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

心脏是一个至关重要的器官，它不停地跳动（即扩张和收缩），以维持血液循环，维持我们的生命。心脏有节奏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。当心脏的这种节律功能由于各种病理生理原因而受到干扰时，心律失常的收缩会损害心脏的正常功能。根据这些不规律的收缩的来源，它可能导致致命的情况。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）。如果在发作后几分钟内没有医疗护理，室性心动过速可导致心源性猝死。北美每年约有30万例scd报告（加拿大45000例），其中大多数与VF有关。心房颤动（AF）起源于心房，虽然不像室性心动过速那样致命，但可严重影响生活质量并增加中风的风险。尽管经过了几十年的研究，但在了解心脏颤动的机制基础方面仍然存在重大的知识差距，这是预防有效手段来降低与心脏颤动相关的死亡率（特别是对于VF）。这强烈地激发了开发新的工程方法来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的需求。解码致死性室性心动过速背后机制的主要瓶颈是SCD发生在几分钟内，在大多数情况下（特别是在院外心脏骤停（OHCA）），唯一可立即获得的心脏电状态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，从心律失常期间的多通道心电图和心电图中分析和提取信息，并建立计算机模拟模型，以破译心脏颤动的机制见解。具体而言，该研究将与多伦多综合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。这些纤颤源的信息线索将适当地转化为心电图和多通道心电图信号形态。这些鉴别信号形态以及心律失常随时间的演变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著增强心律失常的长期（住院）医疗策略，并提高OHCA的生存率。

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.