Feature selection for swallowing sounds classification.
In recent years swallowing sounds analysis have received great attention for observing the abnormalities in swallowing mechanisms. In this paper a comprehensive set of features were extracted from time and frequency domains characteristics of the signals. 111 features were obtained from different parts of swallowing sounds including initial discrete sounds (IDS), bolus transmission sounds (BTS) and the entire swallowing sounds signal (WHL). Reducing the number of features and selecting a set of most important ones is a crucial step in sketching the signal characteristics, observing the signal variations in classification problems. Therefore, in this study features were examined thoroughly and arranged by maximizing the Mahalanobis distances between normal and dysphagic classes. The results indicate low- and high-frequency components represent the main characteristics of the signals for IDS segment of the swallowing sound, while the medium frequency components play the principal role for BTS segment. Different feature subsets with variable number of features were investigated for classifying normal and dysphagic swallowing sound signals. It was found that the overall performances of the feature subset extracted from WHL was superior to the results of the feature subsets extracted from IDS or BTS individually.
Yadollahi
Azadeh
A
Faculty of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada, R3T 5V6. azadeh@ee.umanitoba.ca
Moussavi
Zahra
Z
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001314:Auscultation / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D003679:Deglutition / Q000502:physiology*; D003680:Deglutition Disorders / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*