A DECISION SUPPORT SYSTEM FOR THE DIAGNOSIS OF HEART VALVE DISEASES

Özet

In this pa per, a decision s up port system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with the feature extraction from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Wavelet transforms and power spectrum estimate by Yule-Walker AR method are used to feature extract from the Doppler signals on the time­frequency domain. Wavelet entropy method is applied to these features. The back-propagation neural network is used to classify the extracted features. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective to detect Doppler heart sounds. The correct classification rate was about 84°/o for normal subjects and 95.9°/o for abnormal subjects.