Dysphagia

, Volume 33, Issue 3, pp 345–357 | Cite as

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

  • Gabriela Constantinescu
  • Kristina Kuffel
  • Daniel Aalto
  • William Hodgetts
  • Jana Rieger
Original Article

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 

Notes

Acknowledgements

This work was supported by Alberta Cancer Foundation Transformative Program Grant (26355) and Alberta Innovates (AI) Clinician Fellowship (201400350). The team also would like to thank Mr. Fraaz Kamal for developing the custom software used in data acquisition.

Compliance with Ethical Standards

Conflict of interest

Gabriela Constantinescu and Jana Rieger are inventors listed on a patent for the mobile swallowing therapy device. The patent application was made through TEC Edmonton Office, University of Alberta (File Number 2014015. No commercial interest has been shown at this stage).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Communication Sciences and Disorders, Faculty of Rehabilitation MedicineUniversity of AlbertaEdmontonCanada
  2. 2.Institute for Reconstructive Sciences in Medicine (iRSM)Misericordia Community HospitalEdmontonCanada

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