title: Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings.
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.
  
  
  METHODS
  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.
  
  
  METHODS
  Our method had an average kappa score of 0.72 when compared to visual sleep scoring by an independent blinded sleep scorer.
  
  
  CONCLUSIONS
  This shows that this simple method is capable of differentiating between SWS and non-SWS epochs reliably based solely on intracranial EEG recordings.
authors.affiliation: Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: chrystal.reed@cshs.org.
authors.forename: Chrystal M
authors.initials: CM
authors.lastname: Reed
authors.affiliation: Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Kurtis G
authors.initials: KG
authors.lastname: Birch
authors.affiliation: Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Kamiński
authors.affiliation: Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Shannon
authors.initials: S
authors.lastname: Sullivan
authors.affiliation: Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Chung
authors.affiliation: Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Adam N
authors.initials: AN
authors.lastname: Mamelak
authors.affiliation: Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Ueli
authors.initials: U
authors.lastname: Rutishauser
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001921:Brain / Q000503:physiopathology; D003700:Delta Rhythm; D000069280:Electrocorticography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D012640:Seizures / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology; D013826:Theta Rhythm
keywords: Automatic sleep staging; Electroencephalography; Intracranial EEG; Slow wave sleep; Vigilance index