Predicting seizures from local field potentials recorded via intracortical microelectrode arrays.
Predicting seizures from local field potentials recorded via intracortical microelectrode arrays.
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DOI：
10.1109/embc.2016.7592181
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发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Truccolo W
中科院分区：
文献类型：
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作者：
Aghagolzadeh M;Hochberg LR;Cash SS;Truccolo W
需要新的治疗干预来治疗抗癫痫局灶性癫痫发作，导致最近开发的闭环系统控制癫痫发作。一旦系统预测/检测到癫痫发作，则递送电刺激以防止癫痫发作开始或扩散。到目前为止，癫痫发作预测/检测一直限于跟踪非侵入性脑电图（EEG）或颅内EEG（iEEG）信号。在这里，我们检查癫痫发作预测的基础上，从一个小的新皮层补丁记录通过10×10微电极阵列植入一个局灶性癫痫发作的患者的局部场电位（LFPs）。我们制定的癫痫发作（发作）预测问题，在发作间期和发作前的神经活动之间的区别。使用深度卷积神经网络（CNN），我们证明了可以成功地将发作前活动期（80%检测;无假阳性）与癫痫发作前几（2 - 18）分钟的发作间期活动期区分开来。CNN输入特征由在50个频带（0 - 100 Hz; 2 Hz步长）中计算的LFP通道（1秒时间窗口）的频谱功率组成。我们的初步研究结果表明，皮质内LFPs可能是一个有前途的神经信号的癫痫发作预测局灶性癫痫。
The need for new therapeutic interventions to treat pharmacologically resistant focal epileptic seizures has led recently to the development of closed-loop systems for seizure control. Once a seizure is predicted/detected by the system, electrical stimulation is delivered to prevent seizure initiation or spread. So far, seizure prediction/detection has been limited to tracking non-invasive electroencephalogram (EEG) or intracranial EEG (iEEG) signals. Here, we examine seizure prediction based on local field potentials (LFPs) from a small neocortical patch recorded via a 10×10 microelectrode array implanted in a patient with focal seizures. We formulate the seizure (ictal) prediction problem in terms of discriminating between interictal and preictal neural activity. Using deep Convolutional Neural Networks (CNNs), we show that periods of preictal activity can be successfully discriminated (80% detection; no false positives) from periods of interictal activity several (2 – 18) minutes prior to seizure onset. CNN input features consisted of the spectral power of LFP channels (1-second time windows) computed in 50 frequency bands (0 – 100 Hz; 2 Hz steps). Our preliminary results show that intracortical LFPs may be a promising neural signal for seizure prediction in focal epilepsy.