title: Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data.
abstract: This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results. As results, EEG features showed 83% while features from a wrist wearable sensor showed 74% and the combination of ACC and ST played more important roles in sleep/wake classification.
authors.forename: Akane
authors.initials: A
authors.lastname: Sano
authors.forename: Rosalind W
authors.initials: RW
authors.lastname: Picard
mesh_terms: D061725:Accelerometry; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D005712:Galvanic Skin Response / Q000502:physiology; D006801:Humans; D017286:Polysomnography; D012372:ROC Curve; D012881:Skin Temperature / Q000502:physiology; D012890:Sleep / Q000502:physiology*; D014851:Wakefulness / Q000502:physiology*; D014953:Wrist / Q000502:physiology*; D055815:Young Adult