Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG

Sushree Satvatee Swain; Dipti Patra

Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG

View Fulltext

Author(s): Sushree Satvatee Swain¹ and Dipti Patra¹
- Affiliations: 1: IPCV Lab, Department of Electrical Engineering, National Institute of Technology , Rourkela , India
Source: Volume 6, Issue 1, February 2019, p. 1 – 7
DOI: 10.1049/htl.2018.5007 , Online ISSN 2053-3713

« Previous Article
Table of contents
Next Article »

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)

Received 08/05/2018, Accepted 13/08/2018, Revised 31/07/2018, Published 19/12/2018

Over the decades, electrocardiogram (ECG) has been proved as the chief diagnostic tool for assessment of the cardiovascular condition of human being. Myocardial Infarction (MI) is commonly known as heart attack, happens when blood supply stops to heart muscles causing occlusions in some portion or whole artery. MI is the result of three pathological changes such as elevation of ST-segment, the appearance of wide pathological Q-wave and inversion of T-wave in ECG record. Detection of MI by considering few ECG leads generally requires prior information about the pathological behaviour of the disease. The present work considers 12 leads to view the cardiac condition from various angles in ECG signal for accurate detection of MI. This Letter investigates on various wavelet basis functions, i.e. Haar, Daubechies, Symlet, Coiflet and biorthogonal basis filters of different order for selecting the most suitable one for the detection of MI. Wavelet transform of 12-lead ECG signal decomposes the signal into different subbands. A comparative study has been done based on the multiscale energy at different wavelet subbands for the selection of most suitable wavelet basis for the accurate detection of MI. The experimentation is carried out on different datasets from the PTB diagnostic ECG database.

References

1. 1)
  - 18. Bakshi, B.R.: ‘Multiscale PCA with application to multivariate statistical process monitoring’, AIChE J., 1998, 44, (7), pp. 1596–1610 (doi: 10.1002/aic.690440712).
2. 2)
  - 15. Hedén, B., Öhlin, H., Rittner, R., et al: ‘Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks’, Circulation, 1997, 96, (6), pp. 1798–1802 (doi: 10.1161/01.CIR.96.6.1798).
3. 3)
  - 20. Swain, S.S., Patra, D.: ‘Analysis of myocardial infarction using wavelet transform and multiscale energy analysis’. TENCON 2017–2017 IEEE Region 10 Conf., 2017, pp. 1640–1645.
4. 4)
  - 8. Castells, F., Laguna, P., Sörnmo, L., et al: ‘Principal component analysis in ECG signal processing’, EURASIP J. Adv. Signal Process., 2007, 2007, (1), p. 074580 (doi: 10.1155/2007/74580).
5. 5)
  - 14. Papaloukas, C., Fotiadis, D.I., Likas, A., et al: ‘Automated methods for ischemia detection in long duration ECGs’, Cardiovasc. Rev. Rep., 2003, 24, (6), pp. 313–319.
6. 6)
  - 3. Morrow, D.A.: ‘Myocardial infarction: a companion to Braunwald's heart disease’ (Elsevier Health Sciences, New York, NY, USA, 2016).
7. 7)
  - 7. Addison, P.S.: ‘Wavelet transforms and the ECG: a review’, Physiol. Meas., 2005, 26, (5), pp. R155–R199 (doi: 10.1088/0967-3334/26/5/R01).
8. 8)
  - 21. Oeff, M., Koch, H., Bousseljot, R., et al: ‘The PTB diagnostic ECG database’, National Metrology Institute of Germany, 2012. Available at http://www physionet org/physiobank/database/ptbdb.
9. 9)
  - 5. Thygesen, K., Alpert, J.S., Jaffe, A.S., et al: ‘Third universal definition of myocardial infarction’, Eur. Heart J., 2012, 33, (20), pp. 2551–2567 (doi: 10.1093/eurheartj/ehs184).
10. 10)
  - 16. Lu, H., Ong, K., Chia, P.: ‘An automated ECG classification system based on a neuro-fuzzy system’. Computers in Cardiology 2000, 2000, pp. 387–390.
11. 11)
  - 4. Goodman, S.G., Steg, P.G., Eagle, K.A., et al: ‘The diagnostic and prognostic impact of the redefinition of acute myocardial infarction: lessons from the global registry of acute coronary events (grace)’, Am. Heart J., 2006, 151, (3), pp. 654–660 (doi: 10.1016/j.ahj.2005.05.014).
12. 12)
  - 5. Sharma, L.N., Dandapat, S., Mahanta, A.: ‘Multichannel ECG data compression based on multiscale principal component analysis’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (4), pp. 730–736 (doi: 10.1109/TITB.2012.2195322).
13. 13)
  - 31. Jayachandran, E.S., Joseph, K.P., Acharya, U.R.: ‘Analysis of myocardial infarction using discrete wavelet transform’, J. Med. Syst., 2010, 34, (6), pp. 985–992 (doi: 10.1007/s10916-009-9314-5).
14. 14)
  - 10. Ranjith, P., Baby, P., Joseph, P.: ‘ECG analysis using wavelet transform: application to myocardial ischemia detection’, ITBM-RBM, 2003, 24, (1), pp. 44–47 (doi: 10.1016/S1297-9562(03)00003-2).
15. 15)
  - 1. Hall, J.E.: ‘Text book of medical physiology’ (Elsevier Health Sciences, New York, NY, USA, 2006, 11th edn.).
16. 16)
  - 9. Sun, L., Lu, Y., Yang, K., et al: ‘ECG analysis using multiple instance learning for myocardial infarction detection’, IEEE Trans. Biomed. Eng., 2012, 59, (12), pp. 3348–3356 (doi: 10.1109/TBME.2012.2213597).
17. 17)
  - 7. Gramatikov, B., Georgiev, I.: ‘Wavelets as alternative to short-time Fourier transform in signal-averaged electrocardiography’, Med. Biol. Eng. Comput., 1995, 33, (3), pp. 482–487 (doi: 10.1007/BF02510534).
18. 18)
  - 2. Goldberger, A.L.: ‘Clinical electrocardiography: a simplified approach’ (Elsevier Health Sciences, New York, NY, USA, 2012).
19. 19)
  - 23. Tripathy, R., Dandapat, S.: ‘Detection of myocardial infarction from vectorcardiogram using relevance vector machine’, Signal. Image Video Process., 2017, 11, (6), pp. 1139–1146 (doi: 10.1007/s11760-017-1068-9).
20. 20)
  - 13. Arif, M., Malagore, I.A., Afsar, F.A.: ‘Detection and localization of myocardial infarction using k-nearest neighbor classifier’, J. Med. Syst., 2012, 36, (1), pp. 279–289 (doi: 10.1007/s10916-010-9474-3).
21. 21)
  - 27. Al-Fahoum, A.S.: ‘Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure’, IEEE Trans. Inf. Technol. Biomed., 2006, 10, (1), pp. 182–191 (doi: 10.1109/TITB.2005.855554).
22. 22)
  - 12. Mitra, S., Mitra, M., Chaudhuri, B.B.: ‘A rough-set-based inference engine for ECG classification’, IEEE Trans. Instrum. Meas., 2006, 55, (6), pp. 2198–2206 (doi: 10.1109/TIM.2006.884279).
23. 23)
  - 3. de Chazal, P., O'Dwyer, M., Reilly, R.B.: ‘Automatic classification of heartbeats using ECG morphology and heartbeat interval features’, IEEE Trans. Biomed. Eng., 2004, 51, (7), pp. 1196–1206 (doi: 10.1109/TBME.2004.827359).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Multiscale energy based suitable wavelet selection for detection of myocardial infarction in ECG

References

Related content