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.

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Temple University
United States
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