STTR Phase I: Real-time Automatic Analysis of Electroencephalograms in an Intensive Care Environment Using Deep Learning

这个小企业技术转让第一阶段项目的更广泛的影响/商业潜力是使重症监护病房的实时癫痫发作检测？特别是在没有24/7神经科医生覆盖的医院单位，以及时解释扫描结果。ICU环境中关键EEG事件的高性能实时检测将增加重症监护中大脑监测的使用，从而改善患者结局，提高医疗保健效率并减少护理人员的认知负担。目前的自动检测方法遭受无法接受的高误报率，这使护理提供者不堪重负，并且在这种环境中使用有限。一项可靠的服务将为美国4,000多家社区医院的877,500名神经功能受损的重症监护患者提供优质护理。ICU中实时癫痫发作检测的市场机会约为每年8000万美元。 拟议的项目将开发一种用于EEG分析的辅助技术，以支持临床医生在ICU环境中评估EEG信号的医学重要事件。EEG信号的分析需要训练有素的神经学家，并且是耗时且昂贵的，因为识别罕见的临床事件需要分析长数据流。大多数社区医院没有24/7的训练有素的神经科医生，不能提供连续的脑电图监测，以检测神经功能受损患者的非惊厥性癫痫发作。可靠的自动检测通过自动扫描EEG信号和标记需要临床医生进一步检查的信号部分，改善了患者对长期大脑监测的访问。该工具将需要手动审查的数据量减少了两个数量级，在临床环境中提供了大量的生产力提高。该项目将利用一种创新的方法来集成隐马尔可夫模型，深度学习和主动学习，以便从最少量的手动注释数据中快速开发高性能机器学习系统。 由此产生的自动分析将达到95%的癫痫发作检测准确率，误报率为每8小时1次。

The broader impact / commercial potential of this Small Business Technology Transfer Phase I project is enabling real-time seizure detection in intensive care units ? especially units at hospitals without 24/7 neurologist coverage to interpret scans in a timely manner. High performance real-time detection of critical EEG events in an ICU setting will increase the use of brain monitoring in critical care, thereby improving patient outcomes, increasing the efficiency of healthcare and decreasing the cognitive burden placed on caregivers. Current approaches to automatic detection suffer from unacceptably high false alarm rates that overwhelm care providers, and are of limited use in this environment. A reliable service would expand access to quality care for 877,500 neurologically compromised critical care patients in 4,000+ community hospitals in the United States. The market opportunity for real-time seizure detection in the ICU is approximately $80M per year. The proposed project will develop an assistive technology for EEG analysis to support clinicians in evaluating EEG signals for medically important events in an ICU environment. Analysis of EEG signals requires a highly trained neurologist, and is time consuming and expensive since identifying rare clinical events requires analysis of long data streams. Most community hospitals do not have 24/7 access to trained neurologists and can not provide continuous EEG monitoring to detect non-convulsive seizures in neurologically compromised patients. Reliable automatic detection improves patient access to long-term brain monitoring by auto-scanning EEG signals and flagging sections of the signal that need further review by a clinician. The tool reduces the amount of data needing manual review by two orders of magnitude, offering substantial productivity gains in a clinical setting. The project will leverage an innovative approach for integrating hidden Markov models, deep learning and active learning to allow the rapid development of a high performance machine learning system from minimal amounts of manually annotated data. The resulting automatic analysis will achieve 95% detection accuracy for seizures with a false alarm rate of 1 per 8-hour period.