Deep convolutional neural network for classification of sleep stages from single-channel EEG signals-Reference-Cited by-同舟云学术

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

Published:2019-08 Issue: Volume:324 Page:108312
ISSN:0165-0270
Container-title:Journal of Neuroscience Methods
language:en
Short-container-title:Journal of Neuroscience Methods

Author:

Mousavi Z.,Yousefi Rezaii T.,Sheykhivand S.,Farzamnia A.,Razavi S.N.

Publisher

Elsevier BV

Subject

General Neuroscience

Reference39 articles.

1. Short-Term sleep EEG recordings after partial sleep deprivation as a routine procedure in order to uncover epileptic phenomena: an evaluation of 719 EEG recordings;Kubicki;Epilepsy Res. Suppl.,1991

2. The inﬂuence of light drowsiness on the latency and amplitude of P300;Koshino;Clin. Electroencephalogr.,1993

3. AUtomated sleep stage identiﬁcation system based on time–frequency analysis of a single eeg channel and random forest classiﬁer;Fraiwan;Comput. Methods Programs Biomed.,2012

4. Manual of standardized terminology;Rechtschaffen,1968

5. Automatic signal abnormality detection using time-frequency features and machine learning: a newborn EEG seizure case study;Boashash;Knowl. Based Syst.,2016

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