Localizing epileptogenic regions using high-frequency oscillations and machine learning.
Localizing epileptogenic regions using high-frequency oscillations and machine learning.
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DOI：
10.2217/bmm-2018-0335
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发表时间：
2019-04
影响因子：
2.2
通讯作者：
S. Weiss;Zachary Waldman;F. Raimondo;D. Slezak;Mustafa Donmez;G. Worrell;A. Bragin;J. Engel;R. Staba;M. Sperling
中科院分区：
文献类型：
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作者：
S. Weiss;Zachary Waldman;F. Raimondo;D. Slezak;Mustafa Donmez;G. Worrell;A. Bragin;J. Engel;R. Staba;M. Sperling
病理性高频振荡（HFO）是致癫痫脑组织的神经生理学生物标志物。利用HFO进行癫痫手术计划为难治性癫痫患者提供了改善癫痫发作结局的希望。这篇综述讨论了可能的机器学习策略，可以应用于HFO生物标志物，以更好地识别致癫痫区域。我们讨论了HFO率的作用，并利用特征，如明确的HFO属性（频谱内容，持续时间和功率）和相位-振幅耦合，用于区分病理性HFO（pHFO）事件与生理性HFO事件。此外，该综述强调了神经解剖定位在机器学习策略中的重要性。
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.