Swallowing disorders analysis using surface EMG biomarkers and classification models

Roldan-Vasco, Sebastian; Duque, Andrés; Orozco-Arroyave, Juan Rafael

doi:10.1016/j.dsp.2022.103815

Cited by 8 publications

(6 citation statements)

References 47 publications

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“…The VFSS poses radiation risks, may be difficult to access, and is time-consuming, reliant on clinician expertise, and costly, driving the search for non-invasive, quantitative approaches [52].…”

Section: Videofluoroscopic Swallowing Study (Vfss)mentioning

confidence: 99%

“…Swallowing involves a sequence of voluntary and involuntary muscle contractions. Dysfunction in muscles associated with swallowing can lead to difficulty in performing this critical function [52]. Muscle contraction and relaxation generate weak bioelectrical signals, known as myosignals, that are produced by the electrical activity of neurons within the muscles.…”

Section: Electromyography (Emg)mentioning

confidence: 99%

“…It is predominantly used to assess the electrophysiological impacts of bolus volume and consistency. Although sEMG can be analyzed visually and through amplitude-based measurements, it is subjective, time-intensive, hard to replicate, and prone to amplitude fluctuations [52].…”

Section: Electromyography (Emg)mentioning

confidence: 99%

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Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Wu,

Guo,

Chu

et al. 2024

Bioengineering

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Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes.

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Section: Videofluoroscopic Swallowing Study (Vfss)mentioning

confidence: 99%

Section: Electromyography (Emg)mentioning

confidence: 99%

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Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Wu,

Guo,

Chu

et al. 2024

Bioengineering

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“…In machine learning, algorithms are trained to find patterns and correlations in large data sets and make the best decisions and predictions based on this analysis [15]. Machine learning algorithms are one of the extremely popular methods applied to classification and regression problems in many fields, such as medicine [16,17], engineering [18,19], economy [20], education [21,22], business [23,24], natural sciences [25,26], sport sciences [27] and agriculture [28,29]. Alkali et al (2014) [30] utilized an artificial neural network (ANN) to predict some mechanical properties of melon fruit.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods

Ercan,

Kabas,

Moiceanu

2024

Applied Sciences

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Alfalfa holds an extremely significant place in animal nutrition when it comes to providing essential nutrients. The leaves of alfalfa specifically boast the highest nutritional value, containing a remarkable 70% of crude protein and an impressive 90% of essential vitamins. Due to this incredible nutritional profile, it becomes exceedingly important to ensure that the harvesting and threshing processes are executed with utmost care to minimize any potential loss of these invaluable nutrients present in the leaves. To minimize losses, it is essential to accurately determine the resistance of the leaves in both their green and dried forms. This study aimed to estimate the breaking resistance of green and dried alfalfa plants using machine learning methods. During the modeling phase, five different popular machine learning methods, Extra Trees (ET), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boosting (XGB), and CatBoost (CB), were used. The correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics were used to evaluate the models. The obtained metric results and the graphs obtained from the prediction values of the models revealed that the machine learning methods made successful predictions. The best R2 (0.9853), RMSE (0.0171), MAE (0.0099) and MAPE (0.0969) values for the dry alfalfa plant were obtained from the model established with the ET method, while the best RMSE (0.0616) and R2 (0.96) values for the green alfalfa plant were obtained from the model established with the RF method and the best MAE (0.0340) value was obtained from the model established with the ET method. Additionally, the best MAPE (0.1447) value was obtained from the model established with the GB method.

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“… 13 In addition, surface EMG improves the performance of automatic classification models for dysphagia detection. 14 – 17 …”

Section: Introductionmentioning

confidence: 99%

Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study

et al. 2023

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Objective This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Methods Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex–brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Results Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. Conclusion The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.

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Swallowing disorders analysis using surface EMG biomarkers and classification models

Cited by 8 publications

References 47 publications

Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review

Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods

Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study

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