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.

心脏是一个至关重要的器官，不停地跳动（即扩大合同）以保持血液循环以保持我们的生命。节奏收缩和心脏的扩张将营养和氧气通过血液传递到身体的各个部位，以维持生命。当由于各种病理生理原因而感到心脏的这种节奏功能会受到干扰时，心律不齐的收缩会损害心脏的正常功能。根据这些心律不齐收缩的起源，它可能导致致命状况。心律不齐的最致命是心室纤颤（VF），起源于心脏的下腔（即心室）。如果在发作几分钟内未提供医疗护理，VF可能会导致心脏死亡（SCD）。每年在北美（加拿大45,000个）报告大约30万SCD，其中大多数与VF有关。源自心房的房颤（AF）虽然不像VF那样致命，但可以严重影响生活质量并增加中风的风险。 尽管研究了数十年的研究，但了解心脏纤颤的机理基础仍然存在显着的知识差距，这阻止了有效的手段降低与心脏纤维化相关的死亡率（尤其是对于VF）。这强烈促使需要开发新的工程方法，以理解这些心律不齐背后的机制，并将其转化为可实现的实用解决方案，以降低与心律不齐相关的死亡率。解码致命VF背后的机制的主要瓶颈是SCD发生在几分钟之内，并且在大多数情况下（尤其是在院外心脏骤停（OHCA）中），唯一关于心脏电气状态的立即可用信息是通过表面心电图。 在解决上述知识差距时，拟议的研究计划将开发新的方法来分析和提取心律不齐期间多通道电图和心电图中的信息，并构建计算机模拟模型，以破译心脏纤维化的机械见解。具体而言，该研究与多伦多将军和圣迈克尔医院合作，将开发高级数据处理和建模技术，以表征并在区域定位启动和维持心律不齐的来源。这些纤维源的信息线索将适当地转换为电图和多通道心电图信号形态。这些歧视性信号形态以及心律失常随时间的演变将用于发展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心律不齐的长期关注（医疗）医疗策略，并提高OHCA的生存率。