Dysphagia screening with sEMG, accelerometry and speech: Multimodal machine and deep learning approaches

Dysphagia is a swallowing disorder that affects food, liquid, or saliva transit from the mouth to the stomach. Dysphagia leads to malnutrition, dehydration, and aspiration of the bolus into the respiratory system, which can lead to pneumonia with subsequent death. Clinically accepted dysphagia diagnosis and follow-up methods are invasive, uncomfortable, expensive, and experience-dependent. This paper explores a multimodal non-invasive approach to objectively assess dysphagia with three biosignals: surface electromyography, accelerometry-based cervical auscultation, and speech. The defined acquisition protocol was applied to patients with dysphagia and healthy control subjects. Features were extracted from the three biosignals in different domains with the aim of proposing interpretable biomarkers. Finally, the methodology was evaluated according to the accuracy and area under the receiver operating characteristic curve obtained with different classifiers. According to our results, all signals demonstrated their suitability for dysphagia screening, specially speech and multi-modal scenarios evaluated with machine learning models and also with Gated Multimodal Units. This paper contributes to reducing the knowledge gap about swallowing-related phenomena and incorporates non-invasive and multi-modal methods with high potential to be transferred and implemented in clinical practice.