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

脑电(EEG，使用头皮上的电极测量人类大脑活动)是一种关键工具 癫痫的诊断。这也是诊断睡眠障碍的关键一步。目前，对脑电的解读是 依赖于专业的神经科医生。这限制了较小社区获得脑电服务的机会。即使是在大范围内 社区，脑电解释的繁重性质和对专家的严重依赖给 卫生保健系统，并可能延误诊断和治疗。脑电解释的完全自动化可能会 解决这些问题。然而，这样做的尝试收效甚微，部分原因是脑电的某些方面 像视觉模式识别这样的解释，虽然对训练有素的人类专家来说相对容易，但却很难 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算法相结合 将人工输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限制。 人类计算已被证明是解决科学中许多关键问题的强大工具，这些问题被证明是难以解决的 标准的人工智能方法。我们建议建立一个结合最先进的机器学习的系统 算法和人类计算方法，实现对人类脑电的快速、可扩展、准确的解释，同时 最大限度地减少对专家的依赖。我们将使该系统适用于加拿大的医院，并将其与 独特的基于智能手机的脑电记录设备，无需当地专业人员即可在社区进行脑电诊断 神经学家。好处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更偏远的地区，我们的智能手机集成设备将为您提供本地 获得准确且经济高效的脑电诊断，无需前往更大的中心。

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.