Localizing epileptogenic regions using high-frequency oscillations and machine learning
| العنوان: | Localizing epileptogenic regions using high-frequency oscillations and machine learning |
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| المؤلفون: | Gregory A. Worrell, Federico Raimondo, Zachary J. Waldman, Diego Fernández Slezak, Jerome Engel, Anatol Bragin, Michael R. Sperling, Mustafa Donmez, Shennan A. Weiss, Richard J. Staba |
| المصدر: | Biomarkers in medicine, vol 13, iss 5 |
| بيانات النشر: | Future Medicine Ltd, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | phase–amplitude coupling, Clinical Biochemistry, Review, Brain tissue, Neurodegenerative, Medical Biochemistry and Metabolomics, 030204 cardiovascular system & hematology, HFO, computer.software_genre, Machine Learning, Epilepsy, 0302 clinical medicine, Drug Discovery, Medicine, Epilepsy surgery, ripple, Brain, artificial intelligence, fast ripple, 030220 oncology & carcinogenesis, Neurological, epilepsy surgery, CIENCIAS NATURALES Y EXACTAS, high frequency oscillation, PHASE-AMPLITUDE COUPLING, seizure, Clinical Sciences, High frequency oscillation, Machine learning, Medicinal and Biomolecular Chemistry, 03 medical and health sciences, high-frequency oscillation, wavelet, Humans, Oncology & Carcinogenesis, epileptiform spike, business.industry, phase amplitude coupling, Biochemistry (medical), Neurosciences, Neurophysiology, medicine.disease, Ciencias de la Computación, Brain Disorders, Ciencias de la Computación e Información, Refractory epilepsy, Artificial intelligence, business, computer, Medical Informatics, Biomarkers, Phase amplitude coupling |
| الوصف: | Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies. Fil: Weiss, Shennan A.. Thomas Jefferson University; Estados Unidos Fil: Waldman, Zachary. Thomas Jefferson University; Estados Unidos Fil: Raimondo, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Donmez, Mustafa. Thomas Jefferson University; Estados Unidos Fil: Worrell, Gregory. Mayo Systems Electrophysiology Laboratory; Estados Unidos Fil: Bragin, Anatol. David Geffen School of Medicine at UCLA; Estados Unidos Fil: Engel, Jerome. David Geffen School of Medicine at UCLA; Estados Unidos Fil: Staba, Richard. David Geffen School of Medicine at UCLA; Estados Unidos Fil: Sperling, Michael. Thomas Jefferson University; Estados Unidos |
| وصف الملف: | application/pdf |
| تدمد: | 1752-0371 1752-0363 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9efdc47a9d37dc720c94948817a3a9dTest https://doi.org/10.2217/bmm-2018-0335Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b9efdc47a9d37dc720c94948817a3a9d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17520371 17520363 |
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