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Seizure Detection: A Low Computational Effective Approach without Classification Methods
Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpreta...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657642/ https://www.ncbi.nlm.nih.gov/pubmed/36366141 http://dx.doi.org/10.3390/s22218444 |
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author | Sreenivasan, Neethu Gargiulo, Gaetano D. Gunawardana, Upul Naik, Ganesh Nikpour, Armin |
author_facet | Sreenivasan, Neethu Gargiulo, Gaetano D. Gunawardana, Upul Naik, Ganesh Nikpour, Armin |
author_sort | Sreenivasan, Neethu |
collection | PubMed |
description | Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin. |
format | Online Article Text |
id | pubmed-9657642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96576422022-11-15 Seizure Detection: A Low Computational Effective Approach without Classification Methods Sreenivasan, Neethu Gargiulo, Gaetano D. Gunawardana, Upul Naik, Ganesh Nikpour, Armin Sensors (Basel) Article Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin. MDPI 2022-11-03 /pmc/articles/PMC9657642/ /pubmed/36366141 http://dx.doi.org/10.3390/s22218444 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sreenivasan, Neethu Gargiulo, Gaetano D. Gunawardana, Upul Naik, Ganesh Nikpour, Armin Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title | Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title_full | Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title_fullStr | Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title_full_unstemmed | Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title_short | Seizure Detection: A Low Computational Effective Approach without Classification Methods |
title_sort | seizure detection: a low computational effective approach without classification methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657642/ https://www.ncbi.nlm.nih.gov/pubmed/36366141 http://dx.doi.org/10.3390/s22218444 |
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