NDLI: Advanced measure selection in automatic NREM discrimination based on EEG

Content Provider	IEEE Xplore Digital Library
Author	Wen Zhao Jingzhi Yan Bin Hu Haoyu Ma Li Liu
Copyright Year	2010
Description	Author affiliation: School of CTN, TEE, Birmingham City University, Birmingham, UK (Bin Hu) \|\| School of Information Science ana Engineering, Lanzhou University, Lanzhou, China (Wen Zhao; Jingzhi Yan; Haoyu Ma; Li Liu)
Abstract	Smart homes have been proposed for senior citizens aiming to improve their quality of life. In order to monitor the seniors ' sleep condition in smart homes to protect them from sleep disorder, an automatic sleep staging system is necessary. Furthermore, measure selection is a crucial step in an automatic sleep staging system. In this paper, we present three advanced combination schemes among 15 sleep measures, which have been most commonly used in the automatic discrimination of sleep stages. For testing the validity of these combination schemes, we apply each combination of measures into a BP network to calculate the discrimination rates of NREM. The result of experiment shows that the combination method of two different ideologies of nonlinear measures is better than those including only one of them. And combining measures that have high accuracy rate in discriminating three sleep stages respectively can get effective results. Finally, we can get an optimum measure combination, composed of measures including EDGE, KK, delta, SaEn and LLE. By testing, the total accuracy rate of sleep staging reaches 85.23%, 69.57%, 86.11% and 100% in SI, S2 and SWS respectively. This result is desirable under the condition of only using EEG.
Starting Page	26
Ending Page	31
File Size	130207
Page Count	6
File Format	PDF
ISBN	9781424491445
e-ISBN	9781424491438
DOI	10.1109/ICPCA.2010.5704070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-01
Publisher Place	Slovenia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation nonlinear measures EEG Smart homes measure selection Electroencephalography Entropy Sleep staging Training Accuracy BP neural network Sleep spectral measures
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

EEG based detection of alcoholics using spectral entropy with neural network classifiers

MPA EEG model-based vigilance level estimation by artificial neural network

Extracting Impact Characteristics of Sports Training on EEG by Genetic Algorithm

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

Feature selection of EEG data with neuro-statistical method

Automatic sleep stage classification for daytime nap based on hopfield neural network

BP Neural Network for Human Activity Recognition in Smart Home

Neural network in the application of EEG signal classification method

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Advanced measure selection in automatic NREM discrimination based on EEG

Similar Documents

EEG based detection of alcoholics using spectral entropy with neural network classifiers

MPA EEG model-based vigilance level estimation by artificial neural network

Extracting Impact Characteristics of Sports Training on EEG by Genetic Algorithm

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

Feature selection of EEG data with neuro-statistical method

Automatic sleep stage classification for daytime nap based on hopfield neural network

BP Neural Network for Human Activity Recognition in Smart Home

Neural network in the application of EEG signal classification method

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Advanced measure selection in automatic NREM discrimination based on EEG