Automatic real-time classification of remotely collected ECG data

远程采集心电数据自动实时分类

• 批准号: 501118-2016
• 负责人: Misic, Vojislav
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=608482
• 关键词: Automatic; real; time; classification; remotely

The objective of the proposed project is to develop or adapt an ECG feature extraction method to be used in an automated model to detect potential cardiac anomalies from remotely collected ECG data and flag them for immediate attention of a medical technician. The goal is not to replace the trained technicians, but to perform the initial triage that will allow them to focus on patients who need urgent medical attention. In this manner, the project will develop a high accuracy system to classify, in real time, ECG signals collected remotely by mobile devices. This will help reduce the time to attend to patients in need and, thus, improve their

拟议项目的目标是开发或调整一种心电图特征提取方法，用于自动模型，从远程收集的心电图数据中检测潜在的心脏异常，并将其标记，以便医疗技术人员立即注意。我们的目标不是取代训练有素的技术人员，而是进行初步的分类，使他们能够专注于需要紧急医疗照顾的病人。通过这种方式，该项目将开发一个高精度系统，对移动的设备远程收集的ECG信号进行真实的分类。这将有助于减少照顾有需要的病人的时间，从而改善他们的健康状况。