Continuous Estimation of Swallowing Motion With EMG and MMG Signals
Oropharyngeal dysphagia (OD) is a symptom of swallowing dysfunction that is associated with aspiration, severe respiratory complications, and even death. OD is a highly prevalent condition in populations including the elderly and patients with neurological diseases (e.g., stroke and Parkinson&#x...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10879466/ |
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| author | Zhenhui Guo Ziyang Wang Yue Wang Weiguang Huo Jianda Han |
| author_facet | Zhenhui Guo Ziyang Wang Yue Wang Weiguang Huo Jianda Han |
| author_sort | Zhenhui Guo |
| collection | DOAJ |
| description | Oropharyngeal dysphagia (OD) is a symptom of swallowing dysfunction that is associated with aspiration, severe respiratory complications, and even death. OD is a highly prevalent condition in populations including the elderly and patients with neurological diseases (e.g., stroke and Parkinson’s disease (PD)). Assessment of swallow function is crucial for managing OD, yet depends on devices for long-term monitoring during daily life and relevant methods for accurately assessing swallow function. The videofluoroscopic swallowing study (VFSS) is usually considered a gold standard method. However, it has several limitations, such as radiation exposure, the need for technical experts, high cost, and clinical use only. This study investigates the performances of electromyography (EMG) and mechanomyography (MMG) signals, which can be easily measured using wearable sensors, to continuously estimate swallowing movement. Meanwhile, three methods, i.e., Gaussian process regression (GPR), LSTM, and random forest (RF), are used for swallowing motion estimation based on EMG/MMG signals measured from six healthy subjects and a patient with PD, respectively. Moreover, a depth camera-based approach is proposed to provide the reference laryngeal displacement (i.e., the swallowing movement). The experimental results show that EMG models with three machine learning methods can accurately estimate swallowing movement. For the healthy subjects, the mean correlation coefficient (CC) is about 0.90 and the normalized root mean square error (NRMSE) is less than 0.15. For the PD patient, the CC is 0.804 and the NRMSE is 0.205 when using RF. The performance of the MMG model is comparable to that of EMG: CC/NRMSE of the LSTM model is 0.844/0.150 (healthy subjects); CC/NRMSE of RF model is 0.727/0.204 (PD patient). To the best of our knowledge, this is the first study proving that both EMG and MMG are two effective means for an accurate continuous estimation of swallowing motion, enabling the possibility of a safe and convenient evaluation and management of OD. |
| format | Article |
| id | doaj-art-39c844e588014898ab6991a98e2ca3aa |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-39c844e588014898ab6991a98e2ca3aa2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013378779710.1109/TNSRE.2025.354084210879466Continuous Estimation of Swallowing Motion With EMG and MMG SignalsZhenhui Guo0Ziyang Wang1Yue Wang2Weiguang Huo3https://orcid.org/0000-0002-7370-5189Jianda Han4https://orcid.org/0000-0002-9664-4534College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, ChinaCollege of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, ChinaDepartment of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin, ChinaCollege of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, ChinaCollege of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, ChinaOropharyngeal dysphagia (OD) is a symptom of swallowing dysfunction that is associated with aspiration, severe respiratory complications, and even death. OD is a highly prevalent condition in populations including the elderly and patients with neurological diseases (e.g., stroke and Parkinson’s disease (PD)). Assessment of swallow function is crucial for managing OD, yet depends on devices for long-term monitoring during daily life and relevant methods for accurately assessing swallow function. The videofluoroscopic swallowing study (VFSS) is usually considered a gold standard method. However, it has several limitations, such as radiation exposure, the need for technical experts, high cost, and clinical use only. This study investigates the performances of electromyography (EMG) and mechanomyography (MMG) signals, which can be easily measured using wearable sensors, to continuously estimate swallowing movement. Meanwhile, three methods, i.e., Gaussian process regression (GPR), LSTM, and random forest (RF), are used for swallowing motion estimation based on EMG/MMG signals measured from six healthy subjects and a patient with PD, respectively. Moreover, a depth camera-based approach is proposed to provide the reference laryngeal displacement (i.e., the swallowing movement). The experimental results show that EMG models with three machine learning methods can accurately estimate swallowing movement. For the healthy subjects, the mean correlation coefficient (CC) is about 0.90 and the normalized root mean square error (NRMSE) is less than 0.15. For the PD patient, the CC is 0.804 and the NRMSE is 0.205 when using RF. The performance of the MMG model is comparable to that of EMG: CC/NRMSE of the LSTM model is 0.844/0.150 (healthy subjects); CC/NRMSE of RF model is 0.727/0.204 (PD patient). To the best of our knowledge, this is the first study proving that both EMG and MMG are two effective means for an accurate continuous estimation of swallowing motion, enabling the possibility of a safe and convenient evaluation and management of OD.https://ieeexplore.ieee.org/document/10879466/EMGMMGlaryngeal displacementmotion estimation |
| spellingShingle | Zhenhui Guo Ziyang Wang Yue Wang Weiguang Huo Jianda Han Continuous Estimation of Swallowing Motion With EMG and MMG Signals IEEE Transactions on Neural Systems and Rehabilitation Engineering EMG MMG laryngeal displacement motion estimation |
| title | Continuous Estimation of Swallowing Motion With EMG and MMG Signals |
| title_full | Continuous Estimation of Swallowing Motion With EMG and MMG Signals |
| title_fullStr | Continuous Estimation of Swallowing Motion With EMG and MMG Signals |
| title_full_unstemmed | Continuous Estimation of Swallowing Motion With EMG and MMG Signals |
| title_short | Continuous Estimation of Swallowing Motion With EMG and MMG Signals |
| title_sort | continuous estimation of swallowing motion with emg and mmg signals |
| topic | EMG MMG laryngeal displacement motion estimation |
| url | https://ieeexplore.ieee.org/document/10879466/ |
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