Faculty of Medicine, Epilepsy Center, Medical Center-University of Freiburg, University of Freiburg, Breisacher Str. 64, 79106 Freiburg, Germany.
J Neural Eng. 2019 Aug;16(4):041001. doi: 10.1088/1741-2552/ab094a. Epub 2019 Feb 21.
Current treatment concepts for epilepsy are based on continuous drug delivery or electrical stimulation to prevent the occurrence of seizures, exposing the brain and body to a mostly unneeded risk of adverse effects. To address the infrequent occurrence and short duration of epileptic seizures, intelligent implantable closed-loop devices are needed which are based on a refined analysis of ongoing brain activity with highly specific and fast detection algorithms to allow for timely, ictal interventions. Since the development and FDA approval of a first closed loop neurostimulation device relying on simple threshold-based approaches, machine learning approaches became widely available, probably outperformed in the near future by deep convolutional neural networks, which already showed to be extremely successful in pattern recognition in images and partly in signal analysis. Handcrafted features or rules defined by experts become replaced by systematic feature selection procedures and systematic hyperparameter search approaches. Training of these classifiers augments the need of large databases with intracranial EEG recordings, which is partly given by existing databases but potentially can be replaced by continuously transferring data from implanted devices and their publication for research purposes. Already in early design states, the final target hardware must be taken into account for algorithm development. Size, power consumption and, as a consequence, limited computational resources given by low power microcontrollers, FPGAs and ASICS limit the complexity of feature computation, classifier complexity, and the numbers and complexity of layers of deep neuronal networks. Novel approaches for early seizure detection will be a key module for new generations of closed-loop devices together with improved low power implant hardware and will provide together with more efficient intervention paradigms new treatment options for patients with difficult to treat epilepsy.
目前针对癫痫的治疗方法主要包括持续药物输送或电刺激,以防止癫痫发作,从而使大脑和身体面临不必要的不良反应风险。为了解决癫痫发作的偶发性和短暂性问题,需要智能植入式闭环设备,该设备基于对持续脑活动的精细分析,采用高度特异性和快速检测算法,以便及时进行癫痫干预。自第一台基于简单阈值方法的闭环神经刺激设备的开发和 FDA 批准以来,机器学习方法已得到广泛应用,在不久的将来可能会被深度卷积神经网络所超越,后者在图像模式识别和部分信号分析方面已经取得了巨大成功。手工制作的特征或专家定义的规则逐渐被系统的特征选择过程和系统的超参数搜索方法所取代。这些分类器的训练需要有颅内 EEG 记录的大型数据库,而这部分可以从现有的数据库中获取,但也可以通过从植入设备中不断传输数据并将其用于研究目的来替代。在早期设计阶段,就必须考虑最终的目标硬件来开发算法。尺寸、功耗以及由此产生的低功耗微控制器、FPGA 和 ASIC 所提供的有限计算资源,限制了特征计算、分类器复杂性以及深度神经网络的层数和复杂性。早期癫痫检测的新方法将成为新一代闭环设备的关键模块,同时还需要改进低功耗植入硬件,并与更有效的干预范式一起为治疗困难的癫痫患者提供新的治疗选择。