I-Corps: AutoEEG-enhancing productivity by autoscanning EEG signals

脑电图（eeg）是最普遍的神经诊断工具；他们需要一个训练有素的神经学家来分析他们。长期脑电图监测用于诊断罕见事件，如癫痫发作，如果没有决策软件的支持，很难或不可能手动扫描。开发便携式独立诊断工具（可用于接触性运动等新兴市场）非常困难；小型或独立的医疗机构往往缺乏现场诊断的专业知识，因此会损失收入。目前，商业化临床决策支持工具的创新很少，而快速诊断脑相关损伤和疾病的全球市场正在增长。提出的AutoEEGTM是一种软件工具，通过自动扫描脑电图信号和标记需要临床医生进一步审查的信号部分来提高工作效率。该工具将需要人工审查的数据量减少了两个数量级，在临床环境中提供了实质性的生产力提高。提出的临床决策支持工具是基于成熟的，先进的，深度学习技术。它减少了诊断时间，减少了错误，并且足够轻量级，可以在便携式独立平台上运行。该技术能够识别信号中的EEG事件，并随后根据检测到的事件提供总结其发现的报告。转录的脑电图信号可以从任何便携式计算设备上查看。它还具有从数据中学习的能力，有助于未来的决策，提供实时反馈以帮助诊断，并且，对于接受长期监测的患者，当发现异常信号时，它会发出警报。这个市场领先的产品将(1)使临床神经科医生采用基于量的业务模式，减少分析脑电图所花费的时间，从而增加计费；(2)允许制药公司在临床试验中定量评估神经激活的变化；(3)允许神经科医生在这个经过验证的决策支持工具的基础上订购和计费更长期的监测测试；(4)通过提供有意义的实时信号分析，为目前进入市场的商品EEG耳机增加价值。该研究项目有两个关键组成部分：(1)对市场进行详细分析，以了解各种机会，如设备制造商的许可和合同研究组织的离线分析；(2)可用性设计和工程，以了解为临床医生和初级保健医生等潜在用户带来最大价值的分析和用户界面问题。本研究成果将用于强化技术并指导与现有EEG产品的集成。

Electroencephalograms (EEGs) are the most pervasive neural diagnostic tool; they require a highly trained neurologist to analyze them. Long-term EEG monitoring, used to diagnose rare events such as epileptic seizures, is difficult or impossible to scan manually without decision software support. Development of portable standalone diagnostic tools, which can address emerging markets such as contact sports, is highly difficult; and smaller or stand-alone medical practices often lack expertise to conduct diagnostics on-site and accordingly lose revenue. At present, innovation in commercial clinical decision support tools is minimal, whereas the global market for rapidly diagnosing brain-related injury and disease is growing. The proposed AutoEEGTM is a software tool that enhances productivity by auto-scanning EEG signals and flagging sections of the signal that need further review by a clinician. The proposed tool reduces the amount of data needing manual review by two orders of magnitude, offering substantial productivity gains in a clinical setting.The proposed clinical decision support tool is based on proven, advanced, deep learning technology. It reduces time to diagnosis, reduces error and is sufficiently lightweight to run on portable standalone platforms. This technology is able to identify EEG events in the signal and subsequently to provide a report that summarizes its findings based on the event detected. The transcribed EEG signals can be viewed from any portable computing device. It also has the ability to learn from data, helping in future decision making, providing real-time feedback to aid in diagnosis, and, for patients undergoing long-term monitoring, creating an alert when abnormal signals are identified. This market-leading product will (1) Enable clinical neurologists employing a volume-based business mode to decrease the time spent analyzing an EEG and thereby increase billing; (2) Allow pharmas to assess changes quantitatively in neural activation during clinical trials; (3) Allow neurologists to order and bill for substantially more long-term monitoring tests based on this proven decision support tool; and (4) Add value to the commodity EEG headsets currently entering the market by providing meaningful, real-time signal analysis. This research project has two key components: (1) a detailed analysis of the market to understand various opportunities such as licensing to equipment manufacturers and off-line analysis for contract research organizations; and (2) usability design and engineering to understand the analytics and user interface issues that bring most value to potential users such as clinicians and primary care physicians. The outcomes of this research will be used to harden the technology and guide integration into existing EEG products.