A framework for hybrid machine and human computation for the accurate and scalable analysis of human clinical EEG recordings

混合机器和人类计算框架，用于对人类临床脑电图记录进行准确和可扩展的分析

• 批准号: 478468-2015
• 负责人: Law, Edith
• 金额: $ 5.3万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=579533
• 关键词: framework; hybrid; machine; human; computation

Electroencephalography (EEG the measurement of human brain activity using electrodes on the scalp) is a key tool in the diagnosis of epilepsy. It is also a key step in the diagnosis of sleep disorders. Right now, interpretation of EEGs is dependent on specialized neurologists. This limits access to EEG services in smaller communities. Even in large communities, the laborious nature of EEG interpretation and the heavy reliance on specialists imposes substantial costs on the health care system, and can delay diagnosis and treatment. Full automation of EEG interpretation could potentially address these issues. However, attempts to do so have had limited success, in part because some aspects of EEG interpretation such as visual pattern recognition, while relatively easy for trained human experts, are very difficult to automate. Human computing is a field of computer science that aims to combine automated algorithms with judicious application of human input into one framework to leverage the strengths of each while avoiding the limitations of either. Human computing has proven to be a powerful tool to solve many key problems in science that had proven intractable to standard artificial intelligence approaches. We propose to build a system combining state of the art machine learning algorithms and human computing approaches to enable rapid, scalable, accurate interpretation of human EEGs, while minimizing the dependence on experts. We will adapt this system for use in Canadian hospitals and also integrate it with a unique smartphone-based EEG recording device to enable EEG diagnosis in communities without local specialized neurologists. Benefits will include improved timeliness and accuracy of epilepsy and sleep disorder diagnosis, accompanied by reduced costs. For more remote regions in Canada and elsewhere, our smartphone-integrated device will provide local access to accurate and cost-efficient EEG diagnosis avoiding the need to travel to larger centers.

脑电图（EEG，使用头皮上的电极测量人类大脑活动）是研究脑电活动的关键工具。 癫痫的诊断这也是诊断睡眠障碍的关键步骤。现在脑电图的解读 依赖于专门的神经科医生这限制了较小社区获得脑电图服务的机会。即使在大 社区，脑电图解释的费力性质和对专家的严重依赖给患者带来了巨大的成本。 医疗保健系统，并可能延误诊断和治疗。脑电图解释的完全自动化可能会 解决这些问题。然而，尝试这样做的成功有限，部分原因是EEG的某些方面 诸如视觉模式识别之类解释虽然对于受过训练的人类专家来说相对容易，但是非常难以 自动化。人类计算是计算机科学的一个领域，旨在将自动算法与明智的联合收割机结合起来 将人力投入应用到一个框架中，以利用每个框架的优势，同时避免任何一个框架的局限性。 人类计算已被证明是一个强大的工具，可以解决许多科学上的关键问题，这些问题已被证明是难以解决的。 标准的人工智能方法。我们建议建立一个系统结合最先进的机器学习 算法和人工计算方法，以实现对人类EEG的快速、可扩展、准确解释， 减少对专家的依赖。我们将调整这一系统在加拿大医院使用，并将其与一个 独特的基于智能手机的EEG记录设备，可在社区进行EEG诊断，无需当地专业人员 神经科医生受益将包括提高癫痫和睡眠障碍诊断的及时性和准确性， 通过降低成本。对于加拿大和其他地方的偏远地区，我们的智能手机集成设备将提供本地 获得准确且具有成本效益的EEG诊断，避免前往大型中心。