Automated Seizure Detection Following Nerve Agent Exposure

描述(由申请人提供)：神经毒剂(NA)攻击后的癫痫导致时间紧迫的紧急情况，可能导致猝死或严重疾病。生存依赖于早期和积极的治疗。从体检中可能看不到明显的癫痫发作，急救人员通常无法解读脑电。因此，正如RFA中指出的那样，迫切需要对癫痫样活动进行自动脑电解释和检测。为了应对这一需求，我们组建了一个由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组成的跨学科团队。总而言之，该团队在NA诱导的神经病理学、癫痫的临床管理、定量神经诊断和硬件设计方面拥有广泛的经验。基于这一经验，我们建议开发一种便携式癫痫自动检测系统。它包括一个无线头带和一个具有化学癫痫向量(CSV)算法的手持设备。CSV是一种基于5种光谱和时间分析的“数字指纹”，专门用于识别癫痫的形态。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。噪声抑制和伪影抑制被结合在个体算法的水平上，并且同时考虑了5维空间中的个体分析。在第一阶段，我们将评估CSV检测癫痫发作的能力。鉴于研究患者暴露神经毒剂是不可行的，我们将采用两个互补的模型。首先，我们将使用已建立的灵长类动物模型，其中包括接触神经毒剂。其次，我们将使用一个涉及非NA相关病因的癫痫患者的人体模型。我们将使用接收算子特征(ROC)曲线来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根据对ROC的分析，创建一种最佳的化学品扣押检测算法。这一快速通道应用程序的第二阶段涉及进一步改进CSV，以产生一个三级指标。我们将癫痫发作(红色)与发作周活动(黄色)和正常脑电(绿色)区分开来。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。这项技术将被封装到一个系统中，该系统具有自动粘合电极头带和手持设备。最后，整个系统将在真实的化学应急演练中进行验证。接触神经毒剂是一个严重的威胁，可能会导致脑损伤和死亡。我们将开发一个自动系统来诊断大脑中的脑电发作活动，以帮助照顾袭击受害者。

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.