Adaptive Data Processing, Modeling, and Quantification Methods for Analyzing Cardiac Fibrillation

用于分析心颤的自适应数据处理、建模和量化方法

• 批准号: RGPIN-2020-04933
• 负责人: Umapathy, Karthikeyan
• 金额: $ 2.04万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752560
• 关键词: Adaptive; Data; Processing; Modeling; Quantification

Heart is a vital organ that beats (i.e. expands and contracts) nonstop to maintain blood circulation to keep us alive. The rhythmic contractions and expansion of the heart transports nutrients and oxygen via blood to all parts of the body to sustain life. When this rhythmic functioning of the heart gets disturbed because of various pathophysiological reasons, arrhythmic contractions result in compromising the normal functioning of the heart. Depending on the origin of these arrhythmic contractions, it may lead to lethal conditions. The most lethal of the arrhythmias is Ventricular Fibrillation (VF) which originates from the lower chambers of the heart (i.e. ventricles). VF can lead to sudden cardiac death (SCD) if no medical attention is provided within minutes of onset. About 300,000 SCDs are reported every year in North America (45,000 in Canada) most of which are VF related. Atrial fibrillation (AF) originating from atria, although not as lethal as VF, can seriously impact quality of life and increases the risk of stroke. Despite research efforts over many decades, there is still a significant knowledge gap in understanding the mechanistic basis of cardiac fibrillation which is preventing effective means to reduce the mortality rates associated with cardiac fibrillation (especially for VF). This strongly motivates the need for developing new engineering methods in understanding mechanisms behind these arrhythmias and translating them to realizable practical solutions to reduce the mortality associated with the arrhythmias. Major bottle necks in decoding the mechanisms behind lethal VF is that SCD occurs within minutes and that in most cases [especially in out-of-the-hospital cardiac arrests (OHCA)] the only immediately available information on the electrical state of the heart is through surface electrocardiograms. In addressing the above knowledge gap, the proposed research program will develop new ways of analyzing and extracting information from multi-channel electrograms and electrocardiograms during arrhythmia and build computer simulation models to decipher the mechanistic insights of cardiac fibrillation. Specifically, the research, in collaboration with Toronto General and St. Michael's Hospitals, will develop advanced data processing and modeling techniques to characterize and regionally locate the sources that initiate and sustain cardiac arrhythmias. The informative clues on these fibrillatory sources will be appropriately translated into electrograms and multi-channel electrocardiogram signal morphologies. These discriminative signal morphologies along with the evolution of the arrhythmia over time will then be used to develop intelligent ablation and defibrillation strategies. The mechanistic knowledge gained through the proposed research program and the developed analysis strategies will significantly augment long-term focused (in-hospital) medical strategies for arrhythmias as well as improve survival rates in OHCA.

心脏是一个重要的器官，它不停地跳动（即扩张和收缩）以维持血液循环以维持我们的生命。心脏有节律的收缩和扩张，通过血液将营养和氧气输送到身体的各个部位以维持生命。当心脏的节律功能由于各种病理生理原因而受到干扰时，心律失常收缩会导致心脏的正常功能受到损害。根据这些心律失常收缩的起源，它可能会导致致命的情况。最致命的心律失常是心室颤动 (VF)，它起源于心脏的下腔（即心室）。如果心室颤动在发作后几分钟内没有提供医疗护理，可能会导致心源性猝死 (SCD)。北美每年报告约 300,000 例 SCD（加拿大 45,000 例），其中大部分与室颤相关。源自心房的心房颤动 (AF) 虽然不像心室颤动那样致命，但会严重影响生活质量并增加中风风险。 尽管进行了数十年的研究努力，但在了解心脏颤动的机制基础方面仍然存在重大知识差距，这阻碍了降低心脏颤动（尤其是心室颤动）相关死亡率的有效手段。这强烈要求开发新的工程方法来了解这些心律失常背后的机制，并将其转化为可实现的实际解决方案，以降低与心律失常相关的死亡率。解码致死性心室颤动背后机制的主要瓶颈是 SCD 在几分钟内发生，并且在大多数情况下 [特别是院外心脏骤停 (OHCA)]，关于心脏电状态的唯一立即可用信息是通过表面心电图。 为了解决上述知识差距，拟议的研究计划将开发新的方法来分析和提取心律失常期间多通道电图和心电图的信息，并建立计算机模拟模型来破译心脏颤动的机制见解。具体来说，该研究将与多伦多总医院和圣迈克尔医院合作，开发先进的数据处理和建模技术，以表征和区域定位引发和维持心律失常的来源。有关这些颤动源的信息线索将被适当地转化为电图和多通道心电图信号形态。这些判别信号形态以及心律失常随时间的演变将被用于开发智能消融和除颤策略。通过拟议的研究计划和开发的分析策略获得的机械知识将显着增强针对心律失常的长期（院内）医疗策略，并提高 OHCA 的生存率。