Deep learning in classifying sleep stages
Author | Al-Meer M.H. |
Author | Al Mamun M.D.A. |
Available date | 2020-03-03T06:19:33Z |
Publication Date | 2018 |
Publication Name | 2018 13th International Conference on Digital Information Management, ICDIM 2018 |
Resource | Scopus |
Abstract | This paper presents a deep feed-forward neural network classifier to automatically classify the stages of sleep using raw data taken from a single electropalatogram channel (Fpz-Cz). No features are extracted at all from the data, and the network can classify the five sleep stages: waking, Nl, N2, N3, N4, and rapid eye movement. The network has three layers, takes as an input a l-s epochs to be classified, and requires no signal pre-processing nor feature extraction. We trained and evaluated our system using DeepLearning4J, the free Java framework for test data taken from PhysioNet's Polysomnography Sleep database. An accuracy of 0.99 within a constrained environment has been reached. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Deep Learning Machine Learning PSG Sleep Stages |
Type | Conference Paper |
Pagination | 17-Dec |
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Computer Science & Engineering [2284 items ]