Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data

Author

Sano, Akihide ; Picard, Rosalind W.

Author_Institution

Massachusetts Inst. of Technol., Cambridge, MA, USA

fYear

2014

fDate

26-30 Aug. 2014

Firstpage

930

Lastpage

933

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.

Keywords

biomedical measurement; electroencephalography; medical signal processing; signal classification; sleep; EEG; acceleration data; electroencephalogram; intersubject classification; intrasubject classification; physiological data; skin conductance; skin temperature; sleep-wake classification; wrist wearable sensor; wrist-worn multimodal sensor data; Accuracy; Electroencephalography; Sensors; Skin; Sleep; Standards; Wrist;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2014.6943744

Filename

6943744