An application of wavelet neural network for classification of patients with coronary artery disease based on HRV analysis

Presents some recently obtained results concerning the possibility of application of wavelet neural networks (WNN) for classification purposes in the case of patients with coronary artery disease of different levels. Patients with respectively one, two and three coronary arteries blocked have been taken into consideration. The Heart Rate Variability signal has been registered for 5 minutes for each of such patients. All the patients have been previously preliminary classified by an experienced cardiologist with regard to the estimation of the number of coronary arteries blocked. Then half of each HRV record has been applied for teaching the neural network after features selection from raw HRV through the application of a wavelet transform being the first layer of the WNN system. The second half of data has been used for classification. Due to the fact that four classification groups were expected the output layer of the neural network has only two output neurons.