Authors
Lian Wang, Nannan Cui, Xiaozhen Li, Jia Qiao, Zhenhai Wei, Zulin Dou, Yanxia Liu, Xiaomei Wei
Published in
Digital health. Volume 12. Pages 20552076261465164. Epub Jun 27, 2026.
Abstract
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.
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.
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.
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.
PMID:
42371600
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.
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