PFI-TT: Software for Automated Real-time Electroencephalogram Seizure Detection in Intensive Care Units

这个PFI项目的更广泛的影响/商业潜力是，它将改善重症监护病房(ICU)中神经科患者的临床结果。虽然在ICU中使用连续脑电(EEG)采集的数据记录成本低，并且为指导临床决策提供了丰富的信息来源，但由于手动分析需要太长时间，因此经常不使用。这项拟议的技术将能够实时评估脑电，以便在癫痫等临床相关事件发生时向医生发出警报。这将通过允许医生以更及时、更准确的方式干预药物来改善患者的预后。这项工作还将产生更广泛的影响，提高科学对机器学习如何具体应用于神经信号处理的基本原理的理解，目前这是一个知之甚少的领域。拟议的项目将使能够检测重症监护病房患者癫痫发作和异常大脑活动的软件技术能够并加速商业化。这将通过三项主要任务来实现。在第一个任务中，现有的癫痫检测软件目前离线工作，将转换为实时工作，目标延迟为20秒，以检测癫痫发作。这将通过智能内存处理和开发低延迟、高度优化的后处理算法来实现。第二项任务将加强现有的癫痫检测代码，使其在临床可接受的灵敏度和误警率水平下运行。这将通过在更加多样化和复杂的脑电数据库上重新训练我们的算法来实现，以便使软件暴露在尽可能多的癫痫发作呈现的变化中。在第三项也是最后一项任务中，将进行广泛的软件测试，以优化机器学习配置，使任务1和任务2中获得的收益最大化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this PFI project is that it will lead to improved clinical outcomes for neurological patients in intensive care units (ICUs). Although the data acquired using continuous electroencephalography (EEG) in the ICU is inexpensive to record and a rich source of information for guiding clinical decision making, it is often not used because it takes too long to be analyzed manually. The proposed technology will be capable of evaluating EEGs in real-time in order to alert doctors when clinically relevant events such as seizures occur. This will improve patient outcomes by allowing doctors to intervene with medications in a timelier and more precise fashion. This work will also have the broader impact of improving science's understanding of the fundamentals of how machine learning can be applied specifically to neural signal processing, which is currently a poorly understood area. The proposed project will enable and accelerate the commercialization of software technology that detects seizures and abnormal brain activity in Intensive Care Unit patients. This will be accomplished with three main tasks. In the first task, the existing seizure detection software, which currently works offline, will be converted to work in real-time with a target latency of 20 seconds to detect a seizure. This will be accomplished through intelligent memory handling and by developing a low-latency, highly optimized post-processing algorithm. The second task will strengthen the existing seizure detection code to operate at clinically acceptable levels of sensitivity and false alarm rates. This will be achieved by retraining our algorithms on a significantly more diverse and complex EEG database in order to expose the software to as many variations of seizure presentation as possible. In the third and final task, extensive software testing will be conducted in order to optimize the machine learning configuration that maximizes the gains achieved in Tasks 1 and 2.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.