High Performance Seizure Monitoring and Alert System

描述（由申请人提供）：患者经常住院治疗因癫痫或急性神经损伤（例如创伤、中风、感染以及许多毒性和代谢性疾病）而导致的不受控制的癫痫发作。然而，癫痫发作的住院管理是复杂的，因为它们间歇性地和不可预测地发生，因此患者的癫痫发作不被识别的情况并不罕见。这可能导致不必要的住院时间延长，或更糟的是，治疗延迟和不可逆的脑损伤。因此，非常需要开发一种精确的床旁癫痫监测和警报（SMA）系统。 该SBIR项目的总体目标是将一种准确、可靠且用户友好的基于EEG的癫痫发作监测和警报（EEGSMA）系统商业化，用于需要密切神经监测的患者的临床环境。预期临床环境包括但不限于癫痫监测单元（EMU）、重症监护室（ICU）、急诊科（ED）以及神经科和神经外科患者的普通护理单元。Optima Neuroscience的研究人员开发了一种自动算法，通过分析头皮EEG信号的时空模式来准确检测癫痫发作。该算法被纳入我们的IdentEvent癫痫发作检测软件，该软件于2009年10月16日获得FDA批准。在SBIR项目的第一阶段，我们进一步完成了SMA系统的床旁硬件设计，并将IdentEvent转换为实时应用。SMA系统在模拟实时模式下成功测试，并已完成初步临床可行性测试。 在本II期申请中，我们建议继续SMA系统的临床试验，并扩展EEGSMA系统在急性护理环境中使用的功能，例如，ICU和ED。为了实现这一目标，我们不仅需要开发一个可靠、便携、用户友好的EEG采集模块，而且还需要扩展IdentEvent，以用于儿童和ICU患者。因此，本申请的具体目的是：（1）完成SMA模块在EMU环境中的临床性能评估，（2）设计和测试EEG头盒的前端硬件和软件组件，并将其与SMA系统集成，（3）完成集成EEGSMA系统的临床前测试和试点门诊研究，（4）在EMU中进行集成EEGSMA系统的住院测试，（5）在EMU中的儿科患者（3 ~ 17岁）中测试检测算法（IdentEvent），（6）进一步开发ICU患者的癫痫发作检测算法。 这种EEGSMA设备的成功商业化将通过允许检测间歇性和先前误诊的事件来改善癫痫发作的住院管理。

DESCRIPTION (provided by applicant): Patients are frequently hospitalized for management of uncontrolled seizures due to epilepsy or acute neurological insults such as trauma, stroke, infections, and a number of toxic and metabolic disorders. However, inpatient management of seizures is complicated by the fact that they occur intermittently and unpredictably, and thus it is not infrequent that patients' seizures go unrecognized. This can result in unnecessarily prolonged hospital stays, or worse, delay of treatment and irreversible brain injury. Therefore, there is a great need to develop an accurate bed-side seizure monitroing and alert (SMA) system. The overall goal of this SBIR project is to commercialize an accurate, reliable, and user-friendly EEG-based seizure monitoring and alert (EEGSMA) system for use in clinical settings where patients require close neurological monitoring. Intended clinical settings include but are not limited to, epilepsy monitoring units (EMUs), intensive care units (ICUs), emergency departments (EDs), and general care units for neurology and neurosurgical patients. Researchers at Optima Neuroscience have developed an automated algorithm to accurately detect seizures by analyzing the spatiotemporal patterns of scalp EEG signals. The algorithm was incorporated in our IdentEvent" seizure detection software, which received FDA approval on October 16, 2009. During the Phase I of this SBIR project, we have further completed bed-side hardware design of the SMA system and transformed IdentEvent for real-time application. The SMA system was successfully tested in simulation real-time mode, and initial clinical feasibility testing has been completed. In this Phase II application, we propose to continue the clinical testing for the SMA system as well as expand the functions of the EEGSMA system for use in acute care environments, e.g., ICUs and EDs. To accomplish this, we not only have to develop a reliable, portable, and user-friendly EEG acquisition module that can be set up quickly, but also we need to expand IdentEvent for use in children and for ICU patients. Therefore, the specific aims of this application are: (1) to complete the clinical performance evaluation of the SMA module in an EMU setting, (2) to design and test front end hardware and software components of the EEG head-box and integrate them with the SMA system, (3) to complete pre-clinical testing and pilot outpatient study of the integrated EEGSMA system, (4) to conduct inpatient testing of the integrated EEGSMA system in EMUs, (5) to test the detection algorithm (IdentEvent) on pediatric patients (age 3 ~ 17) in EMUs, and (6) to further develop a seizure detection algorithm for ICU patients. Successful commercialization of this EEGSMA device will improve inpatient management of seizures by allowing for detection of intermittent and previously misdiagnosed events.