title: Classification of normal and dysphagic swallows by acoustical means.
abstract: This paper proposes a noninvasive, acoustic-based method to differentiate between individuals with and without dysphagia or swallowing dysfunction. Swallowing sound signals, both normal and abnormal (i.e., at risk of some degree of dysphagia) were recorded with accelerometers over the trachea. Segmentation based on waveform dimension trajectory (a distance-based technique) was developed to segment the nonstationary swallowing sound signals. Two characteristic sections emerged, Opening and Transmission, and 24 characteristic features were extracted and subsequently reduced via discriminant analysis. A discriminant algorithm was also employed for classification, with the system trained and tested using the leave-one-out approach. Overall, 350 signals were used from three bolus consistencies (semisolid, thick and thin liquids). A final screening algorithm correctly classified 13 of 15 control subjects and 11 of 11 subjects with some degree of dysphagia and/or neurological impairments. The proposed method has great potential to reduce the need for videofluoroscopic swallowing studies (the current gold standard method for swallowing assessment, which is invasive and nonportable) and to assist in the overall clinical assessment of swallowing sound signals.
authors.affiliation: Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6 Canada. lisa.lazareck@eng.ox.ac.uk
authors.forename: Lisa J
authors.initials: LJ
authors.lastname: Lazareck
authors.forename: Zahra M K
authors.initials: ZM
authors.lastname: Moussavi
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D003679:Deglutition; D003680:Deglutition Disorders / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017585:Ultrasonography, Doppler, Transcranial / Q000379:methods*