5:1–15.10.1109/JTEHM.2017.2722998Search in Google Scholar PubMed PubMed Central
[17] Chen PC, Lee S, Kuo CD. Delineation of T-wave in ECG by wavelet transform using multi scale differential operator. IEEE Trans Biomed Eng 2006;53:1429–33.10.1109/TBME.2006.875719Search in Google Scholar PubMed
[18] Madeiro JP, Santos EM, Cortez PC, Felix JH, Schlindwein FS. Evaluating Gaussian and Rayleigh-based mathematical models for T and P-waves in ECG. IEEE Latin Am Trans 2017;15:843–53.10.1109/TLA.2017.7910197Search in Google Scholar
[19] Saini I, Singh D, Khosla A. K-nearest neighbor based algorithm for P-and T-waves detection and delineation. J Med Eng Technol 2014;38:115–24.10.3109/03091902.2014.882424Search in Google Scholar PubMed
[20] Madeiro J, Nicolson WB, Cortez PC, Marques JA, Vázquez-Seisdedos CR, Elangovan N, et al. New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model. Med Eng Phys 2013;35:1105–15.10.1016/j.medengphy.2012.11.007Search in Google Scholar PubMed
[21] Sharma LD, Kumar S. Novel T-wave detection technique with minimal processing and RR interval based enhanced efficiency. Cardiovasc Eng Technol 2019;10:367–9.10.1007/s13239-019-00415-4Search in Google Scholar PubMed
[22] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20.10.1161/01.CIR.101.23.e215Search in Google Scholar PubMed
[23] Laguna P, Mark RG, Goldberger AL, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol 1997;24:673–6.10.1109/CIC.1997.648140Search in Google Scholar
[24] Press WH, Teukolsky SA. Savitzky-Golay smoothing filters. Comput Phys 1990;4:669–72.10.1063/1.4822961Search in Google Scholar
[25] Savitzky A, Golay MJ. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 1964;36:1627–39.10.1021/ac60214a047Search in Google Scholar
[26] Sauvola J, Pietikainen M. Adaptive document image binarization. Pattern Recognit 2000;33:225–36.10.1016/S0031-3203(99)00055-2Search in Google Scholar
[27] Leutheuser H, Gradl S, et al. Instantaneous P and T-wave detection: assessment of three ECG fiducial points detection algorithms. In 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2016;329–34.10.1109/BSN.2016.7516283Search in Google Scholar
[28] Elgendi M, Eskofier B, Abbott D. Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors (Basel) 2015;15:17693–714.10.3390/s150717693Search in Google Scholar PubMed PubMed Central
[29] Mehta SS, Lingayat NS. Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram. Comput Methods Prog Biomed 2009;93:46–60.10.1016/j.cmpb.2008.07.014Search in Google Scholar PubMed
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