Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network.
There are a variety of electrocardiogram based methods to detect myocardial infarction (MI) patients. This study used the signal averaged electrocardiogram (SAECG) and its wavelet coefficient as an index to detect MI. Orthogonal leads signals from 50 acute myocardial infarction (AMI) and 50 healthy subjects were selected from the national metrology institute of Germany (PTB diagnostic database). They were filtered and discrete wavelet transformed was exerted on them. Four conventional features and two new features introduced in this study were extracted from SAECG and its wavelet decompositions. Finally for data classification, probabilistic neural network were used. This method was able to detect and discriminate AMI patients from healthy subjects using the probabilistic neural network, which shows 93.0% sensitivity at 86.0% specificity with 89.5% accuracy. This technique and the new extracted features showed good promise in the identification of MI patients. However, the sensitivity and specificity is comparable with other findings and has high accuracy although we extracted only 6 features.
Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran ; Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Ahmad
A
Keshtkar
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran.
Hadi
H
Seyedarabi
Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Peyman
P
Sheikhzadeh
Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran ; Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Seyed Hossein
SH
Rasta
Discrete wavelet transform; electrocardiogram; myocardial infarction; probabilistic neural network