Automatic sleep stage classification: From classical machine learning methods to deep learning
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http://hdl.handle.net/10045/123563
Títol: | Automatic sleep stage classification: From classical machine learning methods to deep learning |
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Autors: | Sekkal, Rym Nihel | Bereksi-Reguig, Fethi | Ruiz-Fernandez, Daniel | Dib, Nabil | Sekkal, Samira |
Grups d'investigació o GITE: | Ingeniería Bioinspirada e Informática para la Salud |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Sleep stage classification | EEG | Data preprocessing | Features selection | Machine learning | LSTM |
Àrees de coneixement: | Arquitectura y Tecnología de Computadores |
Data de publicació: | 11-de maig-2022 |
Editor: | Elsevier |
Citació bibliogràfica: | Biomedical Signal Processing and Control. 2022, 77: 103751. https://doi.org/10.1016/j.bspc.2022.103751 |
Resum: | Background and objectives: The classification of sleep stages is a preliminary exam that contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time-consuming task when conducted manually by experts. Many studies explored ways of automating polysomnogram signals analysis. They are based on two main strategies: conventional machine learning and deep learning methods. The objective of this work is to carry out a comparative study on these two classes of models. Method: A primary comparison of performance of these classifiers is carried out using eight conventional machine learning algorithms and a feed-forward neural networks to assess whether this latter method have definitely supplanted the first. As sleep epochs show inter-epochs correlation, a study of the distinctive influence of this temporal dependence on the classifiers performance is then conducted introducing for this purpose (uni- and bi-directional) long short-term memory networks. In a context of generalization of the use of wearable devices, a comparison of the classification methods examined is also carried out in their accuracy when dealing with a reduced number of channels. Finally, the robustness of the results obtained to the choice of features selection algorithms is discussed. Results and conclusion: Our results show that support vector machine with radial basis function and random forest are just as valid for predicting sleep stages classification as feature-based neural networks with performance closed to the state of the art. This conclusion remains valid even after the introduction of inter-epochs temporal dependence, reduction of the number of channels or change in features selection method. |
URI: | http://hdl.handle.net/10045/123563 |
ISSN: | 1746-8094 (Print) | 1746-8108 (Online) |
DOI: | 10.1016/j.bspc.2022.103751 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2022 Elsevier Ltd. |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.1016/j.bspc.2022.103751 |
Apareix a la col·lecció: | INV - IBIS - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Sekkal_etal_2022_BiomedSignalProcessControl_final.pdf | Versión final (acceso restringido) | 1,95 MB | Adobe PDF | Obrir Sol·licitar una còpia |
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