Analysis of electrophysiological and mechanical dimensions of swallowing by non-invasive biosignals. (April 2023) - Roldan-Vasco, Sebastian; Restrepo-Uribe, Juan Pablo; Orozco-Duque, Andres; Suarez-Escudero, Juan Camilo; Orozco-Arroyave, Juan Rafael
Abstract: Objective: Alterations in the neuromuscular coordination of swallowing are known as dysphagia, which can produce malnutrition, dehydration and aspiration pneumonia. Its instrumental diagnosis is invasive and expertise dependent. Thus, we introduced a non-invasive multimodal approach for dysphagia screening using surface electromyography (sEMG) and accelerometry-based cervical auscultation (Acc). Methods: Thirty healthy individuals and 30 patients with functional oropharyngeal dysphagia were recruited. Swallowing tasks of saliva and 5, 10, and 20 mL of yogurt and water were performed. Supra- and infrahyoid sEMG and tri-axial Acc signals were recorded. Linear and non-linear features were extracted and selected. Two unimodal and one multimodal classification scenarios were tested. Classical algorithms were applied and the Area Under the ROC curve (AUC) was the criterion for hyperparameters optimization. Results: The Acc related features were the most consistently selected. Although the classification results with Acc signals were higher than with sEMG, the signal fusion improved the unimodal results regardless of swallowing task (AUC > 0.82). The highest classification results were achieved with small volumes of water (AUC = 0.86 ± 0.15) and yogurt (AUC = 0.87 ± 0.12). Conclusion: The combination of non-invasive sEMG and Acc signals improves the performance of automatic classification models for dysphagia detection. Significance: This paper proposes a multimodal approach based on electrophysiological and mechanical swallowing dimensions, for automatic, non-invasive and quantitative dysphagia screening. Highlights: The evaluation of dysphagia is made by invasive and examiner's dependent methods. Electrophysiological and mechanical dimensions of swallowing were analyzed together. We found potential biomarkers of dysphagia from sEMG and accelerometry signals. The multimodal analysis of swallowing improves the automatic dysphagia screening. Machine learning is suitable to detect dysphagic recordings non-invasively