A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

R.K. Tripathy; L.N. Sharma; S. Dandapat

A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

View Fulltext

Author(s): R.K. Tripathy ¹ ; L.N. Sharma ¹ ; S. Dandapat ¹
- Affiliations: 1: Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam 781039, India
Source: Volume 1, Issue 4, October 2014, p. 98 – 103
DOI: 10.1049/htl.2014.0080 , Online ISSN 2053-3713

Received 02/08/2014, Accepted 14/10/2014, Revised 13/10/2014, Published 06/11/2014

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

References

1. 1)
  - 8. Sharma, L.N., Dandapat, S., Mahanta, A.: ‘Multichannel ecg data compression based on multiscale principal component analysis’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, pp. 730–736 (doi: 10.1109/TITB.2012.2195322).
2. 2)
  - 11. Whitsel, E.A., Raghunathan, T.E., Pearce, R.M., Rautaharju, P.M., Lemaitre, R., Siscovick, D.S.: ‘RR interval variation, the QT interval index and risk of primary cardiac arrest among patients without clinically recognized heart disease’, Eur. Heart J., 2001, 22, pp. 165–173 (doi: 10.1053/euhj.2000.2262).
3. 3)
  - 6. Das, M.K., Ari, S.: ‘Patient-specific ECG beat classification technique’, Healthcare Technol. Lett., 2014, 1, pp. 98–103 (doi: 10.1049/htl.2014.0072).
4. 4)
  - 16. Ahmed, M.U., Mandic, D.P., Murray, A.: ‘Multivariate multiscale entropy analysis’, IEEE Signal Process. Lett., 2012, 19, pp. 91–94 (doi: 10.1109/LSP.2011.2180713).
5. 5)
  - 32. Kamath, C.: ‘ECG beat classification using features extracted from Teager energy functions in time and frequency domains’, IET Signal Process.2011, 5, (6), pp. 575–581 (doi: 10.1049/iet-spr.2010.0138).
6. 6)
  - 2. Thaler, M.S.: ‘The only EKG book you'll ever need’ (Lippincott Williams & Wilkins, 2010), vol. 365.
7. 7)
  - 1. Mendis, S., Puska, P., Norrving, B.: ‘Global atlas on cardiovascular disease prevention and control’ (World Health Organization, 2011).
8. 8)
  - 17. Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Vandewalle, J., Suykens, J.A.K., Gestel, T.V.: ‘Least squares support vector machines’ (World Scientific, 2002), vol. 4.
9. 9)
  - 13. Romero, I.: ‘Principal component analysis or independent component analysis applied to ambulatory electrocardiogram signals’. EP Patent App. EP20,110,181,173, 2012.
10. 10)
  - 3. Clifford, G., Tarassenko, L., Townsend, N.: ‘One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats’, Electron. Lett., 2001, 37, pp. 1126–1127 (doi: 10.1049/el:20010762).
11. 11)
  - 23. Yang, H.: ‘Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals’, IEEE Trans. Biomed. Eng., 2011, 58, pp. 339–347 (doi: 10.1109/TBME.2010.2063704).
12. 12)
  - 14. Martis, R.J., Acharya, U.R., Mandana, K.M., Ray, A.K., Chakraborty, C.: ‘Application of principal component analysis to ECG signals for automated diagnosis of cardiac health’, Expert Syst. Appl., 2012, 39, pp. 11792–11800 (doi: 10.1016/j.eswa.2012.04.072).
13. 13)
  - 21. Dehnavi, A.R.M., Farahabadi, I., Rabbani, H., Farahabadi, A., Mahjoob, M.P., Dehnavi, N.R.: ‘Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network’, J. Res. Med. Sci., Official J. Isfahan Univ. Med. Sci., 2011, 16.
14. 14)
  - 5. Li, Q., Rajagopalan, C., Clifford, G.D.: ‘Ventricular fibrillation and tachycardia classification using a machine learning approach’, IEEE Trans. Biomed. Eng.., 2014, 61, pp. 1607–1613 (doi: 10.1109/TBME.2013.2275000).
15. 15)
  - 18. Behera, S., Tripathy, R.K., Mohanty, S.: ‘Least square support vector machine modelling of breakdown voltage of solid insulating materials in the presence of voids’, J. Inst. Eng. (India) B, 2013, 94, pp. 21–27 (doi: 10.1007/s40031-013-0039-y).
16. 16)
  - 20. Oeff, M., Koch, H., Bousseljot, R., Kreiseler, D.: ‘The ptb diagnostic ecg database’ (National Metrology Institute of Germany, 2012), http://www.physionet.org/physiobank/database/ptbdb.
17. 17)
  - 12. Hasan, M.A., Abbott, D., Baumert, M.: ‘Beat-to-beat vectorcardiographic analysis of ventricular depolarization and repolarization in myocardial infarction’, PLoS One, 2012, 7, p. e49489 (doi: 10.1371/journal.pone.0049489).
18. 18)
  - 19. Varun, B., Pachori, R.B.: ‘Classification of seizure and nonseizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, pp. 1135–1142 (doi: 10.1109/TITB.2011.2181403).
19. 19)
  - 9. Pan, J., Tompkins, W.J.: ‘A real-time QRS detection algorithm’, IEEE Trans. Biomed. Eng., 1986, 3, pp. 230–236.
20. 20)
  - 22. Eriksson, P., Andersen, K., Swedberg, K., Dellbor, M.: ‘Vectorcardiographic monitoring of patients with acute myocardial infarction and chronic bundle branch block’, Eur. Heart J., 1997, 18, pp. 1288–1295 (doi: 10.1093/oxfordjournals.eurheartj.a015440).
21. 21)
  - 7. Manikandan, S., Barathram, R.: ‘Straightforward and robust QRS detection algorithm for wearable cardiac monitor’, Healthcare Technol. Lett., 2014, 1, pp. 40–44 (doi: 10.1049/htl.2013.0019).
22. 22)
  - 15. Tripathy, R.K., Mahanta, S., Paul, S.: ‘Artificial intelligence-based classification of breast cancer using cellular images’, RSC Adv., 2014, 4, pp. 9349–9355 (doi: 10.1039/c3ra47489e).
23. 23)
  - 10. Manikandan, S., Dandapat, S.: ‘Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review’, Biomed. Signal Process. Control, 2014, 14, pp. 73–107 (doi: 10.1016/j.bspc.2014.07.002).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

References

Related content