An algorithm for seizure onset detection using intracranial EEG
This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously max...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/100243 https://orcid.org/0000-0003-0992-0906 |
| _version_ | 1811083890645794816 |
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| author | Kharbouch, Alaa Shoeb, Ali Cash, Sydney S. Guttag, John V. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Kharbouch, Alaa Shoeb, Ali Cash, Sydney S. Guttag, John V. |
| author_sort | Kharbouch, Alaa |
| collection | MIT |
| description | This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. |
| first_indexed | 2024-09-23T12:41:15Z |
| format | Article |
| id | mit-1721.1/100243 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:41:15Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1002432022-10-01T10:30:53Z An algorithm for seizure onset detection using intracranial EEG Kharbouch, Alaa Shoeb, Ali Cash, Sydney S. Guttag, John V. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Kharbouch, Alaa Guttag, John V. This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Center for Integration of Medicine and Innovative Technology Quanta Computer (Firm) Cyberonics, Inc. 2015-12-14T19:50:19Z 2015-12-14T19:50:19Z 2011-11 2011-08 Article http://purl.org/eprint/type/JournalArticle 15255050 1525-5069 http://hdl.handle.net/1721.1/100243 Kharbouch, Alaa, Ali Shoeb, John Guttag, and Sydney S. Cash. “An Algorithm for Seizure Onset Detection Using Intracranial EEG.” Epilepsy & Behavior 22 (December 2011): S29–S35. https://orcid.org/0000-0003-0992-0906 en_US http://dx.doi.org/10.1016/j.yebeh.2011.08.031 Epilepsy & Behavior Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Kharbouch, Alaa Shoeb, Ali Cash, Sydney S. Guttag, John V. An algorithm for seizure onset detection using intracranial EEG |
| title | An algorithm for seizure onset detection using intracranial EEG |
| title_full | An algorithm for seizure onset detection using intracranial EEG |
| title_fullStr | An algorithm for seizure onset detection using intracranial EEG |
| title_full_unstemmed | An algorithm for seizure onset detection using intracranial EEG |
| title_short | An algorithm for seizure onset detection using intracranial EEG |
| title_sort | algorithm for seizure onset detection using intracranial eeg |
| url | http://hdl.handle.net/1721.1/100243 https://orcid.org/0000-0003-0992-0906 |
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