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Electroencephalogram‐sleep study

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The study of sleep, and in particular electroencephalogram (EEG)‐sleep recordings, is important in several areas of medicine. Next to pain, sleep anomalies are the most significant indicators of illness. During sleep the human brain goes through several physiological stages; therefore, the problem of automated detection of sleep stages using EEG data naturally arises in neurosciences. A two step procedure of computerized scoring of sleep stages is considered, with the first step involving features extractions via spectral and nonlinear dynamics characteristics and the second step in which sleep classifications can be accomplished. WIREs Comput Stat 2013, 5:326–333. doi: 10.1002/wics.1261 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Applications of Computational Statistics > Health and Medical Data/Informatics Applications of Computational Statistics > Signal and Image Processing and Coding Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Examples of neonatal EEG Recordings in active (left) and quiet (right) sleep stages, a single channel.
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An example of time‐dependant delta power for a full‐term neonate.
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A typical example of an EEG signal filtered into four frequency bands reflecting the common classification of the spectral contents of EEG signals. From top to bottom: Delta Waves (0.5–4 Hz), Theta Waves (4–8 Hz), Alpha Waves (8–12 Hz), Beta Waves (12–30 Hz).
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Example of the spectrum for active and quiet sleep of 30 seconds recording for a fullterm neonate.
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Example of polysomnographic data of a neonate, 1 min of the recording.
[ Normal View | Magnified View ]

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Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Applications of Computational Statistics > Signal and Image Processing and Coding
Applications of Computational Statistics > Health and Medical Data/Informatics
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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