Sleep monitoring classification strategy for an unobtrusive EEG system

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.

Share This Post