Validation of a Novel Automated Seizure Detection and EEG Trending System for Con

描述(申请人提供)：对危重病人非惊厥性发作(NCSs)发生率的认识增加，导致重症监护病房(ICU)对持续脑电监测的需求日益增长。然而，在ICU扩大使用连续脑电监测的最大挑战之一是难以提供脑电专家对临床数据的及时审查。因此，尽管脑电被连续记录，但在专业人员对原始脑电进行彻底审查后，ICU医生可能要到几个小时后才能注意到癫痫的发生。最终结果是患者护理受到影响，无法做出适当的实时治疗决定。自动癫痫检测软件有时用于协助脑电检查过程。然而，现有的癫痫检测软件对于大多数ICU患者来说是不够的，因为脑病患者会出现异常的背景脑电和高度可变的癫痫发作放电。这些脑电模式与癫痫监测单元(EMU)中出现的模式有很大不同。因此，商业软件在用于重症护理患者时表现不佳，导致癫痫发作遗漏和错误检测的高发生率。虽然现有的脑电趋势软件更有用，但现有算法存在显著的技术局限性：1)无法显示BREF、局灶性或缓慢演变、低频发作的明显变化，2)区分需要紧急治疗的NCSs与其他异常(通常不使用抗癫痫药物治疗)的特异性较低，以及3)临床实用的科学证据有限-没有FDA批准用于ICU。这个SBIR项目的总体目标是开发和商业化一个准确、可靠和用户友好的ICU癫痫监测和警报系统CereScope。该系统将采用一种新的自动癫痫检测算法ICU-ASDA，具有高灵敏度(85%)和低误检率(&lt；0.2/小时或5/天)。为了提高系统的灵敏度，它将 还将与伪像减少的新型定量脑电(QEEG)趋势相连接，这是一种癫痫指数(SI)，有助于通过视觉检查快速识别潜在的癫痫发作模式。CereScope“将自动创建和传输包含检测到的事件的数字图形文件，以供专家立即审查。在完成了ICU-ASDA的设计和培训研究后，在这个第一阶段项目中，我们提出了一项临床研究，以统计评估和验证算法的性能，以满足FDA的要求。此外，我们将在四个神经ICU进行研究，以调查如何将qEEG趋势与检测算法的结果相结合，以提高系统的整体性能。这项第一阶段可行性研究的具体目标是：1)进行一项临床研究，以评估一种新的癫痫检测算法ICU-ASDA在急性疾病成人患者脑电记录中的性能(灵敏度和误检率)，以及2)当与ICU-ASDA一起使用时，研究新的qEEG趋势在提高在长期ICU脑电记录中识别NCSs方面的应用。CereScope“系统的成功商业化将改善对危重患者癫痫发作的识别和管理。 公共卫生相关性：对危重病人高发病率癫痫的认识增加，导致重症监护病房(ICU)对持续脑监测的需求日益增长。然而，在ICU中扩大连续脑监测的最大挑战之一是专家难以及时审查大量脑电(Brain Electric Activity Signal)数据，导致无法做出适当的实时治疗决策和患者护理。这个SBIR项目的总体目标是开发一种新的脑电分析系统，使ICU的医生、技术人员和护士能够快速识别癫痫发作以及大脑功能的其他异常变化。

DESCRIPTION (provided by applicant): Increased recognition of the incidence of nonconvulsive seizures (NCSs) in critically ill patients has led to a growing demand for continuous EEG monitoring in Intensive Care Units (ICUs). However, one of the biggest challenges to the expanding use of continuous EEG monitoring in the ICU lies in the difficulty of providing a timely review of clinical data by EEG experts. As a result, though EEG is being recorded continuously, ICU physicians may not be alerted to the occurrence of seizures until several hours later, following thorough review of the raw EEG by specialized personnel. The end result is compromised patient care and the inability to make appropriate real-time treatment decisions. Automated seizure detection software is occasionally used to assist in the EEG review process. However, existing seizure detection software is inadequate for most ICU patients because of the abnormal background EEG and highly variable seizure discharges that occur in encephalopathic patients. These EEG patterns differ greatly from those patterns that occur in epilepsy monitoring units (EMUs). As a result, commercially available software performs poorly when used with critical care patients, resulting in missed seizures and a high incidence of false detections. While available EEG-trending software is more useful, there are significant technical limitations in existing algorithms: 1) inability to show clear changes for bref, focal or slowly evolving, low frequency seizures, 2) low specificity in differentiating NCSs that require urgent treatment from other abnormalities, which are usually not treated with anti-seizure medicines, and 3) limited scientific evidence of clinical utility - none are FDA approved for ICU use. The overall goal of this SBIR project is to develop and commercialize an accurate, reliable, and user-friendly ICU seizure monitoring and alert system, CereScope". The system will feature a novel automated seizure detection algorithm, ICU-ASDA, with a high sensitivity (> 85 percent) and low false detection rate (< 0.2/hr or 5/day). To enhance the system sensitivity, it will also be interfaced with artifact-reduced novel quantitative EEG (qEEG) trending, a Seizure Index (SI) which facilitates rapid recognition of potential seizure patterns by visual inspection. CereScope" will automatically create and transmit digital graphic files containing detected events for immediate expert review. Having completed the design and training studies for the ICU-ASDA, in this Phase I project we propose a clinical study to statistically evaluate and validate the performance of the algorithm that will meet the FDA's requirements. In addition, we will conduct studies at four Neuro-ICUs to investigate how combining qEEG trending with the results from the detection algorithm can enhance overall performance of the system. The specific aims of this Phase I feasibility study are: 1) Conduct a clinical study to evaluate the performance (sensitivity and false detection rate) of a novel seizure detection algorithm, ICU-ASDA, in EEG recordings from acutely ill adult patients, and 2) Investigate the utility of novel qEEG trends for enhancing sensitivity in identifying NCSs in long-term ICU EEG recordings when used in conjunction with the ICU-ASDA. Successful commercialization of the CereScope" system will improve the recognition and management of seizures in critically ill patients. PUBLIC HEALTH RELEVANCE: Increased recognition of the high incidence of seizures in critically ill patients has led to a growing demand for continuous brain monitoring in Intensive Care Units (ICUs). However, one of the biggest challenges to the expanding use of continuous brain monitoring in the ICU is the difficulty of providing a timely review of the high volumes of EEG (brain electrical activity signal) data by experts, resulting in an inability to make appropriae real-time treatment decisions and compromised patient care. The overall goal of this SBIR project is to develop a novel system for EEG analysis that will allow ICU physicians, technicians, and nurses to rapidly identify seizures as well as other abnormal changes in brain function.