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用于在脑电刺激期间跟踪尖峰、癫痫发作和行为的分布式脑协处理器。

Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

作者信息

Sladky Vladimir, Nejedly Petr, Mivalt Filip, Brinkmann Benjamin H, Kim Inyong, St Louis Erik K, Gregg Nicholas M, Lundstrom Brian N, Crowe Chelsea M, Attia Tal Pal, Crepeau Daniel, Balzekas Irena, Marks Victoria S, Wheeler Lydia P, Cimbalnik Jan, Cook Mark, Janca Radek, Sturges Beverly K, Leyde Kent, Miller Kai J, Van Gompel Jamie J, Denison Timothy, Worrell Gregory A, Kremen Vaclav

机构信息

Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Rochester, MN, USA.

Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA.

出版信息

Brain Commun. 2022 May 6;4(3):fcac115. doi: 10.1093/braincomms/fcac115. eCollection 2022.

Abstract

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

摘要

早期的植入式癫痫治疗设备提供开环电刺激,没有脑传感、计算功能,也没有用于同步患者行为输入的接口。最近的癫痫刺激设备具备脑传感功能,但尚未开发出用于准确跟踪和量化行为及癫痫发作的分析方法。在此,我们描述一种分布式脑协处理器,它在患者、植入式神经刺激与传感设备以及本地和分布式计算资源之间提供直观的双向接口。利用手持设备将连续流式电生理的自动分析与患者报告同步,并与分布式云计算资源集成,以量化在治疗性脑电刺激期间的癫痫发作、发作间期癫痫样放电和患者症状。发作间期癫痫样放电和癫痫发作的分类算法是使用来自9名人类和8只患有癫痫的犬类的长期动态数据开发并进行参数化的,然后前瞻性地应用于两只宠物犬和4名患有耐药性癫痫且生活在自然环境中的人类的样本外测试。准确的癫痫发作日记是癫痫治疗的主要临床结局指标,也是指导脑刺激优化所必需的。本文描述的脑协处理器系统能够跟踪发作间期癫痫样放电、癫痫发作以及与患者行为报告的相关性。未来,放电和癫痫发作与行为的相关性将使我们能够更详细地研究放电和癫痫发作对患者的临床影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0da1/9217965/7e3542a5b3d0/fcac115ga1.jpg

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