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Dysphagia. 2018 Jun;33(3):345-357. doi: 10.1007/s00455-017-9859-2. Epub 2017 Nov 2.

Evaluation of an Automated Swallow-Detection Algorithm Using Visual Biofeedback in Healthy Adults and Head and Neck Cancer Survivors.

Author information

1
Department of Communication Sciences and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, 8205 114St 2-70 Corbett Hall, Edmonton, AB, T6R 3T5, Canada.
2
Institute for Reconstructive Sciences in Medicine (iRSM), Misericordia Community Hospital, 1W-02, 16940-87 Avenue, Edmonton, AB, Canada.
3
Department of Communication Sciences and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, 8205 114St 2-70 Corbett Hall, Edmonton, AB, T6R 3T5, Canada. jana.rieger@ualberta.ca.
4
Institute for Reconstructive Sciences in Medicine (iRSM), Misericordia Community Hospital, 1W-02, 16940-87 Avenue, Edmonton, AB, Canada. jana.rieger@ualberta.ca.

Abstract

Mobile health (mHealth) technologies may offer an opportunity to address longstanding clinical challenges, such as access and adherence to swallowing therapy. Mobili-T® is an mHealth device that uses surface electromyography (sEMG) to provide biofeedback on submental muscles activity during exercise. An automated swallow-detection algorithm was developed for Mobili-T®. This study evaluated the performance of the swallow-detection algorithm. Ten healthy participants and 10 head and neck cancer (HNC) patients were fitted with the device. Signal was acquired during regular, effortful, and Mendelsohn maneuver saliva swallows, as well as lip presses, tongue, and head movements. Signals of interest were tagged during data acquisition and used to evaluate algorithm performance. Sensitivity and positive predictive values (PPV) were calculated for each participant. Saliva swallows were compared between HNC and controls in the four sEMG-based parameters used in the algorithm: duration, peak amplitude ratio, median frequency, and 15th percentile of the power spectrum density. In healthy participants, sensitivity and PPV were 92.3 and 83.9%, respectively. In HNC patients, sensitivity was 92.7% and PPV was 72.2%. In saliva swallows, HNC patients had longer event durations (U = 1925.5, p < 0.001), lower median frequency (U = 2674.0, p < 0.001), and lower 15th percentile of the power spectrum density [t(176.9) = 2.07, p < 0.001] than healthy participants. The automated swallow-detection algorithm performed well with healthy participants and retained a high sensitivity, but had lowered PPV with HNC patients. With respect to Mobili-T®, the algorithm will next be evaluated using the mHealth system.

KEYWORDS:

Deglutition; Deglutition disorders; Dysphagia; Head and neck cancer; Mobile health; Surface electromyography; Swallow recognition; Visual biofeedback

PMID:
29098398
DOI:
10.1007/s00455-017-9859-2
[Indexed for MEDLINE]

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