Adaptive Signal Modeling and Feature Extraction Methods for Analyzing Cardiac Fibrillation

用于分析心颤的自适应信号建模和特征提取方法

• 批准号: RGPIN-2015-06644
• 负责人: Umapathy, Karthikeyan
• 金额: $ 1.6万
• 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: 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=667373
• 关键词: Adaptive; Signal; Modeling; Feature; Extraction

The heart is a fascinating and complexly designed vital organ with electro mechanical functionalities that beats (i.e. expands and contracts) rhythmically to maintain blood circulation throughout the lifetime of a living being. When this rhythm gets disturbed or the heart goes into arrhythmic contractions and expansions for a multitude of pathophysiological reasons, it may result in life threatening medical conditions. These rhythmic disorders can result in cardiac arrhythmias, which can seriously affect cardiac output (or blood flow). Ventricular fibrillation (VF) is an arrhythmia that originates from the lower chambers of the heart and can lead to sudden cardiac death if medical attention is not provided within minutes of onset. Most of the approximately 300,000 sudden cardiac deaths (SCDs) reported every year in North America (45,000 of them in Canada) is related to VF. Atrial Fibrillation (AF), in comparison, originates from the upper chambers of the heart, and although not as lethal as VF, can seriously affect quality of life and increase the risk of stroke in patients. There is a great need to develop new engineering methodologies to improve understanding and assist in reducing the mortality rates associated with these cardiac arrhythmias. The mechanisms behind VF and AF are elusive due to the nonstationary nature of the processes and the ethical/practical limitations in studying human arrhythmias. Over the years, signal processing approaches have aided the medical community in extracting information from electrograms (electrical signals from the heart's surface) and electrocardiograms (cardiac electrical signals from the body surface), optimizing treatment options, and developing intelligent medical devices. The proposed research program will aim to identify novel ways to quantify these cardiac arrhythmias, so as to arrive at short-term and long-term treatment options. Specifically, the research, in collaboration with Toronto General Hospital and St. Michael's Hospital, will develop advanced electrogram and electrocardiogram signal and image processing techniques to improve the efficiency of long-term focused medical therapies in identifying and eliminating the sources responsible for these arrhythmias, increase the intelligence of implantable devices, and provide vital information to the emergency medical services personnel to improve survival rates in cardiac resuscitation efforts.

心脏是一个迷人的和复杂设计的重要器官，具有机电功能，有节奏地跳动（即扩张和收缩），以维持生命体一生的血液循环。当这种节律受到干扰或心脏由于多种病理生理原因进入心脏收缩和扩张时，可能会导致危及生命的医疗状况。这些节律紊乱会导致心律失常，严重影响心输出量（或血流）。心室颤动（VF）是一种起源于心脏下腔的心律失常，如果在发病后几分钟内没有提供医疗护理，可能导致心脏性猝死。在北美每年报告的约300，000例心脏性猝死（SCD）中，大多数（其中45，000例在加拿大）与VF有关。 相比之下，房颤（AF）起源于心脏的上腔，虽然不像VF那样致命，但会严重影响患者的生活质量并增加中风的风险。非常需要开发新的工程方法，以提高理解并帮助降低与这些心律失常相关的死亡率。VF和AF背后的机制是难以捉摸的，由于过程的非平稳性和研究人类心律失常的伦理/实践限制。多年来，信号处理方法帮助医学界从心电图（来自心脏表面的电信号）和心电图（来自体表的心脏电信号）中提取信息，优化治疗方案，并开发智能医疗设备。拟议的研究计划将旨在确定量化这些心律失常的新方法，以便获得短期和长期治疗方案。具体而言，该研究与多伦多总医院和圣迈克尔医院合作，将开发先进的电描记图和心电图信号和图像处理技术，以提高长期集中医疗治疗的效率，以识别和消除导致这些心律失常的来源，增加植入式设备的智能，并向紧急医疗服务人员提供重要信息，以提高心脏复苏努力中的存活率。