Kremen Vaclav, Duque Juliano J, Brinkmann Benjamin H, Berry Brent M, Kucewicz Michal T, Khadjevand Fatemeh, Van Gompel Jamie, Stead Matt, St Louis Erik K, Worrell Gregory A
Department of Neurology, Mayo Systems Electrophysiology Laboratory, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Zikova street 1903/4, 166 36 Prague 6, Czech Republic. Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
J Neural Eng. 2017 Apr;14(2):026001. doi: 10.1088/1741-2552/aa5688. Epub 2017 Jan 4.
Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery.
Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier.
Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature.
Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.
自动行为状态分类有助于下一代植入式癫痫设备。在本研究中，我们探讨了利用宽带颅内脑电图（iEEG）对接受癫痫手术评估的患者进行自动清醒（AW）和慢波睡眠（SWS）分类的可行性。
纳入7例（年龄[公式：见正文]，4例女性）接受颅内深部电极植入以进行iEEG监测的患者的数据。使用来自单个电极的跨越多个频带的频谱功率特征（0.1 - 600 Hz）来训练和测试支持向量机分类器。
使用来自单个电极的多个频谱功率特征，7名受试者的正常组织分类准确率为97.8 ± 0.3%，癫痫组织分类准确率为89.4 ± 0.8%。发现放置在正常颞叶新皮质的电极的频谱功率特征对于睡眠 - 觉醒状态分类比位于正常海马体的电极更有用（准确率90.8 ± 0.8%）（87.1 ± 1.6%）。高频带特征（涟漪（80 - 250 Hz）、快速涟漪（250 - 600 Hz））中的频谱功率在AW和SWS分类中表现与表现最佳的贝格尔带（阿尔法、贝塔、低伽马）相当，使用单个电极触点和单个频谱特征时准确率⩾90%。
对于未来计算能力、内存和电极数量有限的植入式癫痫设备，清醒和SWS的自动分类应被证明是有用的。应用包括量化患者睡眠模式以及与行为状态相关的检测、预测和电刺激治疗。
