Keshtkar Ahmad, Seyedarabi Hadi, Sheikhzadeh Peyman, Rasta Seyed Hossein
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.
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran.
J Med Signals Sens. 2013 Oct;3(4):225-30.
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.
有多种基于心电图的方法来检测心肌梗死（MI）患者。本研究使用信号平均心电图（SAECG）及其小波系数作为指标来检测心肌梗死。从德国国家计量研究所（PTB诊断数据库）中选取了50例急性心肌梗死（AMI）患者和50例健康受试者的正交导联信号。对这些信号进行滤波并施加离散小波变换。从SAECG及其小波分解中提取了四个传统特征和本研究中引入的两个新特征。最后，使用概率神经网络进行数据分类。该方法能够使用概率神经网络从健康受试者中检测和区分AMI患者，在特异性为86.0%时灵敏度为93.0%，准确率为89.5%。这项技术和新提取的特征在识别MI患者方面显示出良好的前景。然而，尽管我们只提取了6个特征，但其灵敏度和特异性与其他研究结果相当且具有较高的准确率。
