Advanced Signal Processing Methods for Analysis of Fibrillatory Waves

用于分析颤动波的先进信号处理方法

• 批准号: RGPIN-2018-05540
• 负责人: Hashemi, Javad
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653152
• 关键词: Advanced; Signal; Processing; Methods; Analysis

Signal processing methods have been used for decades to extract critical information from electrogram recordings of the human heart for diagnosis of numerous heart diseases including atrial fibrillation (AF). AF is the most common cardiac arrhythmia affecting more than 34 million people with an annual cost of $6 billion just in North America. Unfortunately, the current processing methods have had limited success in targeting the sources of AF due to the complex and dynamic nature of the disease. ******The conventional processing methods used for AF diagnosis are based on sequential data collection and often utilize the local information from individual recording channels to localize AF sources without considering temporal association between the channels to extract wave propagation characteristics. This can be attributed to the fact that the recordings obtained from these systems have low spatial resolution and, due to the sequential acquisition, a global snapshot of fibrillatory waves in atria is not feasible. Recent efforts focus on employing alternative recording equipment to create a panoramic view of the fibrillatory wave propagation in atria. However, the reported outcomes of these studies have been often contradictory. In addition, the high cost and difficulty in placement and maneuvering of these recording devices into small chambers of the heart have prohibited the widespread use of these new diagnostic systems. ******The aim of my research program is to develop new signal processing methods to study fibrillatory wave propagation in the human heart using data obtained from the conventional recording systems. This will be achieved by developing methods for accurate estimation of local activation intervals and by analyzing the temporal association of local active intervals among simultaneously recorded signals to obtain regional information, in contrast to local information. Although the regional information cannot provide a global wave propagation map in atria, it has an unexplored potential to reveal footprints of the trajectory of the fibrillatory waves and the underlying physiologic properties of the regions within the atria. Our processing methods will be developed and validated by generating realistic simulated data from a detailed 3D model. The applicability of our methods on a clinical database will also be studied. To achieve the goals of this research program, a number of projects will be conducted by five graduate students under my supervision.******Successful results from this research program will provide a unique approach to investigate and better understand the propagation of fibrillatory waves using simulated/real sequential data and will facilitate a subsequent study to validate the findings on human data. This can lead to shorter clinical procedures, reduce the financial burden on the Canadian health care system and help the millions of people whose lives are affected by AF.

几十年来，信号处理方法一直用于从人类心脏的电描记图记录中提取关键信息，用于诊断包括心房颤动（AF）在内的许多心脏疾病。AF是最常见的心律失常，影响超过3400万人，仅在北美每年的费用就达60亿美元。不幸的是，由于疾病的复杂性和动态性，目前的处理方法在靶向AF来源方面的成功有限。 ** 用于AF诊断的传统处理方法基于顺序数据收集，并且通常利用来自各个记录通道的局部信息来定位AF源，而不考虑通道之间的时间关联来提取波传播特性。这可以归因于从这些系统获得的记录具有低空间分辨率的事实，并且由于顺序采集，心房中的反射波的全局快照是不可行的。最近的努力集中在采用替代的记录设备，以创建一个全景的解释波在心房中的传播。然而，这些研究报告的结果往往相互矛盾。此外，将这些记录装置放置和操纵到心脏的小腔室中的高成本和困难已经阻止了这些新诊断系统的广泛使用。** 我的研究计划的目的是开发新的信号处理方法，使用从传统记录系统获得的数据来研究人类心脏中的电波传播。这将通过开发用于精确估计局部激活间隔的方法以及通过分析同时记录的信号之间的局部激活间隔的时间关联来实现，以获得与局部信息相反的区域信息。尽管区域信息无法提供心房中的全局波传播地图，但它具有未开发的潜力来揭示心房内颤动波轨迹的足迹和心房内区域的潜在生理特性。我们的处理方法将通过从详细的3D模型生成逼真的模拟数据来开发和验证。我们的方法对临床数据库的适用性也将进行研究。为了实现这个研究计划的目标，一些项目将由五名研究生在我的监督下进行。这项研究计划的成功结果将提供一种独特的方法来研究和更好地了解使用模拟/真实的序列数据的反射波的传播，并将促进后续的研究，以验证对人类数据的研究结果。这可以缩短临床程序，减轻加拿大医疗保健系统的经济负担，并帮助数百万生活受到AF影响的人。