Dysphagia (swallowing disorder) is a common health problem that degrades the quality of life of many people. The videofluoroscopic swallowing study (VFSS) is the current gold standard in dysphagia assessment but is associated with high cost, long wait times, and a lack of portability. As a result, there is a pining need for an alternative technique that can serve day-to-day monitoring of dysphagia as well as screening for VFSS referral. The primary objective of this thesis was to investigate three non-invasive signal modalities, namely dual-axis accelerometry, submental mechanomyography (MMG), and nasal airflow, for their potential as alternatives to VFSS. To this end, signals were acquired from 17 healthy individuals and 24 patients with dysphagia, with various stimuli. In a characterization study, the anterior-posterior (A-P) and superior-inferior (S-I) axes in dual-axis accelerometry were found to contain non-overlapping information about swallowing, justifying the extension of single-axis (A-P only) to dual-axis (A-P and S-I) accelerometry. Also, several dual-axis accelerometry signal features were found to be stimulus dependent, and the observed stimulus effects were linked to slower swallowing function with increasing bolus viscosity. Age and stimulus effects on submental MMG were scrutinized, as an analogy to previous electromyography (EMG) studies of similar design. Similarities to EMG confirmed the validity of MMG as a muscle activity measurement tool in swallowing research. Automatic swallow segmentation, which is a crucial precursory step to swallow diagnosis, was investigated with artificial neural networks. Segmentation performance was shown to improve as more signal modalities were included, verifying the value of multi-sensor fusion. When all signal modalities were utilized, an adjusted accuracy of 89.6% was achieved. Automatic discrimination between healthy and abnormal swallows was investigated in two studies. Using previously collected pediatric data, a radial basis classifier based only on A-P accelerometry resulted in an adjusted accuracy of 81.3% in aspiration detection. In an adult study, linear discriminant classifiers resulted in adjusted accuracies of 74.7%, 83.7%, and 84.2% for aspiration, valleculae residue, and pyriform sinus residue detection, respectively. It was concluded that the three signal modalities analyzed in this thesis possess promising potential for abnormal swallow detection.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/26467 |
| Date | 08 March 2011 |
| Creators | Lee, Joonwu |
| Contributors | Chau, Tom, Steele, Catriona |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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