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A Comparison of Feature Ranking and Rank Aggregation Techniques in Automatic Sleep Stage Classification Based on Polysomnographic Signals

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Bioinformatics and Biomedical Engineering (IWBBIO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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Abstract

Sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using multi-channel recordings like polysomnographic (PSG) signals is an effective way of assessing sleep quality. However, manual sleep stage classification is time-consuming, tedious and highly subjective. To overcome this, automatic sleep classification was proposed, in which pre-processing, feature extraction and classification are the three main steps. Since the classification accuracy is deeply affected by the features selection, in this paper several feature selection methods as well as rank aggregation methods are compared. Feature selection methods are evaluated by three criteria: accuracy, stability and similarity. For classification two different classifiers (k-nearest neighbor and multilayer feedforward neural network) were utilized. Simulation results show that MRMR-MID achieves highest classification performance while Fisher method provides the most stable rankings.

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Acknowledgment

This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS and INCENTIVO/EEI/UI0066/2014 of UNINOVA.

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Authors and Affiliations

  1. Computational Intelligence Group of CTS/UNINOVA, Caparica, Portugal

    Shirin Najdi, Ali Abdollahi Gharbali & José Manuel Fonseca

  2. Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus da Caparica, Quinta da Torre, Monte de Caparica, Portugal

    Shirin Najdi, Ali Abdollahi Gharbali & José Manuel Fonseca

Authors
  1. Shirin Najdi
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  2. Ali Abdollahi Gharbali
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  3. José Manuel Fonseca
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Correspondence to Shirin Najdi .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Najdi, S., Gharbali, A.A., Fonseca, J.M. (2016). A Comparison of Feature Ranking and Rank Aggregation Techniques in Automatic Sleep Stage Classification Based on Polysomnographic Signals. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-31744-1

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