Automated Seizure Detection Following Nerve Agent Exposure

描述（由申请人提供）：神经毒剂（NA）攻击后的癫痫发作会导致时间紧迫的紧急情况，可能导致猝死或严重发病率。生存取决于早期和积极的治疗。癫痫发作在体格检查中可能不明显，急救人员通常不能解释脑电图。因此，正如在RFA中所指出的，迫切需要自动脑电图解释和检测癫痫样活动。为了满足这一需求，我们组建了一个跨学科团队，来自无限生物医学技术有限责任公司、美国陆军化学防御医学研究所（USAMRICD）和约翰霍普金斯医学院。该团队在na诱导的神经病理学、癫痫发作的临床管理、定量神经诊断和硬件设计方面拥有丰富的经验。基于这些经验，我们建议开发一种便携式自动癫痫检测系统。它包括一个无线头带和一个手持设备，该设备具有化学缉获矢量（CSV）算法。CSV是一种基于5种光谱和时间分析的“数字指纹”，专门用于识别癫痫形态。这种组合方法创建了一个5维向量来可靠地分类和区分癫痫波形。降噪和伪影抑制在单个算法的水平上结合，同时考虑5维空间中的单个分析。在第一阶段，我们将评估CSV检测癫痫发作的能力。鉴于研究患者的神经毒剂暴露是不可行的，我们将采用两种互补的模型。首先，我们将使用一个已建立的灵长类动物模型，其中包括接触神经毒剂。其次，我们将使用涉及非dna相关病因的癫痫患者的人类模型。我们将使用接收算子特征（ROC）曲线研究单个分量算法和组合CSV的性能。第一阶段的主要里程碑是建立基于roc分析的最佳化学缉获检测算法。这个快速通道应用程序的第二阶段涉及进一步细化CSV，以产生一个三级指标。我们将区分癫痫发作（红色）、脑周活动（黄色）和正常脑电图（绿色）。CSV将在灵长类动物模型和人类癫痫患者中进行前瞻性评估。该技术将被封装到一个系统中，该系统具有一个自粘电极头带和一个手持设备。最后，整个系统将在真实的化学应急演习中进行验证。接触神经毒剂是一种严重的威胁，可能导致脑损伤和死亡。我们将开发一个自动化系统来诊断脑电图发作活动在大脑中，以帮助照顾攻击受害者。

DESCRIPTION (provided by applicant): Seizures following a nerve agent (NA) attack induce a time-critical emergency which can lead to sudden death or severe morbidity. Survival is dependent upon early and aggressive therapy. Seizures may not be evident from physical examination, and emergency personnel typically cannot interpret the electroencephalogram. Thus, as noted in the RFA, there is a pressing need for automated EEG interpretation and detection of epileptiform activity. In order to respond to this need, we assembled an interdisciplinary team from Infinite Biomedical Technologies, LLC, the United States Army Medical Research Institute of Chemical Defense (USAMRICD), and the Johns Hopkins School of Medicine. Together, the team has broad experience with NA-induced neuropathology, clinical management of seizures, quantitative neurodiagnostics, and hardware design. Based on this experience, we propose the development of a portable system for automated seizure detection. It includes a wireless headband and a handheld device which features a Chemical Seizure Vector (CSV) algorithm. The CSV is a "digital fingerprint" based on 5 spectral and temporal analyses specifically selected to recognize seizure morphology. This combinatorial method creates a 5-dimensional vector to reliably classify and discriminate seizure waveforms. Noise-reduction and artifact rejection are incorporated both at the level of the individual algorithm and upon simultaneous consideration of the individual analyses in 5-dimensional space. During Phase I we will evaluate the ability of CSV to detect the presence of seizures. Given that it is not feasible to study nerve agent exposure in patients, we will employ two complementary models. First, we will use an established primate model which incorporates exposure to a nerve agent. Second, we will use a human model involving seizure patients of a non-NA-associated etiology. We will investigate the performance of the individual component algorithms and the combined CSV using Receiver Operator Characteristic (ROC) curves. The primary milestone for Phase I is creation of an optimal chemical seizure detection algorithm based on the analysis of the ROCs. Phase II of this Fast-track application involves further refinement of CSV to produce a three-level indicator. We will discriminate seizures (RED) from peri-ictal activity (YELLOW) and the normal EEG (GREEN). CSV will be tested in a prospective evaluation in the primate model and in human seizure patients. The technology will be packaged into a system which features a self-adhesive electrode headband and a handheld device. Finally, the entire system will be validated in a realistic setting during live chemical emergency drills. Nerve agent exposures are a serious threat and can cause brain injury and death. We will develop an automated system to diagnose EEG seizure activity in the brain to help care for attack victims.