Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction.
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| Title: | Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction. |
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| Authors: | Chambers JD; Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia., Cook MJ; Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.; Seer Medical, Melbourne, VIC, Australia.; Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia.; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia., Burkitt AN; Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia., Grayden DB; Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.; Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia.; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia. |
| Source: | Frontiers in neuroscience [Front Neurosci] 2024 Nov 15; Vol. 18, pp. 1472747. Date of Electronic Publication: 2024 Nov 15 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101478481 Publication Model: eCollection Cited Medium: Print ISSN: 1662-4548 (Print) Linking ISSN: 1662453X NLM ISO Abbreviation: Front Neurosci Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1662-4548 |
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| DOI: | 10.3389/fnins.2024.1472747 |
