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Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

Reed, Chrystal M. and Birch, Kurtis G. and Kamiński, Jan and Sullivan, Shannon and Chung, Jeffrey M. and Mamelak, Adam N. and Rutishauser, Ueli (2017) Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings. Journal of Neuroscience Methods, 282 . pp. 1-8. ISSN 0165-0270. PMCID PMC5455770. doi:10.1016/j.jneumeth.2017.02.009. https://resolver.caltech.edu/CaltechAUTHORS:20200402-065843081

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Abstract

Background: An automated process for sleep staging based on intracranial EEG data alone is needed to facilitate research into the neural processes occurring during slow wave sleep (SWS). Current manual methods for sleep scoring require a full polysomnography (PSG) set-up, including electrooculography (EOG), electromyography (EMG), and scalp electroencephalography (EEG). This set-up can be technically difficult to place in the presence of intracranial EEG electrodes. There is thus a need for a method for sleep staging based on intracranial recordings alone. New method: Here we show a reliable automated method for the detection of periods of SWS solely based on intracranial EEG recordings. The method utilizes the ratio of spectral power in delta, theta, and spindle frequencies relative to alpha and beta frequencies to classify 30-s segments as SWS or not. Results: We evaluated this new method by comparing its performance against visually scored patients (n = 9), in which we also recorded EOG and EMG simultaneously. Our method had a mean positive predictive value of 64% across all nights. Also, an ROC analysis of the performance of our algorithm compared to manually labeled nights revealed a mean average area under the curve of 0.91 across all nights. Comparison with existing method: Our method had an average kappa score of 0.72 when compared to visual sleep scoring by an independent blinded sleep scorer. Conclusion: This shows that this simple method is capable of differentiating between SWS and non-SWS epochs reliably based solely on intracranial EEG recordings.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jneumeth.2017.02.009DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5455770PubMed CentralArticle
ORCID:
AuthorORCID
Reed, Chrystal M.0000-0002-7157-3645
Mamelak, Adam N.0000-0002-4245-6431
Rutishauser, Ueli0000-0002-9207-7069
Additional Information:© 2017 Elsevier B.V. Received 27 October 2016, Revised 20 February 2017, Accepted 22 February 2017, Available online 24 February 2017. We thank the staff of the Epilepsy Monitoring Unit at Cedars-Sinai Medical Center for invaluable assistance. This work was supported by the Neurosurgery Research and Education Foundation (to K.B.) and NIMH (R01MH110831, to U.R.).
Funders:
Funding AgencyGrant Number
Neurosurgery Research and Education FoundationUNSPECIFIED
NIHR01MH110831
Subject Keywords:Automatic sleep staging; Slow wave sleep; Electroencephalography; Intracranial EEG; Vigilance index
PubMed Central ID:PMC5455770
DOI:10.1016/j.jneumeth.2017.02.009
Record Number:CaltechAUTHORS:20200402-065843081
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200402-065843081
Official Citation:Chrystal M. Reed, Kurtis G. Birch, Jan Kamiński, Shannon Sullivan, Jeffrey M. Chung, Adam N. Mamelak, Ueli Rutishauser, Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings, Journal of Neuroscience Methods, Volume 282, 2017, Pages 1-8, ISSN 0165-0270, https://doi.org/10.1016/j.jneumeth.2017.02.009. (http://www.sciencedirect.com/science/article/pii/S0165027017300511)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102255
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:02 Apr 2020 15:01
Last Modified:16 Nov 2021 18:10

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