Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.
Background: Nowadays, 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 polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process o...
| Publicado en: | BioMedical Engineering OnLine Vol. 16; pp. 1 - 20 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2017 Supplement
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124765313&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124765313 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 2017 Supplement vid: 16 pid: 24147 pub: BioMed Central artinfo: ui: 124765313 124765313 NLM28830438 124765313 10.1186/s12938-017-0358-3 NLM28830438 124765313 ppf: 1 ppct: 19 formats: tig: atl: Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study. aug: au: Najdi, Shirin Gharbali, Ali Fonseca, José Gharbali, Ali Abdollahi Fonseca, José Manuel affil: Computational Intelligence Group of CTS/UNINOVA, Caparica, Portugal sug: subj: Signal Processing, Computer Assisted Sleep Stages Polysomnography Human Automation Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: Nowadays, 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 polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process.Methods: In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity.Results: Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy.Conclusions: The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|