Classification of mitral stenosis from Doppler signals using short time Fourier transform and artificial neural networks

Abstract

Doppler ultrasound is the most widely used diagnostic tool for assessing intracardiac blood flow. In this study diagnostic Doppler ultrasound signals were acquired from mitral valve of 125 patients referred to our institution and 55 healthy volunteers. Firstly the sonograms which represent the changes in Doppler frequency with respect to time were performed from mitral valve Doppler signals using short time Fourier transformation (STFT) method. Secondly the envelopes of these sonograms acquired and data set depicted from sonogram envelopes were applied to artificial neural networks (ANN) as input data. After the training phase, testing of the Levenberg Marquard (LM) backpropagation neural network was established. The overall results show that 97.8% correct classification was achieved, whereas 2 false classifications have been observed for the group of 92 people in test group. This result confirms that our technique contribute to the detection of mitral valve stenosis and our method offers more reliable information than looking at the sonogram on the Doppler screen and making a decision from the visual inspection.