Evidence map›Paper›PMID 32260314›Full record

ArticleSensors (Basel, Switzerland)2020

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.IEEE access : practical innovations, open solutions · 2021
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gi-Ren LiuDepartment of Mathematics, National Chen-Kung University, Tainan, Taiwan.
Caroline LustenbergerNeural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, 8092 Zurich, Switzerland.
Yu-Lun LoDepartment of Thoracic Medicine, Healthcare Center, Chang Gung Memorial Hospital, Chang Gung University, School of Medicine, New Taipei, Taiwan.
Wen-Te LiuSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
Yuan-Chung SheuDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu, Taiwan.
Hau-Tieng WuDepartment of Mathematics and Department of Statistical Science, Duke University, 120 Science Dr. Durham, NC, 27708, USA.

Funding

Swiss National Science Foundation PZ00P3_179795
6 · The paper itself

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.

Indexed as

AlgorithmsElectroencephalographyElectromyographyHumansSleep StagesEEGEMGscattering transformsleep stage classification

Identifiers

PMID32260314
PMCPMC7180982

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.