Background and Objectives: Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear.
Methods: Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterised the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states (SNSs). We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate.
Results: In most patients, the occurrence or duration of at least one SNS was associated with the time since implantation. Some patients had one or more SNSs that were associated with phases of circadian and/or multidien spike rate cycles. A given SNS's occurrence and duration were usually not associated with the same timescale.
Discussion: Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a SNS's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures, and similar principles likely apply to other neurological conditions.
Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy
University of Illinois（伊利诺伊大学）；Mayo Clinic（梅奥诊所）；Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与控制论研究所）；Czech Technical University in Prague（布拉格捷克理工大学）
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
UCLA Samueli School of Engineering（UCLA 工程学院）；UCLA Mattel Children’s Hospital, David Geffen School of Medicine（UCLA 马特尔儿童医院，大卫·格芬医学院）；Children’s Hospital of Michigan, Wayne State University School of Medicine（密歇根儿童医院，韦恩州立大学医学院）
Low-dimensional representation of brain networks for seizure risk forecasting
Industrial University of Santander（桑坦德工业大学）；Paris Brain Institute (ICM), Inria Paris, INSERM-U1127, CNRS-UMR7225, Sorbonne University, Pitié-Salpêtrière Hospital（巴黎脑研究所（ICM），法国国家信息与自动化研究所巴黎分部，INSERM-U1127，CNRS-UMR7225，索邦大学，萨尔佩特里埃医院）；Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière University Hospital（神经病学系，癫痫科，萨尔佩特里埃大学医院）；Paris Brain Institute, Sorbonne Université – CNRS UMR 7225 – Inserm U1127（巴黎脑研究所，索邦大学–CNRS UMR 7225–Inserm U1127）；CNRS, Pitié-Salpêtrière Hospital（法国国家科学研究中心，萨尔佩特里埃医院）
Evaluation of techniques for predicting seizure Build up
头皮电生理信号分析：脑电图（EEG）、脑磁图（MEG）和深度（脑内EEG）IEEG是划定致痫区（EZ）的一种方法。这些癫痫信号呈现两种不同的活动（振荡和尖峰），它们可以在时间频率平面上重叠。癫痫发作发生的自动识别需要多个预处理步骤。在这项研究中，我们评估了两种滤波技术：平稳小波变换（SWT）和Despikifying，以提取发作前伽马振荡（癫痫发作的生物标志物）。然后，我们使用Jmail et al 2017的时间基集作为预处理步骤来评估这两种技术的性能。此外，我们使用模拟和真实数据的时间-频率和时空映射这两种技术，以预测癫痫发作（在时间和空间）。我们的结论是，SWT可以检测到的振荡，但Despikyfying是更强大的比SWT重建纯发作前伽玛振荡，因此在预测癫痫发作建立。
University of Southern California（南加州大学）；University of Kansas（堪萨斯大学）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Instituto de Telecomunicações（电信研究所）；Texas Tech University（德克萨斯理工大学）
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
State Key Laboratory of Digital Medical Engineering（数字医学工程国家重点实验室）；Key Laboratory of Biomedical Engineering of Hainan Province（海南省生物医学工程重点实验室）；School of Biomedical Engineering Hainan University（海南大学生物医学工程学院）；SynSense Co.Ltd.（SynSense有限公司）；SynSense AG（SynSense股份公司）
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；UniversitätsSpital und Universität Zürich（苏黎世大学医院与苏黎世大学）
在这项工作中，我们提出了一种神经形态系统，该系统首次将神经记录头台与信号-尖峰转换电路和多核尖峰神经网络（SNN）架构结合在同一个模具上，用于记录，处理和检测高频振荡（HFO），这是致痫区的生物标志物。该器件采用标准的0.18 $μ$ m CMOS工艺节点制造，总面积为99 mm $^{2}$。我们证明了它的应用程序HFO检测记录从9例颞叶癫痫患者随后接受癫痫手术的iEEG。芯片在检测任务期间的总平均功耗为614.3 $μ$ W。我们展示了神经形态学系统如何可靠地检测HFO：该系统以最先进的准确性，特异性和灵敏度（分别为78%，100%和33%）预测术后癫痫发作结果。这是第一个可行性研究，旨在使用基于事件的处理器和尖峰神经网络实时、片上识别颅内人体数据中的相关特征。通过向神经记录电路提供“神经形态智能”，所提出的方法将为开发能够直接在手术室中检测HFO区域并改善癫痫手术的癫痫发作结果的系统铺平道路。
