High bandwidth EEG data hosting and analytics in cloud-based storage systems

基于云的存储系统中的高带宽脑电图数据托管和分析

• 批准号: 488865-2015
• 负责人: Amza, Cristiana
• 金额: $ 3.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618875
• 关键词: bandwidth; EEG; data; hosting; analytics

In latest years, with the advent of Big Data, especially in the area of biometrics data collected from wearablemonitoring devices, there is a high need for Cloud-based solutions (include large scale distributed storagesystems) for storage and high bandwidth on-the-fly data analysis. Examples of such biometric data are theElectroencephalography (EEG) data that become available from EEG wearable devices and offer newopportunities for contextualized research applications by providing access to dense array, high quality, rawEEG data. Thus, the emergence of high frequency EEG sampling in a scalp based device (a "Holter monitor"for the brain) will assist physicians in diagnosing, assessing and treating a myriad of neurological conditionsand help enable new discovery. In particular, our industry partner, Avertus, has developed a headset for highfrequency sampling that is both comfortable to wear in non-hospital settings and also highly accurate, with along term vision to predict problems and deliver treatments in real-time. However, a key problem for ITcompanies that collect large amounts of biometrics data on-the-fly is their need for real-time solutions foranomaly detection in the collected data. In our case of the EEG headset, high frequency sampling generateslarge amounts of raw data (e.g., an estimate of 5GB/patient/day) that requires significant processing,necessitating a distributed processing platform for both research (discovery and repository) and clinical utility -initially on a batch basis, but also including streaming analytic capability. In this work we propose to develop acloud based data analytics and storage platform for hosting and processing streaming data from Avertusheadsets. The focus is on two aspects: (a) on-the-fly biometric data analysis and anomaly detection ofstreaming data from the Avertus users, and (b) on-the-fly analysis of computer system data for anomalydetection with regards to the system behaviour. We expect that the proposed solution will minimize the dataanalysis search space of the stored data. We will evaluate our solutions in collaboration with the partnerorganization for types of human and system biometrics data on various distributed storage systems.

近年来，随着大数据的出现，特别是在从可穿戴监测设备收集的生物特征数据领域，对基于云的解决方案（包括大规模分布式存储系统）的存储和高带宽实时数据分析的需求很高。这种生物特征数据的示例是脑电图（EEG）数据，其可从EEG可穿戴设备获得，并且通过提供对密集阵列、高质量、原始EEG数据的访问，为情境化研究应用提供新的机会。因此，在基于头皮的设备（用于大脑的“霍尔特监视器“）中出现的高频EEG采样将帮助医生诊断、评估和治疗无数的神经病症，并帮助实现新的发现。特别是，我们的行业合作伙伴Avertus开发了一款用于高频采样的耳机，该耳机在非医院环境中佩戴舒适，并且高度准确，具有沿着长期视觉，可预测问题并实时提供治疗。然而，对于那些在飞行中收集大量生物特征数据的IT公司来说，一个关键问题是他们需要在收集的数据中进行实时异常检测的解决方案。在我们的EEG耳机的情况下，高频采样产生大量的原始数据（例如，估计为5GB/患者/天），这需要大量的处理，需要一个分布式处理平台用于研究（发现和存储库）和临床实用性-最初是在批处理的基础上，但也包括流分析能力。在这项工作中，我们建议开发基于云的数据分析和存储平台，用于托管和处理来自Avertus耳机的流数据。重点是两个方面：（a）对来自Avertus用户的流式数据进行动态生物特征数据分析和异常检测，以及（B）对计算机系统数据进行动态分析，以检测系统行为的异常。我们期望所提出的解决方案将最小化存储数据的数据分析搜索空间。我们将与合作伙伴组织合作，针对各种分布式存储系统上的人类和系统生物识别数据类型评估我们的解决方案。