Table 8.

Results of this study compared with previous studies in the diagnosis of healthy subjects from cases with heart disease (myocardial infarction detection)

Researcher	Year	Method	Database	Specificity (%)	Accuracy (%)	Sensitivity (%)
Safdarian et al.[28]	2014	Extracted T-wave integral and total integral of one ECG cycle Features Classification using PNN, KNN, MLP neural network, naive bayes methods	PhysioNet PTB	-	94.74	-
Sugimoto et al.[4]	2019	Cannulation networks KNN method	PhysioNet PTB	99.59	99.87	99.91
Barmpouti et al.[5]	2019	Grassmannian and euclidean mapping Hilbert transform	PhysioNet PTB	100	100	100
Sharma and Sunkaria[6]	2020	Wavelet transform Extraction energy, entropy, and slope-based as features KNN classification method	PhysioNet PTB	99.40	99.00	98.62
Ketcham and Muankid[7]	2016	R wave detection and QRS complex detection	PhysioNet PTB	77.78	75	80
Pereira and Daimiwal[8]	2016	Wavelet transform-based Features Extraction multiple features from different sub-bands	PhysioNet PTB	70.94	82.14	83.93
Proposed method	2020	Extraction simple morphological features such as Q-wave integral, T-wave integral, and QRS-complex integral from ECG signals Apply some statistical analyses on extracted features GOA-SVM using 3 kernels for classification	PhysioNet PTB	100	100	100

PTB – Physikalisch-Technische Bundesanstalt; SVM – Support vector machine; GOA – Grasshopper optimization algorithm; ECG – Electrocardiogram; PNN – Probabilistic neural network; KNN – K-nearest neighbor; MLP – Multilayer perceptron