Journal ArticleDOI
AI-based approach to automatic sleep classification
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TLDR
A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.Abstract:
The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.read more
Citations
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Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier
TL;DR: An efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy.
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An Introduction to Machine Learning
TL;DR: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.
TL;DR: Techniques of recording, scoring, and doubtful records are carefully considered, and Recommendations for abbreviations, types of pictorial representation, order of polygraphic tracings are suggested.
Journal ArticleDOI
The self-organizing map
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Fourth Edition
TL;DR: Historical aspects introduction to the neurophysiological basis of the EEG and DC potentials cellular substrates of spontaneous and evoked brain rhythms dynamics of EEG as signals and neuronal populations are introduced.