Literature DB >> 27230805

Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography.

Sami Myllymaa1,2,3, Anu Muraja-Murro3, Susanna Westeren-Punnonen3, Taina Hukkanen3, Reijo Lappalainen1, Esa Mervaala3,4, Juha Töyräs1,3, Kirsi Sipilä2,5,6,7, Katja Myllymaa3.   

Abstract

Recently, a number of portable devices designed for full polysomnography at home have appeared. However, current scalp electrodes used for electroencephalograms are not practical for patient self-application. The aim of this study was to evaluate the suitability of recently introduced forehead electroencephalogram electrode set and supplementary chin electromyogram electrodes for sleep staging. From 31 subjects (10 male, 21 female; age 31.3 ± 11.8 years), sleep was recorded simultaneously with a forehead electroencephalogram electrode set and with a standard polysomnography setup consisting of six recommended electroencephalogram channels, two electrooculogram channels and chin electromyogram. Thereafter, two experienced specialists scored each recording twice, based on either standard polysomnography or forehead recordings. Sleep variables recorded with the forehead electroencephalogram electrode set and separate chin electromyogram electrodes were highly consistent with those obtained with the standard polysomnography. There were no statistically significant differences in total sleep time, sleep efficiency or sleep latencies. However, compared with the standard polysomnography, there was a significant increase in the amount of stage N1 and N2, and a significant reduction in stage N3 and rapid eye movement sleep. Overall, epoch-by-epoch agreement between the methods was 79.5%. Inter-scorer agreement for the forehead electroencephalogram was only slightly lower than that for standard polysomnography (76.1% versus 83.2%). Forehead electroencephalogram electrode set as supplemented with chin electromyogram electrodes may serve as a reliable and simple solution for recording total sleep time, and may be adequate for measuring sleep architecture. Because this electrode concept is well suited for patient's self-application, it may offer a significant advancement in home polysomnography.
© 2016 European Sleep Research Society.

Entities:  

Keywords:  portable monitoring; screen-printed electrode; self-applicable sensor; sleep disorders; sleep scoring

Mesh:

Year:  2016        PMID: 27230805     DOI: 10.1111/jsr.12425

Source DB:  PubMed          Journal:  J Sleep Res        ISSN: 0962-1105            Impact factor:   3.981


  8 in total

Review 1.  The future of sleep health: a data-driven revolution in sleep science and medicine.

Authors:  Ignacio Perez-Pozuelo; Bing Zhai; Joao Palotti; Raghvendra Mall; Michaël Aupetit; Juan M Garcia-Gomez; Shahrad Taheri; Yu Guan; Luis Fernandez-Luque
Journal:  NPJ Digit Med       Date:  2020-03-23

2.  Home Polysomnography Reveals a First-Night Effect in Patients With Low Sleep Bruxism Activity.

Authors:  Tomi Miettinen; Katja Myllymaa; Taina Hukkanen; Juha Töyräs; Kirsi Sipilä; Sami Myllymaa
Journal:  J Clin Sleep Med       Date:  2018-08-15       Impact factor: 4.062

3.  Polysomnographic scoring of sleep bruxism events is accurate even in the absence of video recording but unreliable with EMG-only setups.

Authors:  Tomi Miettinen; Katja Myllymaa; Anu Muraja-Murro; Susanna Westeren-Punnonen; Taina Hukkanen; Juha Töyräs; Reijo Lappalainen; Esa Mervaala; Kirsi Sipilä; Sami Myllymaa
Journal:  Sleep Breath       Date:  2019-08-12       Impact factor: 2.816

4.  Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Authors:  Kaare B Mikkelsen; James K Ebajemito; Maria A Bonmati-Carrion; Nayantara Santhi; Victoria L Revell; Giuseppe Atzori; Ciro Della Monica; Stefan Debener; Derk-Jan Dijk; Annette Sterr; Maarten de Vos
Journal:  J Sleep Res       Date:  2018-11-13       Impact factor: 3.981

5.  Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

Authors:  Henri Korkalainen; Juhani Aakko; Brett Duce; Samu Kainulainen; Akseli Leino; Sami Nikkonen; Isaac O Afara; Sami Myllymaa; Juha Töyräs; Timo Leppänen
Journal:  Sleep       Date:  2020-11-12       Impact factor: 5.849

6.  Acute stroke and TIA patients have specific polygraphic features of obstructive sleep apnea.

Authors:  Akseli Leino; Susanna Westeren-Punnonen; Juha Töyräs; Sami Myllymaa; Timo Leppänen; Salla Ylä-Herttuala; Anu Muraja-Murro; Anne-Mari Kantanen; Jaana Autere; Pekka Jäkälä; Esa Mervaala; Katja Myllymaa
Journal:  Sleep Breath       Date:  2020-01-14       Impact factor: 2.816

Review 7.  The future of sleep health: a data-driven revolution in sleep science and medicine.

Authors:  Ignacio Perez-Pozuelo; Bing Zhai; Joao Palotti; Raghvendra Mall; Michaël Aupetit; Juan M Garcia-Gomez; Shahrad Taheri; Yu Guan; Luis Fernandez-Luque
Journal:  NPJ Digit Med       Date:  2020-03-23

8.  Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy.

Authors:  Sirin W Gangstad; Kaare B Mikkelsen; Preben Kidmose; Yousef R Tabar; Sigge Weisdorf; Maja H Lauritzen; Martin C Hemmsen; Lars K Hansen; Troels W Kjaer; Jonas Duun-Henriksen
Journal:  Biomed Eng Online       Date:  2019-10-30       Impact factor: 2.819

  8 in total

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