Time-frequency analysis and fuzzy-based detection of heat-stressed sleep EEG spectra.
Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculo...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 1; pp. 23 - 40 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2021
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| 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=148139428&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148139428 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2021 vid: 59 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148139428 148139428 NLM33188622 10.1007/s11517-020-02278-7 NLM33188622 148139428 ppf: 23 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Time-frequency analysis and fuzzy-based detection of heat-stressed sleep EEG spectra. aug: au: Upadhyay, Prabhat Kumar Nagpal, Chetna affil: Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India sug: subj: Signal Processing, Computer Assisted Heat Sleep Wakefulness Electroencephalography Sleep Stages Scales Arthritis Impact Measurement Scales ab: Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were carried out under defined clinical condition for the classification of sleep EEG. Subsequent upon the extraction of various features from the raw EEG data, neuro-fuzzy system is trained to classify the sleep stages into three major classes namely awake, slow wave sleep (SWS), and rapid eye movement sleep (REM). This classification would enable medical professionals to diagnose sleep related disorders accurately. The results obtained clearly indicate that the mean performance for SWS stage is profound as compared to REM and awake stage. Specificity and sensitivity of the proposed method are obtained as 95.4% and 80%, respectively. The average accuracy of the system employing neuro-fuzzy approach is found to be 90.6% in which SWS stage was best detected among the other stages of sleep EEG.Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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