Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field
To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between i...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Fundamental Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667325824002656 |
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| author | Zhikai Yu Binghao Yang Penghu Wei Hang Xu Yongzhi Shan Xiaotong Fan Huaqiang Zhang Changming Wang Jingjing Wang Shan Yu Guoguang Zhao |
| author_facet | Zhikai Yu Binghao Yang Penghu Wei Hang Xu Yongzhi Shan Xiaotong Fan Huaqiang Zhang Changming Wang Jingjing Wang Shan Yu Guoguang Zhao |
| author_sort | Zhikai Yu |
| collection | DOAJ |
| description | To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy. |
| format | Article |
| id | doaj-art-5bbb37b55d6f4af5949d28fd9fe95b35 |
| institution | Kabale University |
| issn | 2667-3258 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Fundamental Research |
| spelling | doaj-art-5bbb37b55d6f4af5949d28fd9fe95b352025-01-29T05:02:37ZengKeAi Communications Co. Ltd.Fundamental Research2667-32582025-01-0151103114Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy fieldZhikai Yu0Binghao Yang1Penghu Wei2Hang Xu3Yongzhi Shan4Xiaotong Fan5Huaqiang Zhang6Changming Wang7Jingjing Wang8Shan Yu9Guoguang Zhao10Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, ChinaLaboratory of Brain Atlas and Brain Inspired Intelligence, Institute of Automation, Chinese Academy of Science, Beijing 100190, China; School of Future Technology, University of Chinese Academy of Science, Beijing 101408, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, China; Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, China; Corresponding authors.Laboratory of Brain Atlas and Brain Inspired Intelligence, Institute of Automation, Chinese Academy of Science, Beijing 100190, China; School of Future Technology, University of Chinese Academy of Science, Beijing 101408, China; Corresponding authors.Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Clinical Research Center for Epilepsy, Capital Medical University, Beijing 100053, China; Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, China; Corresponding authors.To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.http://www.sciencedirect.com/science/article/pii/S2667325824002656BiomarkerCritical stateFeedback electrical stimulationEpilepsyBrain computer interface |
| spellingShingle | Zhikai Yu Binghao Yang Penghu Wei Hang Xu Yongzhi Shan Xiaotong Fan Huaqiang Zhang Changming Wang Jingjing Wang Shan Yu Guoguang Zhao Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field Fundamental Research Biomarker Critical state Feedback electrical stimulation Epilepsy Brain computer interface |
| title | Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| title_full | Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| title_fullStr | Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| title_full_unstemmed | Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| title_short | Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| title_sort | critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field |
| topic | Biomarker Critical state Feedback electrical stimulation Epilepsy Brain computer interface |
| url | http://www.sciencedirect.com/science/article/pii/S2667325824002656 |
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