This paper is in the following e-collection/theme issue:

Artificial Intelligence (1714) Machine Learning (1727) New Methods (1029)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40211, first published .
Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Authors of this article:

Shahab Haghayegh1, 2 Author Orcid Image ;   Kun Hu1, 2 Author Orcid Image ;   Katie Stone3 Author Orcid Image ;   Susan Redline1, 2 Author Orcid Image ;   Eva Schernhammer1, 2, 4 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Jiang X, Ren Y, Wu H, Li Y, Liu F. Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection. Frontiers in Neuroscience 2023;17 View
  2. Zhang X, Zhang X, Huang Q, Lv Y, Chen F. A review of automated sleep stage based on EEG signals. Biocybernetics and Biomedical Engineering 2024;44(3):651 View
  3. Ying S, Li P, Chen J, Cao W, Zhang H, Gao D, Liu T. An EEG-based single-channel dual-stream automatic sleep staging network with transfer learning. Applied Soft Computing 2025;170:112722 View

Conference Proceedings

  1. Merin P, A R, Vishnubhotla S, Ashok K, Sri T, Kumar P. 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). Investigating the Accuracy of Sleep Stage Classification in Pediatrics: A Comprehensive Review View