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A Low Computational Cost Algorithm for REM Sleep Detection Using Single Channel EEG

Overview of attention for article published in Annals of Biomedical Engineering, August 2014
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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1 tweeter
wikipedia
1 Wikipedia page

Citations

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26 Dimensions

Readers on

mendeley
65 Mendeley
citeulike
1 CiteULike
Title
A Low Computational Cost Algorithm for REM Sleep Detection Using Single Channel EEG
Published in
Annals of Biomedical Engineering, August 2014
DOI 10.1007/s10439-014-1085-6
Pubmed ID
Authors

Syed Anas Imtiaz, Esther Rodriguez-Villegas

Abstract

The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8-16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
United Kingdom 1 2%
South Africa 1 2%
United States 1 2%
Unknown 61 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 28%
Researcher 13 20%
Unspecified 9 14%
Student > Master 9 14%
Student > Bachelor 7 11%
Other 9 14%
Readers by discipline Count As %
Unspecified 17 26%
Engineering 17 26%
Computer Science 7 11%
Neuroscience 6 9%
Medicine and Dentistry 6 9%
Other 12 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2017.
All research outputs
#3,116,604
of 12,356,791 outputs
Outputs from Annals of Biomedical Engineering
#241
of 1,206 outputs
Outputs of similar age
#46,923
of 197,746 outputs
Outputs of similar age from Annals of Biomedical Engineering
#5
of 23 outputs
Altmetric has tracked 12,356,791 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,206 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 197,746 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.