Journal ArticleDOI
Automatic sleep scoring using statistical features in the EMD domain and ensemble methods
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TLDR
The proposed sleep staging algorithm is a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal and its non-REM 1 stage detection accuracy is better than most of the existing works.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2016-01-01. It has received 204 citations till now. The article focuses on the topics: Ensemble learning & Feature selection.read more
Citations
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Journal ArticleDOI
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
TL;DR: This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and introduces a simple yet efficient CNN architecture to power the framework.
Journal ArticleDOI
A convolutional neural network for sleep stage scoring from raw single-channel EEG
TL;DR: A deep convolutional neural network is introduced on raw EEG samples for supervised learning of 5-class sleep stage prediction and a method for visualizing class-wise patterns learned by the network is presented.
Journal ArticleDOI
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
TL;DR: A novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals is presented.
Journal ArticleDOI
A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features
TL;DR: Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously and is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient.
Journal ArticleDOI
Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting
TL;DR: The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.
References
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Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
Use of Ranks in One-Criterion Variance Analysis
William Kruskal,W. Allen Wallis +1 more
TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.
Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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