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

心脏是一个重要的器官，不停地跳动（即扩张和收缩）以维持血液循环，使我们活着。心脏有节奏的收缩和扩张通过血液将营养物质和氧气输送到身体的各个部位以维持生命。当心脏的这种节律性功能由于各种病理生理学原因而受到干扰时，心脏收缩导致心脏的正常功能受损。根据这些子宫收缩的起源，它可能导致致命的情况。最致命的心律失常是室颤（VF），其起源于心脏的下腔室（即心室）。如果在发病后几分钟内没有提供医疗护理，VF可能导致心脏性猝死（SCD）。北美每年报告约300，000例SCD（加拿大为45，000例），其中大部分与VF相关。起源于心房的心房颤动（AF）虽然不像VF那样致命，但会严重影响生活质量并增加中风的风险。 尽管经过几十年的研究努力，但在理解心脏颤动的机制基础方面仍然存在重大的知识差距，这阻碍了降低与心脏颤动（特别是VF）相关的死亡率的有效手段。这强烈地激发了开发新的工程方法的需要，以理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。解码致命VF背后的机制的主要瓶颈是SCD在几分钟内发生，并且在大多数情况下[特别是在院外心脏骤停（OHCA）中]，关于心脏电状态的唯一立即可用的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分析和提取心律失常期间多通道心电图和心电图信息的新方法，并建立计算机仿真模型以破译心脏颤动的机制见解。具体而言，该研究与多伦多综合医院和圣迈克尔医院合作，将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。这些解释源上的信息线索将被适当地转化为电描记图和多通道心电图信号形态。这些区别性信号形态沿着心律失常随时间的演变，然后将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制知识将显着增加长期集中（住院）的心律失常医疗策略，以及提高生存率在OHCA。

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.