Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

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

doi:10.3390/s20072024

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.

Highlights

Electroencephalogram (EEG) is a widely used monitoring method to record brain activity in a non-invasive way
The EEG signal in our database is sampled at the sampling rate 500 Hz, so theoretically we could inspect the spectral information up to 250 Hz
To show how much muscle tone information is encoded in the EEG signal, we consider four different spectral ranges: (0.5, 15), (15, 35), (35, 80), and (80, 250)

Summary

Introduction

Electroencephalogram (EEG) is a widely used monitoring method to record brain activity in a non-invasive way. It has been extensively applied in various scientific and clinical setups. To date, it is still the gold standard method to define different sleep stages. In order to visually identify these stages, electrooculography (EOG, eye movement) and surface electromyography (EMG, muscle activation/tone) are further recommended, to distinguish non-rapid eye-movement sleep (NREM) from REM sleep. In sleep laboratories all signals are recorded to provide an accurate sleep staging. The EEG signal is bandpass filtered at 0.3–35 Hz for the visualization purpose [1,2,3]

Methods

Results

Conclusion