title: A classification system for stenosis from mitral valve Doppler signals using adaptive network based fuzzy inference system.
abstract: In this study, cardiac Doppler parameters were studied in 60 patients with mitral valve stenosis and compared with 41 ages and sex matched healthy controls. 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 and data set depicted from sonogram envelopes were acquired. Finally, the processed data set are applied to the proposed adaptive network based fuzzy inference system (ANFIS) model has potential in classifying the mitral valve Doppler signals. 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. The proposed ANFIS model combined the neural network with adaptive capabilities and qualitative approach of fuzzy logic. The obtained results show that 98% correct classification was achieved, whereas two false classifications have been observed for the test group of 101 people.
authors.affiliation: Erciyes University Biomedical Engineering Department (Biomedical Research Group), Kayseri 38039, Turkey. tokmakci@erciyes.edu.tr
authors.forename: Mahmut
authors.initials: M
authors.lastname: Tokmakçi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D015150:Echocardiography, Doppler; D005260:Female; D005583:Fourier Analysis; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D008946:Mitral Valve Stenosis / Q000145:classification* / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D011446:Prospective Studies