We have located links that may give you full text access.
EVALUATION STUDIES
JOURNAL ARTICLE
Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings.
Journal of Neuroscience Methods 2017 April 16
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.
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.
Full text links
Trending Papers
A Personalized Approach to the Management of Congestion in Acute Heart Failure.Heart International 2023
Potential Mechanisms of the Protective Effects of the Cardiometabolic Drugs Type-2 Sodium-Glucose Transporter Inhibitors and Glucagon-like Peptide-1 Receptor Agonists in Heart Failure.International Journal of Molecular Sciences 2024 Februrary 21
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app