Chrystal M Reed1, Kurtis G Birch2, Jan Kamiński2, Shannon Sullivan2, Jeffrey M Chung3, Adam N Mamelak2, Ueli Rutishauser4. 1. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: chrystal.reed@cshs.org. 2. Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 3. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 4. Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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.
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.
Authors: Heidi Danker-Hopfe; D Kunz; G Gruber; G Klösch; J L Lorenzo; S L Himanen; B Kemp; T Penzel; J Röschke; H Dorn; A Schlögl; E Trenker; G Dorffner Journal: J Sleep Res Date: 2004-03 Impact factor: 3.981
Authors: Filip Mivalt; Vaclav Kremen; Vladimir Sladky; Irena Balzekas; Petr Nejedly; Nicholas M Gregg; Brian Nils Lundstrom; Kamila Lepkova; Tereza Pridalova; Benjamin H Brinkmann; Pavel Jurak; Jamie J Van Gompel; Kai Miller; Timothy Denison; Erik K St Louis; Gregory A Worrell Journal: J Neural Eng Date: 2022-02-08 Impact factor: 5.043