A wrist-worn biosensor system for assessment of neurological status

Author

Cogan, D. ; Pouyan, M. Baran ; Nourani, M. ; Harvey, J.

Author_Institution

Quality of Life Technol. Lab., Univ. of Texas at Dallas, Richardson, TX, USA

fYear

2014

fDate

26-30 Aug. 2014

Firstpage

5748

Lastpage

5751

Abstract

EEG based monitoring for the purpose of assessing a patient´s neurological status is conspicuous and uncomfortable at best. We are analyzing a set of physiological signals that may be monitored comfortably by a wrist worn device. We have found that these signals and machine based classification allows us to accurately discriminate among four stress states of individuals. Further, we have found a clear change in these signals during the 70 minutes preceding a single convulsive epileptic seizure. Our classification accuracy on all data has been greater than 90% to date.

Keywords

body sensor networks; electroencephalography; learning (artificial intelligence); medical disorders; medical signal processing; neurophysiology; patient monitoring; signal classification; EEG based monitoring; classification accuracy; machine-based classification; neurological status assessment; patient neurological status; physiological signal analysis; single convulsive epileptic seizure; stress states; time 70 min; wrist worn device; wrist-worn biosensor system; Biomedical monitoring; Conferences; Electroencephalography; Monitoring; Stress; Temperature measurement;

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.2014.6944933

Filename

6944933