Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA.
Clin Neurophysiol. 2023 Oct;154:129-140. doi: 10.1016/j.clinph.2023.07.012. Epub 2023 Aug 9.
This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.
We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method.
The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification.
HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs.
Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
本研究旨在探索病理高频振荡（HFO）的敏感检测方法，以提高癫痫手术中的癫痫发作结局。
我们分析了 15 名药物难治性局灶性癫痫儿童的发作间期 HFO（80-500 Hz），这些儿童通过硬膜下网格进行了慢性颅内脑电图检查。使用短期能量（STE）和蒙特利尔神经学研究所（MNI）探测器评估 HFO，并检查其与棘波的关联和时频图特征。应用基于深度学习（DL）的分类来纯化病理 HFO。将术后癫痫发作结果与 HFO 切除比率相关联，以确定最佳 HFO 检测方法。
MNI 探测器比 STE 探测器识别出更高比例的病理 HFO，但有些病理 HFO 仅由 STE 探测器检测到。两种探测器检测到的 HFO 具有最高的棘波关联率。使用基于 DL 的分类进行纯化前后，联合探测器（检测由 MNI 或 STE 探测器识别的 HFO）在使用 HFO 切除比率预测术后癫痫发作结果方面优于其他探测器。
标准自动探测器检测到的 HFO 显示出不同的信号和形态特征。基于 DL 的分类有效地纯化了病理 HFO。
增强 HFO 的检测和分类方法将提高其预测术后癫痫发作结果的效用。
