Diagnosis of Myocardial Ischemia Using Heart-Rate Variability and Recurrent Probabilistic Neural Network

Myocardial ischemia is caused by a lack of sufficient blood flow to the contractile cells and may lead to myocardial infarction with its severe sequel of heart failure, arrhythmias, and death. Therefore, its early diagnosis and treatment is of great importance. During past several years, several strategies have been proposed for automatic detection of ischemic cardiac beats using the ST-T complex of the electrocardiogram (ECG) which is recorded during long-term Holter monitoring. However, automatic detection of ischemic subjects using short-term analysis of the ECG is an open problem. In this paper, we presented a new method for automated detection of ischemic patients. The study was conducted based on short-term analysis of the ECG, time-frequency analysis of heart-rate variability (HRV) and recurrent log linearized Gaussian mixture neural network (RLLGMN) as the classifier. For this purpose the feature vector was extracted from the energy of HRV was obtained from 98 subjects (50 healthy and 48 patients) at different frequency bands during different autonomic tests (i.e. controlled normal breathing, controlled deep breathing and active transition tests) and classified using a probabilistic neural network. The results showed that a correct classification rate of 82% for the healthy subjects and 86% for the ischemic subjects was achieved using the proposed method. The results indicated that HRV signals and recurrent probabilistic neural network can be used as a noninvasive method for identifying ischemia. The proposed method is simple, fast and noninvasive, and does not require the long-term recording of ECG signals as well as exercise treadmill test.