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

PFI-TT：重症监护室自动实时脑电图癫痫发作检测软件

• 批准号: 1827565
• 负责人: Iyad Obeid
• 金额: $ 20万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827565&HistoricalAwards=false
• 关键词: PFI; TT; Software; Automated; Real

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.

该PFI项目更广泛的影响/商业潜力在于，它将改善重症监护室（ICU）神经系统患者的临床结局。尽管在ICU中使用连续脑电图（EEG）采集的数据记录成本低廉，并且是指导临床决策的丰富信息来源，但由于手动分析时间太长，因此通常不使用。该技术将能够实时评估EEG，以便在发生癫痫等临床相关事件时提醒医生。这将通过允许医生以更及时和更精确的方式干预药物来改善患者的治疗效果。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专门应用于神经信号处理的基本原理的理解，这是目前人们知之甚少的领域。拟议的项目将实现并加速软件技术的商业化，该技术可以检测重症监护病房患者的癫痫发作和异常大脑活动。这将通过三项主要任务来实现。在第一项任务中，目前离线工作的现有癫痫发作检测软件将被转换为实时工作，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发作检测代码，使其在临床可接受的灵敏度和误报率水平上运作。这将通过在更加多样化和复杂的EEG数据库上重新训练我们的算法来实现，以便使软件尽可能多地暴露于癫痫发作表现的变化。在第三个也是最后一个任务中，将进行广泛的软件测试，以优化机器学习配置，最大限度地提高任务1和任务2中的收益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Issues in the Reproducibility of Deep Learning Results

• DOI: 10.1109/spmb47826.2019.9037840
• 发表时间: 2019-12
• 期刊: 2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
• 作者: S. Jean-Paul;T. Elseify;I. Obeid;Joseph Picone

Objective Evaluation Metrics for Automatic Classification of EEG Events

脑电图事件自动分类的客观评估指标

• 发表时间: 2021
• 期刊: Biomedical Signal Processing: Innovation and Applications
• 作者: Shah, Vinit;Golmohammadi, Meysam;Obeid, Iyad;Picone, Joseph
• 通讯作者: Picone, Joseph

Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events

TUH EEG 语料库的最新进展：改进伪影和癫痫样事件的评估者间协议

• 发表时间: 2021
• 期刊: Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB
• 作者: Buckwalter, Grace Chhin

Improving the Quality of the TUSZ Corpus

提高 TUSZ 语料库的质量

• 发表时间: 2020
• 期刊: IEEE Signal Processing in Medicine and Biology Symposium (SPMB
• 作者: Rahman, Safwanur Hamid

The Temple University Hospital EEG Corpus: Annotation Guidelines

天普大学医院脑电图语料库：注释指南

• 发表时间: 2020
• 期刊: Institute for Signal and Information Processing Report
• 作者: Ochal, Domenic;Rahman, Safwanur;Ferrell, Sean;Elseify, Tarek;Obeid, Iyad;Picone, Joseph
• 通讯作者: Picone, Joseph