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Objective measure of sleepiness and sleep latency via bispectrum analysis of EEG.

Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement disorder, narcolepsy, etc. It affects 8% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the exis...

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Published in:Medical & Biological Engineering & Computing Vol. 48; no. 12; pp. 1203 - 1214
Main Authors: Swarnkar V, Abeyratne U, Hukins C, Swarnkar, Vinayak, Abeyratne, Udantha, Hukins, Craig
Format: research Journal Article
Published: Springer Nature Dec2010
Online Access:View this record in EBSCOhost
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      dt: Dec2010
      vid: 48
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      pub: Springer Nature
      place: New York, New York
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        atl: Objective measure of sleepiness and sleep latency via bispectrum analysis of EEG.
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        au:
          Swarnkar V
          Abeyratne U
          Hukins C
          Swarnkar, Vinayak
          Abeyratne, Udantha
          Hukins, Craig
        affil: The University of Queensland, Brisbane, Australia
      sug:
        subj:
          Sleep Disorders Diagnosis
          Wakefulness Physiology
          Electroencephalography Methods
          Occupational Health
          Polysomnography
          Signal Processing, Computer Assisted
          Sleep Stages Physiology
      ab: Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement disorder, narcolepsy, etc. It affects 8% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the existing tests for measuring sleepiness. Sleep Latency (SL) is the main measures of sleepiness computed in these tests. These are the laboratory-based tests and require services of an expert sleep technician. There are no tests available to detect inadvertent sleep onset in real time and which can be performed in any professional work environment to measure sleepiness level. In this article, we propose a fully automated, objective sleepiness analysis technique based on the single channel of EEG. The method uses a one-dimensional slice of the EEG Bispectrum representing a nonlinear transformation of the underlying EEG generator to compute a novel index called Sleepiness Index. The SL is then computed from the SI. Working on the patient's database of 42 subjects we computed SI and estimated SL. A strong significant correlation (r ≥ 0.70, s < 0.001) was found between technician scored SL and that computed via SI. The proposed technology holds promise in the automation of the MSLT and MWT tests. It can also be developed into a sleep management system, wherein the SI is incorporated into a sleepiness index alert unit to alarm the user when sleepiness level crosses the predetermined threshold.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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