Alertness Staging Based on Improved Self-Organizing Map
In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an impr...
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Published in | Transactions of Tianjin University Vol. 19; no. 6; pp. 459 - 462 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2013
School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China%Department of Biomedical Engineering, Tulane University, New Orleans 70112, USA |
Subjects | |
Online Access | Get full text |
ISSN | 1006-4982 1995-8196 |
DOI | 10.1007/s12209-013-2027-3 |
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Abstract | In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map(ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%. |
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AbstractList | In order to classify the alertness status, 19 channels of electroencephalogram (EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features (including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map (ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%. In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map(ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%. |
Author | 王学民 张翼 李向新 刘雅婷 曹红宝 周鹏 王晓璐 高翔 |
AuthorAffiliation | School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, Chin~ Department of Biomedical Engineering, Tulane University, New Orleans 70112, USA |
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Cites_doi | 10.1097/WNP.0b013e3181b2f1e3 10.1016/j.clinph.2006.01.017 10.1016/j.ssci.2008.11.009 10.1175/1520-0442(2001)013<0219:NPCATI>2.0.CO;2 10.1056/NEJMoa0707361 10.1007/s00521-007-0117-7 10.1016/j.eswa.2007.12.043 10.1016/j.ergon.2004.09.006 10.1002/wics.101 10.7205/MILMED-D-01-5008 10.1541/ieejeiss.130.420 10.1097/ALN.0b013e31820c2b57 10.1109/IEMBS.2005.1615790 10.1109/DSR.2011.6026802 |
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Keywords | improved self-organizing map (ISOM) electroencephalogram (EEG) alertness staging |
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Notes | In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map(ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%. 12-1248/T electroencephalogram(EEG) improved self-organizing map(ISOM) alertness staging |
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Snippet | In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different... In order to classify the alertness status, 19 channels of electroencephalogram (EEG) signals from 5 subjects were acquired during daytime nap. Ten different... |
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Title | Alertness Staging Based on Improved Self-Organizing Map |
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