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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8715377/ |
| _version_ | 1828889990661144576 |
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| author | Antonio E. Teijeiro Maryamsadat Shokrekhodaei Homer Nazeran |
| author_facet | Antonio E. Teijeiro Maryamsadat Shokrekhodaei Homer Nazeran |
| author_sort | Antonio E. Teijeiro |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T12:50:50Z |
| format | Article |
| id | doaj.art-b258ccb4516e4e9daad3233365181b16 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-12-13T12:50:50Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-b258ccb4516e4e9daad3233365181b162022-12-21T23:45:20ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722019-01-01711010.1109/JTEHM.2019.29100638715377The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth ApplicationsAntonio E. Teijeiro0https://orcid.org/0000-0003-1285-7864Maryamsadat Shokrekhodaei1Homer Nazeran2Electrical and Computer Engineering Department, The University of Texas at El Paso, El Paso, TX, USAElectrical and Computer Engineering Department, The University of Texas at El Paso, El Paso, TX, USAElectrical and Computer Engineering Department, The University of Texas at El Paso, El Paso, TX, USARecent 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.https://ieeexplore.ieee.org/document/8715377/EEG analysisEEG machine learning computerseizure predictionseizure prediction workstation |
| spellingShingle | Antonio E. Teijeiro Maryamsadat Shokrekhodaei Homer Nazeran The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications IEEE Journal of Translational Engineering in Health and Medicine EEG analysis EEG machine learning computer seizure prediction seizure prediction workstation |
| 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 | EEG analysis EEG machine learning computer seizure prediction seizure prediction workstation |
| url | https://ieeexplore.ieee.org/document/8715377/ |
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