Session

Saturday, June 24, 2017 - 09:00 to 15:45
Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings
Abstract: 
This paper focuses on developing a novel approach to automatic sleep stage classification based on electroencephalographic (EEG) data. The proposed methodology employs contemporary mathematical tools such as the synchronization likelihood and graph theory metrics applied on sleep EEG data. The derived features are then fitted into three different machine learning techniques, namely k-nearest neighbors, support vector machines and neural networks. The evaluation of their comparative performance is investigated according to their accuracy. Interestingly, the support vector machine achieves the maximum possible accuracy, i.e., 89.07%, which renders it as a suitable method for sleep stage classification.
Panteleimon Chriskos's picture
Panteleimon Chriskos
Dimitra Kaitalidou's picture
Dimitra Kaitalidou
Georgios Karakasis's picture
Georgios Karakasis
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Polyxeni Gkivogkli's picture
Polyxeni Gkivogkli
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Chrysoula Kourtidou-Papadeli's picture
Chrysoula Kourtidou-Papadeli