{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sleepeegnet-automated-sleep-stage-scoring","title":"SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach","arxiv_id":"1903.02108","date":"2019-03-05","proceeding":null,"authors":["Sajad Mousavi","Fatemeh Afghah","U. Rajendra Acharya"],"abstract":"Electroencephalogram (EEG) is a common base signal used to monitor brain\nactivity and diagnose sleep disorders. Manual sleep stage scoring is a\ntime-consuming task for sleep experts and is limited by inter-rater\nreliability. In this paper, we propose an automatic sleep stage annotation\nmethod called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is\ncomposed of deep convolutional neural networks (CNNs) to extract time-invariant\nfeatures, frequency information, and a sequence to sequence model to capture\nthe complex and long short-term context dependencies between sleep epochs and\nscores. In addition, to reduce the effect of the class imbalance problem\npresented in the available sleep datasets, we applied novel loss functions to\nhave an equal misclassified error for each sleep stage while training the\nnetwork. We evaluated the proposed method on different single-EEG channels\n(i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets\npublished in 2013 and 2018. The evaluation results demonstrate that the\nproposed method achieved the best annotation performance compared to current\nliterature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and\nCohen's Kappa coefficient = 0.79. Our developed model is ready to test with\nmore sleep EEG signals and aid the sleep specialists to arrive at an accurate\ndiagnosis. The source code is available at\nhttps://github.com/SajadMo/SleepEEGNet.","url_abs":"http://arxiv.org/abs/1903.02108v1","url_pdf":"http://arxiv.org/pdf/1903.02108v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sleepeegnet-automated-sleep-stage-scoring","repo_url":"https://github.com/SajadMo/SleepEEGNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sleepeegnet-automated-sleep-stage-scoring","repo_url":"https://github.com/MousaviSajad/SleepEEGNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sleepeegnet-automated-sleep-stage-scoring","repo_url":"https://github.com/WoongheeLee/SleepEEGNet_in_TensorFlow2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"sleep-stage-detection","task_name":"Sleep Stage Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.02108","atlas_url":"https://app.syntology.ai/?focus=1903.02108","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}