Multi-sensor fusion for differentiating swallows between healthy adults and patients with post-stroke dysphagia.

Objective: The aim of this study was to develop a non-invasive method using multi-sensor fusion to discriminate between abnormal swallows in patients with post-stroke dysphagia and normal swallows in healthy individuals. Methods: Acceleration signals, nasal airflow signals, and sound signals were obtained from 108 healthy adults and 108 post-stroke dysphagia patients. Each swallowing signal was segmented according to videofluoroscopic swallowing study (VFSS), followed by features extraction and selection. Support Vector Machine, Decision Tree, K-Nearest Neighbor, Naïve Bayes, and Logistic Regression models were employed to discriminate between normal swallows in healthy individuals and abnormal swallows in post-stroke dysphagia patients. Results: Overall, classification models utilizing signals from multi-sensor demonstrated superior performance when compared to signals from single-sensor and dual-sensor. Among the five models, the Support Vector Machine model (accuracy: 91.39±3.03%; specificity: 89.53±4.09%; sensitivity: 92.99±2.84%; F1-score: 91.86±2.92%) and Logistic Regression model (accuracy: 91.00±2.71%; specificity: 89.88±3.77%; sensitivity: 91.97±2.70%; F1-score: 91.45±2.55%) have better overall performance for classification. Conclusions: The multi-sensor fusion has shown promising ability in differentiating swallows in healthy adults from those in patients with post-stroke dysphagia. The findings may provide an important foundation for the use of non-invasive multi-sensor fusion methods to identify dysphagia.