Sleep monitoring classification strategy for an unobtrusive EEG system

Author

Gialelis, J. ; Panagiotou, C. ; Karadimas, D. ; Samaras, I. ; Chondros, P. ; Serpanos, D. ; Koubias, S.

Author_Institution

Electr. & Comput. Eng. Dept., Univ. of Patras, Patras, Greece

fYear

2015

fDate

14-18 June 2015

Firstpage

402

Lastpage

406

Abstract

The advances in the wearable devices and Artificial Intelligence domains highlight the need for ICT systems that aim in the improvement of human´s quality of life. In this paper we present the sleeping tracking component of an activity and sleeping tracking system. We present the sleep quality assessment based on EEG processing and support vector machines with sequential minimal optimization classifiers (SVM-SMO). The performance of the system demonstrated by respective experiments (accuracy: 83% and kappa coeff: 72%) exhibits significant prospects for the assessment of sleep quality and the further validation through an evaluation study.

Keywords

artificial intelligence; electroencephalography; medical signal processing; optimisation; signal classification; sleep; support vector machines; ICT systems; SVM-SMO; artificial intelligence domains; sequential minimal optimization classifiers; sleep monitoring classification strategy; sleep quality assessment; sleeping tracking component; support vector machines; unobtrusive EEG system; wearable devices; Biomedical monitoring; Databases; Electrocardiography; Electroencephalography; Medical services; Monitoring; Sleep; EEG; SVM; sleep stages;

fLanguage

English

Publisher

ieee

Conference_Titel

Embedded Computing (MECO), 2015 4th Mediterranean Conference on

Conference_Location

Budva

Print_ISBN

978-1-4799-8999-7

Type

conf

DOI

10.1109/MECO.2015.7181955

Filename

7181955