Title :
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
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;
Conference_Titel :
Embedded Computing (MECO), 2015 4th Mediterranean Conference on
Conference_Location :
Budva
Print_ISBN :
978-1-4799-8999-7
DOI :
10.1109/MECO.2015.7181955
