Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings

被引：13
|
作者：
Chriskos, Panteleimon [1 ]
Kaitalidou, Dimitra S. [1 ]
Karakasis, Georgios [1 ]
Frantzidis, Christos [1 ,2 ]
Gkivogkli, Polyxeni T. [1 ,2 ]
Bamidis, Panagiotis [1 ,2 ]
Kourtidou-Papadeli, Chrysoula [2 ]
机构：
[1] Aristotle Univ Thessaloniki, Med Sch, Lab Med Phys, Thessaloniki, Greece
[2] Grp Aerosp Med Assoc GASMA, Thessaloniki, Greece
来源：
2017 IEEE 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) | 2017年
基金：
欧盟地平线“2020”;
关键词：
GRAPH-THEORETICAL ANALYSIS; BRAIN; NETWORKS; SYNCHRONIZATION; DYNAMICS;
D O I：
10.1109/CBMS.2017.83
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：435 / 439
页数：5
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