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Novel T-wave Detection Technique with Minimal Processing and RR-Interval Based Enhanced Efficiency

  • Lakhan Dev SharmaEmail author
  • Ramesh Kumar Sunkaria
Article
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Abstract

Purpose

T-wave in electrocardiogram (ECG) is a vital wave component and has potential of diagnosing various cardiac disorders. The present work proposes a novel technique for T-wave peak detection using minimal pre-processing and simple root mean square based decision rule.

Methods

The technique uses a two-stage median filter and a Savitzky–Golay smoothing filter for pre-processing. P-QRS-complex is removed from the filtered ECG, and T-wave is left as the most prominent wave segment, which can be detected using a root mean square based adaptive threshold. An RR-interval based T-wave peak correction strategy has been proposed which can handle the challenges of morphological variations in the T-wave, thus increases the detection accuracy.

Results

The proposed technique has been substantiated on a standard QT-database. The detection sensitivity = 97.01%, positive predictivity = 99.61%, detection error rate = 3.36%, and accuracy = 96.66% have been achieved.

Conclusions

A T-wave detection technique requiring minimal pre-processing and with simple decision rule has been designed. The noticeably high positive predictivity rate of the proposed technique shows its efficiency to detect T-wave peak.

Keywords

ECG T-wave Median filter Savitzky–Golay filter Root mean square RR-interval 

Notes

Acknowledgments

Authors are thankful to the Ministry of Human Resource Development, Government of India for providing the financial assistance. This work has been done at Medical Imaging and Computational Modeling of Physiological System Research Laboratory at Department of Electronics and Communication Engineering of Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India.

Conflict of interest

Authors declare that they have no conflict of interest.

Human Studies/Informed Consent

This work uses freely available standard QT-Database for validation of the proposed technique. No human studies were carried out by the authors for this article.

Research Involving Animal Rights

No animal studies were carried out by the authors for this article.

References

  1. 1.
    Albrecht, P. ST segment characterization for long term automated ECG analysis, Ph.D. thesis, Massachusetts Institute of Technology, Department of Electrical Engineering, 1983.Google Scholar
  2. 2.
    Arif, M., I. A. Malagore, and F. A. Afsar. Detection and localization of myocardial infarction using k-nearest neighbor classifier. J. Med. Syst. 36(1):279–289, 2012.CrossRefGoogle Scholar
  3. 3.
    Cesari, M., J. Mehlsen, A.-B. Mehlsen, and H. B. D. Sorensen. A new wavelet-based ECG delineator for the evaluation of the ventricular innervation. IEEE J. Transl. Eng. Health Med. 5:1–15, 2017.CrossRefGoogle Scholar
  4. 4.
    Chen, P.-C., S. Lee, and C.-D. Kuo. Delineation of T-wave in ECG by wavelet transform using multiscale differential operator. IEEE Trans. Biomed. Eng. 53(7):1429–1433, 2006.CrossRefGoogle Scholar
  5. 5.
    Deepu, C. and Y. Lian. A joint QRS detection and data compression scheme for wearable sensors. IEEE Trans. Biomed. Eng. 62:165–175, 2014.CrossRefGoogle Scholar
  6. 6.
    do Vale Madeiro, J. P., E. M. B. E. dos Santos, P. C. Cortez, J. H. da Silva Felix, and F. S. Schlindwein. Evaluating gaussian and rayleigh-based mathematical models for T and P-waves in ECG. IEEE Latin Am. Trans. 15(5):843–853, 2017.CrossRefGoogle Scholar
  7. 7.
    Dohare, A. K., V. Kumar, and R. Kumar. An efficient new method for the detection of QRS in electrocardiogram. Comput. Electr. Eng. 40(5):1717–1730, 2014.CrossRefGoogle Scholar
  8. 8.
    Elgendi, M., B. Eskofier, D. Abbott. Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves. Sensors 15(7):17693–17714, 2015.CrossRefGoogle Scholar
  9. 9.
    Goldberger A., L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. K. Peng, and H. Stanley. PhysioBank, PhysioToolkit, PhysioNet, components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220, 2000.CrossRefGoogle Scholar
  10. 10.
    Goya-Esteban, R., O. Barquero-Perez, M. Blanco-Velasco, A. Caamano-Fernandez, A. Garcia-Alberola, and J. L. Rojo-Alvarez. Nonparametric signal processing validation in T-wave alternans detection and estimation. IEEE Trans. Biomed. Eng. 61(4):1328–1338, 2014.CrossRefGoogle Scholar
  11. 11.
    Greenwald, S. D. The development and analysis of a ventricular fibrillation detector, Ph.D. thesis, Massachusetts Institute of Technology, 1986.Google Scholar
  12. 12.
    Greenwald, S. D., R. S. Patil, and R. G. Mark. Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information. In: Proceedings of the Computers in Cardiology. IEEE, pp. 461–464, 1990.Google Scholar
  13. 13.
    Khaled, A., and B. Abdelhak. Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing 145:438–450, 2014.CrossRefGoogle Scholar
  14. 14.
    Laguna, P., R. G. Mark, A. Goldberg, and G. B. Moody. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Proceedings of the Computers in Cardiology, IEEE, pp. 673–676, 1997.Google Scholar
  15. 15.
    Leutheuser, H., S. Gradl, L. Anneken, M. Arnold, N. Lang, S. Achenbach, and B. M. Eskofier. Instantaneous P-and T-wave detection: assessment of three ECG fuducial points detection algorithms, In: 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, pp. 329–334, 2016.Google Scholar
  16. 16.
    Lin, C., G. Kail, A. Giremus, C. Mailhes, J.-Y. Tourneret, and F. Hlawatsch. Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block gibbs sampler and marginalized particle filter. Signal Process. 104:174–187, 2014.CrossRefGoogle Scholar
  17. 17.
    Lin, C., C. Mailhes, and J.-Y. Tourneret. P-and T-wave delineation in ECG signals using a bayesian approach and a partially collapsed gibbs sampler. IEEE Trans. Biomed. Eng. 57(12):2840–2849, 2010.CrossRefGoogle Scholar
  18. 18.
    Li, C., C. Zheng, and C. Tai. Detection of ecg characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1):21–28, 1995.CrossRefGoogle Scholar
  19. 19.
    Madeiro, J. P., W. B. Nicolson, P. C. Cortez, J. A. Marques, C. R. Vazquez-Seisdedos, N. Elangovan, G. A. Ng, and F. S. Schlindwein. 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. 35(8):1105–1115, 2013.CrossRefGoogle Scholar
  20. 20.
    Manikandan, M. S., and K. Soman. A novel method for detecting R-peaks in electrocardiogram ECG signal. Biomed. Signal Process. Control 7(2):118–128, 2012.CrossRefGoogle Scholar
  21. 21.
    Martinez, J. P., R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4):570–581, 2004.CrossRefGoogle Scholar
  22. 22.
    Mehta, S. S., and N. S. Lingayat. Application of support vector machine for the detection of P-and T-waves in 12-lead electrocardiogram. Comput. Methods Prog. Biomed. 93(1):46–60, 2009.CrossRefGoogle Scholar
  23. 23.
    Merino, M., I. M. Gomez, and A. J. Molina. Envelopment filter and K-means for the detection of QRS waveforms in electrocardiogram. Med. Eng. Phys. 37(6):605–609, 2015.CrossRefGoogle Scholar
  24. 24.
    Mitra, S., M. Mitra, and B. B. Chaudhuri. A rough-set-based inference engine for ECG classification. IEEE Trans. Instrum. Meas. 55(6):2198–2206, 2006.CrossRefzbMATHGoogle Scholar
  25. 25.
    Moody, G. B. , and R. G. Mark. The MIT-BIH arrhythmia database on CD-ROM and software for use with it. In: Proceedings of the Computers in Cardiology. IEEE, pp. 185–188, 1990.Google Scholar
  26. 26.
    Nemati, S., O. Abdala, V. Monasterio, S. Yim-Yeh, A. Malhotra, and G. D. Clifford. A nonparametric surrogate-based test of significance for T-wave alternans detection. IEEE Trans. Biomed. Eng. 58(5):1356–1364, 2011.CrossRefGoogle Scholar
  27. 27.
    Ning, X., and I. W. Selesnick. ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control 8(6):713–723, 2013.CrossRefGoogle Scholar
  28. 28.
    Orini, M., B. Hanson, V. Monasterio, J. P. Martinez, M. Hayward, P. Taggart, and P. Lambiase. Comparative evaluation of methodologies for T-wave alternans mapping in electrograms. IEEE Trans. Biomed. Eng. 61(2):308–316, 2014.CrossRefGoogle Scholar
  29. 29.
    Pillarisetti, J., and K. Gupta. Giant inverted T waves in the emergency department: case report and review of differential diagnoses. J. Electrocardiol. 43(1):40–42, 2010.CrossRefGoogle Scholar
  30. 30.
    Saini, I., D. Singh, and A. Khosla. K-nearest neighbour-based algorithm for P-and T-waves detection and delineation. J. Med. Eng. Technol. 38(3):115–124, 2014.CrossRefGoogle Scholar
  31. 31.
    Shafait, F., D. Keysers, and T. M. Breuel. Efficient implementation of local adaptive thresholding techniques using integral images. In: Proceedings of the Electronic Imaging 2008, International Society for Optics and Photonics, pp. 681510–681510, 2008.Google Scholar
  32. 32.
    Sharma, L. D., and R. K. Sunkaria. A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 87:194–204, 2016.CrossRefGoogle Scholar
  33. 33.
    Sharma, L. D., and R. K. Sunkaria. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process. 12(2):199–206, 2018.CrossRefGoogle Scholar
  34. 34.
    Sharma, L. D. and R. K. Sunkaria. Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers. Measurement 125:29–36, 2018.CrossRefGoogle Scholar
  35. 35.
    Shenthar, J., S. Deora, M. Rai, and C. N. Manjunath. Prolonged T peak-end and T peak- end/QT ratio as predictors of malignant ventricular arrhythmias in the acute phase of ST- segment elevation myocardial infarction: a prospective case-control study. Heart Rhythm 12(3):484–489, 2015.CrossRefGoogle Scholar
  36. 36.
    Taddei, A., A. Biagini, G. Distante, M. Emdin, M. Mazzei, P. Pisani, N. Roggero, M. Varanini, R. Mark, and G. Moody, et al. The european ST-T database: development, distribution and use. In: Proceedings Computers in Cardiology. IEEE, pp. 177–180, 1990.Google Scholar
  37. 37.
    ThalerM. S. The Only EKG Book You’ll Ever Need. Philadelphia: Lippincott Williams and Wilkins, 2010.Google Scholar
  38. 38.
    Verma, N., V. M. Figueredo, A. M. Greenspan, and G. S. Pressman. Giant U waves: an important clinical clue. Res. Rep. Clin. Cardiol. 2:51–55, 2011.Google Scholar
  39. 39.
    Wan, X., Y. Li, C. Xia, M. Wu, J. Liang, and N. Wang. A T-wave alternans assessment method based on least squares curve fitting technique. Measurement 86:93–100, 2016.CrossRefGoogle Scholar
  40. 40.
    Yochum, M., C. Renaud, and S. Jacquir. Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomed. Signal Process. Control 25:46–52, 2016.CrossRefGoogle Scholar
  41. 41.
    Zidelmal, Z., A. Amirou, M. Adnane, and A. Belouchrani. Qrs detection based on wavelet coefficients. Comput. Methods Prog. Biomed. 107(3):490–496, 2012.CrossRefGoogle Scholar
  42. 42.
    Zidelmal, Z., A. Amirou, D. Ould-Abdeslam, A. Moukadem, and A. Dieterlen. QRS detection using S-transform and shannon energy. Comput. Methods Prog. Biomed. 116(1):1–9, 2014.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringDr. B. R. Ambedkar National Institute of TechnologyJalandharIndia

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