Park Seong-Cheol, Chung Chun Kee
Department of Neurosurgery, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea.
Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
J Neurophysiol. 2018 Jun 1;119(6):2265-2275. doi: 10.1152/jn.00225.2017. Epub 2018 Mar 7.
The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13-44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4-25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4-25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17-36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert's manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.
本研究的目的是引入一种新的机器学习方法，该方法以切除性癫痫手术的结果（定义为癫痫发作的有无）为指导，以改善对新皮质癫痫发作间期病理活动的数据挖掘。分析了39例药物难治性新皮质癫痫患者的皮质脑电图。我们分别分析了从0.9到800Hz的38个频率，包括高频活动和低频活动，以选择与癫痫发作结果相关的频段。使用了一种基于幅度-持续时间-数量阈值的自动检测器。为每位患者选择了8分钟的发作间期皮质脑电图数据集。在20例患者的第一个训练数据集中，基于使用遗传算法检测到的活动切除百分比的排名，对自动检测器进行了优化，以最佳地区分无癫痫发作组和非无癫痫发作组。在19例患者的不同数据集中对优化进行了验证。有16例（41%）无癫痫发作患者。平均随访时间为21±11个月（范围13 - 44个月）。验证后，与癫痫发作结果显著相关的低频活动频率为5.8、8.4 - 25、30、36、52和75Hz，高频活动频率为108和800Hz。切除5.8、8.4 - 25、108和800Hz的活动持续改善了癫痫发作结果。17 - 36、52和75Hz活动对癫痫发作结果的切除效果根据阈值而变化。我们通过结果导向的机器学习，采用数据驱动的方法，开发并验证了一种用于监测新皮质癫痫发作间期病理和抑制性/生理性活动的自动检测器。新内容与值得注意之处基于癫痫发作结果的结果导向机器学习用于改善对发作间期皮质脑电图低频和高频活动的检测。该方法比文献中报道的其他方法能更好地分离癫痫发作结果组。自动检测器无需人工干预和先验信息即可训练。它仅基于客观的癫痫发作结果数据，不依赖专家的手动标注。使用该方法，我们能够发现并表征病理和抑制性活动。
