A comparison of feature ranking and rank aggregation techniques in automatic sleep stage classification based on polysomnographic signals

Shirin Najdi, Ali Abdollahi Gharbali, José Manuel Fonseca

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
EditorsF. Ortuno, I. Rojas
Place of PublicationCham
PublisherSpringer-Verlag
Pages230-241
Number of pages12
ISBN (Electronic)978-3-319-31744-1
ISBN (Print)978-3-319-31743-4
DOIs
Publication statusPublished - 2016
Event4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain
Duration: 20 Apr 201622 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9656
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
CountrySpain
CityGranada
Period20/04/1622/04/16

Keywords

  • Biomedical signal processing
  • Feature ranking
  • Feature selection
  • Polysomnography
  • Rank aggregation
  • Sleep stage classification

Fingerprint Dive into the research topics of 'A comparison of feature ranking and rank aggregation techniques in automatic sleep stage classification based on polysomnographic signals'. Together they form a unique fingerprint.

  • Cite this

    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 F. Ortuno, & I. Rojas (Eds.), Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings (pp. 230-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656). Springer-Verlag. https://doi.org/10.1007/978-3-319-31744-1_21