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The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications
Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6557542/ https://www.ncbi.nlm.nih.gov/pubmed/31263633 http://dx.doi.org/10.1109/JTEHM.2019.2910063 |
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collection | PubMed |
description | Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research. On the other hand, building the optimal workstation is a complex task, and system instability can arise from the least obvious sources imaginable. Therefore, the absence of a template for a dedicated seizure prediction workstation in today’s literature is a formidable obstacle to seizure prediction research. To increase the number of researchers working on this problem, a template for a dedicated seizure prediction workstation needs to become available. This paper proposes a novel dedicated system capable of machine learning-based seizure prediction and training for under U.S. $1000, which is significantly less expensive (U.S. $700 or more) than comparable commercial solutions. This powerful workstation will be capable of training sophisticated machine learning algorithms that can be deployed to lightweight wearable devices, which enables the creation of wearable EEG-based seizure early warning systems. |
format | Online Article Text |
id | pubmed-6557542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-65575422019-07-01 The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications IEEE J Transl Eng Health Med Article Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research. On the other hand, building the optimal workstation is a complex task, and system instability can arise from the least obvious sources imaginable. Therefore, the absence of a template for a dedicated seizure prediction workstation in today’s literature is a formidable obstacle to seizure prediction research. To increase the number of researchers working on this problem, a template for a dedicated seizure prediction workstation needs to become available. This paper proposes a novel dedicated system capable of machine learning-based seizure prediction and training for under U.S. $1000, which is significantly less expensive (U.S. $700 or more) than comparable commercial solutions. This powerful workstation will be capable of training sophisticated machine learning algorithms that can be deployed to lightweight wearable devices, which enables the creation of wearable EEG-based seizure early warning systems. IEEE 2019-05-15 /pmc/articles/PMC6557542/ /pubmed/31263633 http://dx.doi.org/10.1109/JTEHM.2019.2910063 Text en 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. |
spellingShingle | Article The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title | The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title_full | The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title_fullStr | The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title_full_unstemmed | The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title_short | The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications |
title_sort | conceptual design of a novel workstation for seizure prediction using machine learning with potential ehealth applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6557542/ https://www.ncbi.nlm.nih.gov/pubmed/31263633 http://dx.doi.org/10.1109/JTEHM.2019.2910063 |
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