Literature DB >> 17376536

The use of two-channel electro-oculography in automatic detection of unintentional sleep onset.

Jussi Virkkala1, Joel Hasan, Alpo Värri, Sari-Leena Himanen, Mikko Härmä.   

Abstract

An automatic method was developed for detecting unintentional sleep onset. The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. An automatic estimation of slow eye movements (SEM) was developed and used as the main criterion to separate sleep stage 1 (S1) from wakefulness. Additionally synchronous electroencephalographic (EEG) activity of sleep stages 1 and 2 was detected by calculating cross-correlation and amplitude difference in the 1.5-6 Hz theta band between the two EOG channels. Alpha power 8-12 Hz and beta power 18-30 Hz were used to determine wakefulness. Unintentional sleep onsets were studied using data from four separate maintenance of wakefulness test (MWT) sessions of 228 subjects. The automatic scoring of 30s sleep onset epochs using only EOG was compared to standard visual sleep stage scoring. The optimal detection thresholds were derived using data from 114 subjects and then applied to the data from different 114 subjects. Cohen's Kappa between the visual and the new automatic scoring system in separating wakefulness and sleep was substantial (0.67) with epoch by epoch agreement of 98%. The sleep epoch detection sensitivity was 70% and specificity 99%. The results are provided with a 1s delay for each 30s epoch. The developed method has to be tested in field applications. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive self-applicable electrodes.

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Year:  2007        PMID: 17376536     DOI: 10.1016/j.jneumeth.2007.02.001

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  2 in total

1.  Sleep stage and obstructive apneaic epoch classification using single-lead ECG.

Authors:  Bülent Yilmaz; Musa H Asyali; Eren Arikan; Sinan Yetkin; Fuat Ozgen
Journal:  Biomed Eng Online       Date:  2010-08-19       Impact factor: 2.819

Review 2.  Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Authors:  Keum-Shik Hong; Muhammad Jawad Khan
Journal:  Front Neurorobot       Date:  2017-07-24       Impact factor: 2.650

  2 in total

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