Objective: There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus- a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep). Approach: Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo EEG evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum (WRS) method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states. Main Results: 79 seizures (10 patients) and 158 segments (approx. 4 hours) of interictal stereo EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 seconds). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 seconds after seizure onset. The random forest (accuracy 93.9 % and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states. Significance: These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.

中文翻译：

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使用非线性机器学习可靠地检测人丘脑前核局灶性发作

目的：尚未开发出癫痫发生皮层以外的大脑区域的癫痫发作检测算法。这项研究旨在证明从人类丘脑（远离癫痫发生皮层的皮层下区域）记录的局部场电位（LFP）对癫痫发作和发作间状态进行分类的可行性。我们测试了一种假设，即从丘脑前核（ANT）记录的LFP中提取的基于光谱和熵的特征可以区分其发作期与其他发作期状态（包括清醒，睡眠）。方法：使用两种监督的机器学习工具（随机森林和随机厨房水槽）评估光谱的性能（离散小波变换-DWT），和时域（多尺度熵-MSE）功能对癫痫手术进行立体脑电图评估的患者发作间期发作分类。在IRB的监督下，从具有抗药性颞叶癫痫的成年人中，从ANT上记录了场电位。使用线长和肉眼检查在ANT中确认癫痫发作。Wilcoxon秩和（WRS）方法用于测试癫痫发作和发作间（清醒和睡眠）状态之间光谱模式的差异。主要结果：分析了79例癫痫发作（10例患者）和158个节段的立体脑电图数据（约4小时）。ANT的线长平均发作检测潜伏期在发作类型之间有所不同（范围为5-34秒）。然而，癫痫发作后的最初20秒内，ANT中的DWT和MSE对所有癫痫发作类型均显示了显着变化。随机森林（准确度93.9％，假阳性4.6％）和随机厨房水槽（准确度97.3％，假阳性1.8％）对癫痫发作和发作间状态进行了分类。意义：这些结果表明，可以训练从丘脑LFP提取的特征以检测癫痫发作，这些发作可用于监测癫痫发作计数和闭环癫痫发作流产干预措施。