title: Automatic discrimination between safe and unsafe swallowing using a reputation-based classifier.
abstract: BACKGROUND
  Swallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations.
  
  
  METHODS
  In this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 ± 13.4 years, 15 male) referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway) and 60 as safe swallows. Three separate support vector machine (SVM) classifiers and eight different features were selected for classification.
  
  
  RESULTS
  With selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 ± 5.0%), high sensitivity (97.1 ± 2%) and modest specificity (64 ± 8.8%). Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static reputation-based algorithm outperformed the democratic majority voting algorithm on this clinical data set.
  
  
  CONCLUSIONS
  Given its computational efficiency and high sensitivity, reputation-based classification of dual-axis accelerometry ought to be considered in future developments of a point-of-care swallow assessment where clinical informatics are desired.
authors.affiliation: Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.
authors.forename: Mohammad S
authors.initials: MS
authors.lastname: Nikjoo
authors.forename: Catriona M
authors.initials: CM
authors.lastname: Steele
authors.forename: Ervin
authors.initials: E
authors.lastname: Sejdić
authors.forename: Tom
authors.initials: T
authors.lastname: Chau
mesh_terms: D000054:Acceleration; D000328:Adult; D000368:Aged; D001331:Automation; D003679:Deglutition / Q000502:physiology*; D003680:Deglutition Disorders / Q000175:diagnosis / Q000503:physiopathology; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D012189:Retrospective Studies; D012449:Safety; D012815:Signal Processing, Computer-Assisted
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