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。癫痫发作的间歇性和不可预测性使住院治疗变得复杂。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。我们建议将该算法商业化为一个用户友好的癫痫发作监测和警报（SMA）系统，用于临床研究以及在医院癫痫监测和重症监护病房的床边使用。为了使这样的系统在临床上有用，检测算法必须以高灵敏度和低误检测率来执行是必要的。在第I阶段，我们将开发和测试SMA原型，该原型将（1）读取和处理Optima癫痫发作检测算法中设计的在线实时EEG信号;（2）在检测到事件时生成警报，以及（3）将包含检测到的事件的选定EEG片段发送给医生进行验证。该原型将作为专门为两种临床应用设计的后续设备的基础：（1）癫痫监测单元和（2）重症监护室。该SMA器械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治疗。公共卫生相关性：尽管对关键心肺功能的自动监测是所有医院的护理标准，但目前对大脑功能的监测几乎完全依赖于床边临床观察。因此，每天都有大量的亚临床癫痫发作（只有细微的可观察到的变化）未被诊断出来。该项目的主要目标是建立和测试一个非常需要的自动化系统的原型，以提醒未受过神经病学培训的工作人员癫痫发作活动的存在。总体目标是改善癫痫患者的诊断和治疗，特别是在社区医院，那里可能没有经过EEG培训的神经科医生。

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. Inpatient management of seizures is complicated by the fact that they occur intermittently and unpredictably. Researchers at Optima Neuroscience have developed an automated algorithm to accurately detect seizures by analyzing the spatiotemporal patterns of scalp EEG signals. We propose to commercialize this algorithm into a user-friendly seizure monitoring and alert (SMA) system for clincial research as well as for bedside use in hospital epilepsy monitoring and intensive care units. For such a system to be clinically useful, it is imperative that the detection algorithm must perform with a high sensitivity and low false detection rate. In Phase I, we will develop and test an SMA prototype that will (1) read and process on-line real-time EEG signals as designed in the Optima seizure detection algorithm; (2) generate an alert when an event is detected, and (3) send selected EEG segments containing the detected event to the physician for verification. This prototype will serve as the basis for subsequent devices designed specifically for two clinical applications: (1) Epilepsy Monitoring Units, and (2) Intensive Care Units. Successful commercialization of this SMA device will improve inpatient management of seizures by allowing for detection of intermittent and previously misdiagnosed events. PUBLIC HEALTH RELEVANCE: Although automated monitoring for critical heart and lung function is the standard of care in all hospitals, monitoring the function of the brain currently relies almost exclusively upon bedside clinical observations. As a result, a large number of subclinical seizures (only subtle observable changes) go undiagnosed every day. The primary goal of this project is to build and test a prototype for a greatly needed automated system to alert staff untrained in neurology to the presence of seizure activities. The overall goal is to improve the diagnosis and treatment of patients suffering from seizure disorders, particularly in community hospitals where EEG trained neurologist may not be available.