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

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