Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach

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.