Signal-to-noise ratio decision trees for extracting continuous, reliable and real-time heart rate variability (HRV) measurements from an ambulatory ECG monitoring device

信噪比决策树，用于从动态心电图监测设备中提取连续、可靠和实时的心率变异性 (HRV) 测量值

• 批准号: 516212-2017
• 负责人: Mitsis, Georgios
• 金额: $ 1.82万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 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=639463
• 关键词: Signal; noise; ratio; decision; trees

The goal of this research project is to develop computational algorithms for processing ambulatoryElectrocardiogram (ECG) signals recorded in real time by a wearable device and accurately measuring thevariation in the time intervals between each heartbeat, referred to as heart rate variability (HRV). HRV is animportant vital signal used by physicians and other healthcare professionals to monitor and assess the risk ofseveral cardiac and non-cardiac disorders. The main technical challenges this research sets out to overcomeinvolve reliably extracting HRV measurements despite common disturbances present in ambulatory ECGmonitoring and efficiently designing the algorithms to operate in real-time to allow for continuousmeasurements of the subject's current HRV. The partner organization intends to add the developed algorithmsto their existing remote monitoring device, which is currently capable of recording ECG amongst otherphysiological and physical activity signals. By adding the capability of accurate HRV measurement to thewearable device and monitoring platform developed by the industry partner, a crucial vital sign will beincorporated, thus further facilitating the adaptation of remote monitoring systems by the Canadian HealthcareSystem. Individuals known to be at risk of specific medical conditions and hospital patients recovering fromoperations where vital sign monitoring is needed can benefit from remote monitoring in the comfort of theirhomes. This will in turn reduce the burden incurred to hospitals and healthcare professionals and greatly reducethe associated hospitalization costs.

该研究项目的目标是开发计算算法，用于处理可穿戴设备真实的时间记录的动态心电图（ECG）信号，并准确测量每次心跳之间的时间间隔变化，称为心率变异性（HRV）。HRV是一个重要的生命信号，被医生和其他医疗保健专业人员用来监测和评估几种心脏和非心脏疾病的风险。本研究要克服的主要技术挑战包括可靠地提取HRV测量值，尽管在动态ECG监测中存在常见干扰，并有效地设计实时操作的算法，以允许连续测量受试者的当前HRV。合作伙伴组织打算将开发的算法添加到其现有的远程监测设备中，该设备目前能够记录ECG以及其他生理和身体活动信号。通过将准确的心率变异性测量功能添加到行业合作伙伴开发的可穿戴设备和监测平台中，将纳入一个关键的生命体征，从而进一步促进加拿大医疗保健系统对远程监测系统的适应。已知处于特定医疗条件风险中的个人和需要生命体征监测的手术后恢复的医院患者可以在舒适的家中受益于远程监测。这将反过来减轻医院和医疗保健专业人员的负担，并大大降低相关的住院费用。