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- Naoto Burioka
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, Yonago, Japan Halberg Chronobiology Center, University of Minnesota, Minneapolis, Minnesota, USA
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- Masanori Miyata
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, Yonago, Japan Halberg Chronobiology Center, University of Minnesota, Minneapolis, Minnesota, USA
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- Germaine Cornélissen
- Division of Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, Yonago, Japan
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- Franz Halberg
- Division of Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, Yonago, Japan
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- Takao Takeshima
- Department of Mathematics and Computer Science, Macalester College, St. Paul, Minnesota
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- Daniel T. Kaplan
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, Yonago, Japan
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- Hisashi Suyama
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, Yonago, Japan Halberg Chronobiology Center, University of Minnesota, Minneapolis, Minnesota, USA
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- Masanori Endo
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, Yonago, Japan Halberg Chronobiology Center, University of Minnesota, Minneapolis, Minnesota, USA
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- Yoshihiro Maegaki
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, Yonago, Japan
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- Takashi Nomura
- Department of Mathematics and Computer Science, Macalester College, St. Paul, Minnesota
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- Yutaka Tomita
- Division of Health Care, Faculty of Medicine, Tottori University, Yonago, Japan
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- Kenji Nakashima
- Department of Mathematics and Computer Science, Macalester College, St. Paul, Minnesota
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- Eiji Shimizu
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, Yonago, Japan Halberg Chronobiology Center, University of Minnesota, Minneapolis, Minnesota, USA
説明
<jats:p> Entropy measurement can discriminate among complex systems, including deterministic, stochastic and composite systems. We evaluated the changes of approximate entropy (ApEn) in signals of the electroencephalogram (EEG) during sleep. </jats:p><jats:p> EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean ± SD) were 0.896 ± 0.264 during eyes-closed waking state, 0.738 ± 0.089 during Stage I, 0.615 ± 0.107 during Stage II, 0.487 ± 0.101 during Stage III, 0.397 ± 0.078 during Stage IV and 0.789 ± 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep. </jats:p><jats:p> We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity. </jats:p>
収録刊行物
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- Clinical EEG and Neuroscience
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Clinical EEG and Neuroscience 36 (1), 21-24, 2005-01
SAGE Publications
