여민경 (충남대학교)

한빛사논문

조회133

충남대학교

Adv. Sci., Jul 09 2024, | https://doi.org/10.1002/advs.202404211 Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics

Beomjune Shin ^1,2, Sung Hoon Lee ^2,3, Kangkyu Kwon ^2,3, Yoon Jae Lee ^2,3, Nikita Crispe^2,4, So-Young Ahn⁵, Sandeep Shelly⁶, Nathaniel Sundholm ⁶, Andrew Tkaczuk ⁶, Min-Kyung Yeo⁷, Hyojung J. Choo⁸, Woon-Hong Yeo ^1,2,4,9

¹George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

²Wearable Intelligent Systems and Healthcare Center (WISH Center), Institute for Matter and Systems, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

³School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

⁴Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, 30332, USA.

⁵Department of Rehabilitation Medicine, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.

⁶Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Emory University, Atlanta, GA, 30322, USA.

⁷Department of Pathology, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.

⁸Department of Cell Biology, School of Medicine, Emory University, Atlanta, GA, 30322, USA.

⁹Parker H. Petit Institute for Bioengineering and Biosciences, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

B.S., S.H.L., and K.K. contributed equally to this work.

CORRESPONDING AUTHORS: Min-Kyung Yeo, Hyojung J. Choo, Woon-Hong Yeo

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.

논문정보

형식Research article
게재일2024년 07월 (BRIC 등록일 2024-07-26)
연구진국내(교신)+국외 연구진
분야 바이오·의료융합 > 의료기기

댓글 작성 로그인

CV 열람하기

연락처 보기