title: A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals.
abstract: BACKGROUND
  Cervical auscultation with high resolution sensors is currently under consideration as a method of automatically screening for specific swallowing abnormalities. To be clinically useful without human involvement, any devices based on cervical auscultation should be able to detect specified swallowing events in an automatic manner.
  
  
  METHODS
  In this paper, we comparatively analyze the density-based spatial clustering of applications with noise algorithm (DBSCAN), a k-means based algorithm, and an algorithm based on quadratic variation as methods of differentiating periods of swallowing activity from periods of time without swallows. These algorithms utilized swallowing vibration data exclusively and compared the results to a gold standard measure of swallowing duration. Data was collected from 23 subjects that were actively suffering from swallowing difficulties.
  
  
  RESULTS
  Comparing the performance of the DBSCAN algorithm with a proven segmentation algorithm that utilizes k-means clustering demonstrated that the DBSCAN algorithm had a higher sensitivity and correctly segmented more swallows. Comparing its performance with a threshold-based algorithm that utilized the quadratic variation of the signal showed that the DBSCAN algorithm offered no direct increase in performance. However, it offered several other benefits including a faster run time and more consistent performance between patients. All algorithms showed noticeable differentiation from the endpoints provided by a videofluoroscopy examination as well as reduced sensitivity.
  
  
  CONCLUSIONS
  In summary, we showed that the DBSCAN algorithm is a viable method for detecting the occurrence of a swallowing event using cervical auscultation signals, but significant work must be done to improve its performance before it can be implemented in an unsupervised manner.
authors: Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O׳Hara Street, Pittsburgh, PA 15261, USA. Electronic address: jmd151@pitt.edu.
authors: Joshua M
authors: JM
authors: Dudik
authors: Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 4028 Forbes Tower, Pittsburgh, PA 15260, USA.
authors: Atsuko
authors: A
authors: Kurosu
authors: Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 4028 Forbes Tower, Pittsburgh, PA 15260, USA. Electronic address: jcoyle@pitt.edu.
authors: James L
authors: JL
authors: Coyle
authors: Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O׳Hara Street, Pittsburgh, PA 15261, USA. Electronic address: esejdic@ieee.org.
authors: Ervin
authors: E
authors: Sejdić
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000465:Algorithms; D001314:Auscultation / Q000379:methods; D016000:Cluster Analysis; D003679:Deglutition / Q000502:physiology*; D003680:Deglutition Disorders / Q000503:physiopathology; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D009333:Neck / Q000502:physiology; D012815:Signal Processing, Computer-Assisted
keywords: Cervical auscultation; Dysphagia; Novelty detection; Segmentation; Swallowing accelerometry