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

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.
Source: Dysphagia - Category: Speech-Language Pathology Source Type: research