A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals

• Published in: Computers in biology and medicine Vol. 59; pp. 10 - 18
• Main Authors: Dudik, Joshua M., Kurosu, Atsuko, Coyle, James L., Sejdić, Ervin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2015; Elsevier Limited
• Subjects: Accelerometry - methods; Accuracy; Algorithms; Auscultation - methods; Biomechanics; Cervical auscultation; Cluster Analysis; Deglutition - physiology; Deglutition Disorders - physiopathology; Dysphagia; Humans; Internal Medicine; Kinematics; Methods; Monitoring, Physiologic - methods; Neck - physiology; Novelty detection; Other; Patients; Physiology; Segmentation; Signal Processing, Computer-Assisted; Studies; Swallowing accelerometry; Swallowing accelerometry; Dysphagia; Segmentation; Cervical auscultation; Novelty detection
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2015.01.007

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. •Three swallowing segmentation algorithms were compared.•The new DBSCAN algorithm can be used to segment swallowing vibrations.•The DBSCAN algorithm is outright superior to the k-means algorithm.•The DBSCAN algorithm has similar sensitivity to the quadratic variation algorithm.•The DBSCAN algorithm is the fastest and most easily applied to general data.