Lisa Lazarek, M.Sc. 

Department of Electrical and Computer Engineering, University of Manitoba
 

Classification of normal and dysphagic swallows by acoustical means
This research proposes a non-invasive, 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 degrees of dysphagia) were recorded with accelerometers over the trachea. Segmentation based on waveform dimension trajectory (WDT, a distance-based technique) was developed to segment the non-stationary swallowing sound signals. Two characteristic sections emerged, Opening and Transmission, and 24 characteristic features were extracted and subsequently reduced via discriminant analyses. 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 neurological impairments. The proposed method has great potential to reduce the need for videofluoroscopic swallowing studies (current gold standard method for swallowing assessment, which is invasive and non-portable) and the overall clinical assessment of swallowing sound signals.

Biography

Lisa graduated with her Master of Science and Bachelor of Science in Electrical Engineering from the University of Manitoba in September 2003 and May 2001, respectively. Her most current research involves the study of swallowing sound signals and its segmentation into characteristic segments. More specifically, her research includes investigation of the key features per characteristic segments, examination of fractals and classification methods (neural networks, rough sets), and comparison of normal and abnormal swallowing sound signal results. Lisa will be pursuing doctoral studies at the University of Oxford in October 2003.