Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data
Author(s)Sano, Akane; Picard, Rosalind W.
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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.
DepartmentMassachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Institute of Electrical and Electronics Engineers (IEEE)
Sano, Akane, and Rosalind W. Picard. “Comparison of Sleep-Wake Classification Using Electroencephalogram and Wrist-Worn Multi-Modal Sensor Data.” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 26-30 August 2014, Chicago, Illinois, USA, IEEE, 2014.
Author's final manuscript