Electromyography- and Bioimpedance-Based Detection of Swallow Onset for the Control of Dysphagia Treatment

Abstract

Several studies support the benefits of biofeedback and Functional Electrical Stimulation (FES) in dysphagia therapy. Most commonly, adhesive electrodes are placed on the submental region of the neck to conduct Electromyography (EMG) measurements for controlling gamified biofeedback and functional electrical stimulation. Due to the diverse origin of EMG activity at the neck, it can be assumed that EMG measurements alone do not accurately reflect the onset of the pharyngeal swallowing phase (onset of swallowing). To date, no study has addressed the timing and detection performance of swallow onsets on a comprehensive database including dysphagia patients. This study includes EMG and BioImpedance (BI) measurements of 41 dysphagia patients to compare the timing and performance in the Detection of Swallow Onsets (DoSO) using EMG alone versus combined BI and EMG measurements. The latter approach employs a BI-based data segmentation of potential swallow onsets and a machine-learning-based classifier to distinguish swallow onsets from non-swallow events. Swallow onsets labeled by an expert serve as a reference. In addition to the F1 score, the mean and standard deviation of the detection delay regarding reference events have been determined. The EMG-based DoSO achieved an F1 score of 0.289 with a detection delay of 0.018 s ± 0.203 s. In comparison, the BI/EMG-based DoSO achieved an F1 score of 0.546 with a detection delay of 0.033 s ± 0.1 s. Therefore, the BI/EMG-based DoSO has better timing and detection performance compared to the EMG-based DoSO and potentially improves biofeedback and FES in dysphagia therapy.

Keywords

• dysphagia
• swallow onset detection
• biofeedback
• functional electrical stimulation
• machine learning