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Complexity science for sleep stage classification from EEG

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Title: Complexity science for sleep stage classification from EEG
Authors: Nakamura, T
Adjei, T
Alqurashi, Y
Looney, D
Morrell, M
Mandic, D
Item Type: Conference Paper
Abstract: Automatic sleep stage classification is an important paradigm in computational intelligence and promises consider- able advantages to the health care. Most current automated methods require the multiple electroencephalogram (EEG) chan- nels and typically cannot distinguish the S1 sleep stage from EEG. The aim of this study is to revisit automatic sleep stage classification from EEGs using complexity science methods. The proposed method applies fuzzy entropy and permutation entropy as kernels of multi-scale entropy analysis. To account for sleep transition, the preceding and following 30 seconds of epoch data were used for analysis as well as the current epoch. Combining the entropy and spectral edge frequency features extracted from one EEG channel, a multi-class support vector machine (SVM) was able to classify 93.8% of 5 sleep stages for the SleepEDF database [expanded], with the sensitivity of S1 stage was 49.1%. Also, the Kappa’s coefficient yielded 0.90, which indicates almost perfect agreement.
Issue Date: 3-Jul-2017
Date of Acceptance: 4-Feb-2017
URI: http://hdl.handle.net/10044/1/45327
DOI: 10.1109/IJCNN.2017.7966411
ISSN: 2161-4407
Publisher: IEEE
Start Page: 4387
End Page: 4394
Journal / Book Title: 2017 International Joint Conference on Neural Networks (IJCNN)
Copyright Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Rosetrees Trust
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: N/A
EP/K503733/1
EP/K025643/1
Conference Name: IEEE International Joint Conference on Neural Networks (IJCNN) 2017
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Engineering
ENTROPY
Publication Status: Published
Start Date: 2017-05-14
Finish Date: 2017-05-19
Conference Place: Anchorage, Alaska, USA
Online Publication Date: 2017-07-03
Appears in Collections:Electrical and Electronic Engineering
National Heart and Lung Institute
Faculty of Engineering