Machine Learning Automated Analysis Applied to Mandibular Jaw Movements During Sleep: A Window on Polysomnography

Martinot, Jean-Benoit;Le-Dong, Nhat-Nam;Pépin, Jean-Louis
(2024) Springer Optimization and Its Applications : Handbook of AI and Data Sciences for Sleep Disorders — ISBN: [9783031682629], p. 259-274, submitted

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  • Martinot, Jean-BenoitUCLouvain
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  • Le-Dong, Nhat-Nam
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  • Pépin, Jean-Louis
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Abstract
The application of data science and artificial intelligence (AI) to bio-signal analysis is not necessarily a revolutionary process, but rather follows on from studies in medical physiology. Thus, the use of data science and AI in bio-signal analysis is just one step further along the journey of studying the physio-pathological processes captured by bio-signals such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG). Most machine learning experiments on bio-signals have been conducted by a shortcut, where data scientists have direct access to a public or experimental bio-signaldatasetandthenattempttofindtheoptimalsolutionforaspecialclassification task, such as sleep staging identification or apneas and hypopneas detection during sleep. Using this approach, the physiological relationship between the signal under investigation and the targeted outcomes is implicitly accepted. In contrast, the application of mandibular jaw movements (MJM) as a surrogate bio-signal in sleep medicine is a special case. MJM is a completely new type of bio-signal and therefore
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Martinot, J.-B., Le-Dong, N.-N., & Pépin, J.-L. (2024). Machine Learning Automated Analysis Applied to Mandibular Jaw Movements During Sleep: A Window on Polysomnography. In Berry, Richard B. ; Pardalos, Panos M. ; Xian, Xiaochen (eds.) (ed.), Springer Optimization and Its Applications : Handbook of AI and Data Sciences for Sleep Disorders (p. p. 259-274). https://doi.org/10.1007/978-3-031-68263-6_10