Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Gi Ren Liu; Caroline Lustenberger; Yu Lun Lo; Wen Te Liu; Yuan Chung Sheu; Hau Tieng Wu

doi:10.3390/s20072024

Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Gi Ren Liu, Caroline Lustenberger, Yu Lun Lo, Wen Te Liu, Yuan Chung Sheu, Hau Tieng Wu

Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

Original language	English (US)
Article number	2024
Journal	Sensors (Switzerland)
Volume	20
Issue number	7
DOIs	https://doi.org/10.3390/s20072024
State	Published - Apr 2020

Keywords

EEG
EMG
Scattering transform
Sleep stage classification

ASJC Scopus subject areas

Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

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10.3390/s20072024

Cite this

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title = "Save muscle information–unfiltered eeg signal helps distinguish sleep stages",

abstract = "Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.",

keywords = "EEG, EMG, Scattering transform, Sleep stage classification",

author = "Liu, {Gi Ren} and Caroline Lustenberger and Lo, {Yu Lun} and Liu, {Wen Te} and Sheu, {Yuan Chung} and Wu, {Hau Tieng}",

note = "Funding Information: Funding: This work is part of the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project support by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. CL is supported by the Swiss National Science Foundation (PZ00P3_179795). Funding Information: This work is part of the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project support by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. CL is supported by the Swiss National Science Foundation (PZ00P3_179795). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2020",

month = apr,

doi = "10.3390/s20072024",

language = "English (US)",

volume = "20",

journal = "Sensors (Switzerland)",

issn = "1424-8220",

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T1 - Save muscle information–unfiltered eeg signal helps distinguish sleep stages

AU - Liu, Gi Ren

AU - Lustenberger, Caroline

AU - Lo, Yu Lun

AU - Liu, Wen Te

AU - Sheu, Yuan Chung

AU - Wu, Hau Tieng

N1 - Funding Information: Funding: This work is part of the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project support by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. CL is supported by the Swiss National Science Foundation (PZ00P3_179795). Funding Information: This work is part of the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project support by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. CL is supported by the Swiss National Science Foundation (PZ00P3_179795). Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/4

Y1 - 2020/4

N2 - Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

AB - Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

KW - EEG

KW - EMG

KW - Scattering transform

KW - Sleep stage classification

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M1 - 2024

ER -

Save muscle information–unfiltered eeg signal helps distinguish sleep stages

Abstract

Keywords

ASJC Scopus subject areas

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