Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach
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Early and accurate detection of myocardial infarction is imperative for reducing the mortality rate due to heart attack. Present work proposes a novel technique aiming toward accurate and timely detection of inferior myocardial infarction (IMI). Stationary wavelet transform has been used to decompose the segmented multilead electrocardiogram (ECG) signal into different sub-bands. Sample entropy, normalized sub-band energy, log energy entropy, and median slope calculated over selected bands of multilead ECG are used as features. Support vector machine (SVM) and K-nearest neighbor (KNN) have been used to classify between subjects admitted for health control (HC) and patients suffering from IMI, using attributes selected on the basis of gain ratio. The full length ECG of lead II, III, and aVF of all the subjects having IMI or admitted for HC from Physikalisch-Technische Bundesanstalt Database (PTB-DB) has been used in the present work. The proposed technique has been scrutinized under both “class-oriented,” and more practical, “subject-oriented” approach. Under the class-oriented approach, data have been divided into training and test data irrespective of the patients, whereas in subject-oriented approach, data from one patient have been used for test and training has been done on the rest of the subjects. Under the class-oriented approach, area under the receiver operating characteristic curve (Roc), sensitivity (Se%), specificity (Sp%), positive predictivity (+P%), and accuracy (Ac%) is Roc \(=\) 0.9945, Se% \(=\) 98.67, Sp% \(=\) 98.72, +P% \(=\) 98.79, Ac% \(=\) 98.69 using KNN and Roc \(=\) 0.9994, Se% \(=\) 99.35, Sp% \(=\) 98.29, +P% \(=\) 98.41, Ac% \(=\) 98.84 using SVM. For the subject-oriented approach, an average Ac% \(=\) 81.71, Se% \(=\) 79.01, Sp% \(=\) 79.26, and +P% \(=\) 80.25 has been achieved. This shows the potential of the proposed technique to work for an unknown subject, on which it has not been trained.
KeywordsInferior myocardial infarction (IMI) Stationary wavelet transform (SWT) Feature extraction Feature selection K-nearest neighbors (KNN) Support vector machine (SVM)
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.
Compliance with ethical standards
Conflict of interest
There is no conflict of interest.
- 3.W.H.O.: World health statistics 2015: part ii: global health indicators. Technical report, World Health Organisation (2015)Google Scholar
- 4.IANS: Heart attack kills one person every 33 seconds in India. http://timesofindia.indiatimes.com/life-style/health-fitness/health-news/Heart-attack-kills-one-person-every-33-seconds-in-India/articleshow/52339891.cms. Times of India, 19 May 2016
- 6.Antman, E.M., et al.: ACC/AHA guidelines for the management of patients with ST-elevation myocardial infarctionexecutive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (writing committee to revise the 1999 guidelines for the management of patients with acute myocardial infarction). J. Am. Coll. Cardiol. 44(3), 671–719 (2004)CrossRefGoogle Scholar
- 8.Acharya, U.R., Fujita, H., Sudarshan, V.K., Oh, S.L., Adam, M., Koh, J.E., Tan, J.H., Ghista, D.N., Martis, R.J., Chua, C.K., et al.: Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl. Based Syst. 99, 146–156 (2016)CrossRefGoogle Scholar
- 13.Lahiri, T., Kumar, U., Mishra, H., Sarkar, S., Roy, A.D.: Analysis of ecg signal by chaos principle to help automatic diagnosis of myocardial infarction. J. Sci. Ind. Res. 68, 866–870 (2009)Google Scholar
- 14.Lu, H., Ong, K., Chia, P.: An automated ECG classification system based on a neuro-fuzzy system. In: Computers in Cardiology, pp. 387–390. IEEE, Cambridge, MA, USA (2000)Google Scholar
- 19.Schou, A., Grove, U.S., Worbech, T.H., Andersen, M.P., Terkelsen, C.J., Kaltoft, A.K., Struijk, J.J., et al.: ECG based estimation of area at risk in acute myocardial infarction. In: Computing in Cardiology, pp. 413–416. IEEE, Hangzhou, China (2011)Google Scholar
- 22.Zheng, H., Wang, H., Nugent, C., Finlay, D.: Supervised classification models to detect the presence of old myocardial infarction in body surface potential maps. In: Computers in Cardiology, pp. 265–268. IEEE, Valencia, Spain (2006)Google Scholar
- 24.Tripathy, R., Dandapat, S.: Detection of myocardial infarction from vectorcardiogram using relevance vector machine. SIVip. 1–8 (2017). doi: 10.1007/s11760-017-1068-9
- 28.Bousseljot, R., Kreiseler, D., Schnabel, A.: Nutzung der ekg-signaldatenbank cardiodat der ptb uber das internet. Biomed. Eng. 40(s1), 317–318 (1995)Google Scholar
- 36.Neurauter, A., Eftestol, T., Kramer-Johansen, J., Abella, B.S., Sunde, K., Wenzel, V., Lindner, K.H., Eilevstjonn, J., Myklebust, H., Steen, P.A., et al.: Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks. Resuscitation 73(2), 253–263 (2007)CrossRefGoogle Scholar
- 38.Oliveira, R.B., Papa, J.P., Pereira, A.S., Tavares, J.M.R.: Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput. Appl. 1–24 (2016). doi: 10.1007/s00521-016-2482-6
- 40.Mitchell, T.M: Machine Learning. McGraw-Hill (1997)Google Scholar
- 42.de Albuquerque, V.H.C., Nunes, T.M., Pereira, D.R., Luz, E.J.D.S., Menotti, D., Papa, J.P., Tavares, J.M.R.: Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput. Appl. 1–15 (2016). doi: 10.1007/s00521-016-2472-8