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Unsupervised Sleep Stages Classification Based on Physiological Signals

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Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection (PAAMS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12092))

Abstract

Automatic sleep scoring has, recently, captured the attention of authors due to its importance in sleep abnormalities detection and treatments. The majority of the proposed works are based on supervised learning and considered mostly a single physiological signal as input. To avoid the exhausting pre-labeling task and to enhance the precision of the sleep staging process, we propose an unsupervised classification model for sleep stages identification based on a flexible architecture to handle different physiological signals. The efficiency of our approach was investigated using real data. Promising results were reached according to a comparative study carried out with the often used classification models.

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Authors and Affiliations

  1. ISGT, SMART Lab, Université de Tunis, 2000, Le Bardo, Tunisia

    Rahma Ferjani, Lilia Rejeb & Lamjed Ben Said

Authors
  1. Rahma Ferjani
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  2. Lilia Rejeb
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  3. Lamjed Ben Said
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Corresponding author

Correspondence to Rahma Ferjani .

Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Grenoble, France

    Yves Demazeau

  2. Catholic University of Leuven, Heverlee, Belgium

    Tom Holvoet

  3. University of Salamanca, Salamanca, Spain

    Juan M. Corchado

  4. University of L’Aquila, L'Aquila, Italy

    Stefania Costantini

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Ferjani, R., Rejeb, L., Said, L.B. (2020). Unsupervised Sleep Stages Classification Based on Physiological Signals. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-49778-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49777-4

  • Online ISBN: 978-3-030-49778-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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