Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics

Abstract

Abstract Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical‐grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami‐structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high‐quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post‐stroke patients captures the system's significance in measuring multiple physiological signals in real‐time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non‐invasive alternative for monitoring swallowing and aspiration events.

Keywords

• dysphagia
• kirigami structure
• machine learning
• silent aspiration
• swallowing disorder
• wearable electronics