Computational NeuroscienceSemi-automatic sleep EEG scoring based on the hypnospectrogram

- We explore the possibility of sleep scoring using the whole-night time-frequency analysis, termed hypnospectrogram, with a computer-assisted K-means clustering method.
- Hypnograms were derived from 10 whole-night sleep EEG recordings using either standard visual scoring under the Rechtshaffen and Kales criteria or semi-automated analysis of the hypnospectrogram derived from a single EEG electrode.
- We measured substantial agreement between the two approaches with Cohen's kappa considering all 7 stages at 0.61.
- The hypnospectrogram approach offers the scorer the opportunity to exploit the information-rich graphic representation of the whole night sleep.

BackgroundSleep EEG organization is revealed by sleep scoring, a time-consuming process based on strictly defined visual criteria.New methodWe explore the possibility of sleep scoring using the whole-night time-frequency analysis, termed hypnospectrogram, with a computer-assisted K-means clustering method.ResultsHypnograms were derived from 10 whole-night sleep EEG recordings using either standard visual scoring under the Rechtshaffen and Kales criteria or semi-automated analysis of the hypnospectrogram derived from a single EEG electrode. We measured substantial agreement between the two approaches with Cohen's kappa considering all 7 stages at 0.61.Comparison with existing methodsA number of existing automated procedures have reached the level of human inter-rater agreement using the standard criteria. However, our approach offers the scorer the opportunity to exploit the information-rich graphic representation of the whole night sleep upon which the automated method works.ConclusionThis work suggests that the hypnospectrogram can be used as an objective graphical representation of sleep architecture upon which sleep scoring can be performed with computer-assisted methods.