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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636975
• 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.

心脏是一个引人入胜且精心设计的重要器官，具有节奏（即扩展和收缩）的电力机械功能，以节奏地在生物的整个生命中维持血液循环。当这种节奏受到干扰或出于多种病理生理原因而进入心律失常和膨胀的心脏时，可能会导致生命威胁医疗状况。这些有节奏的疾病会导致心律不齐，这会严重影响心输出量（或血液流动）。心室纤颤（VF）是一种心律不齐，起源于心脏的下腔，如果在发作几分钟内未提供医疗护理，可能会导致心脏死亡。每年在北美（加拿大45,000人）报告的大约300,000次心脏死亡（SCD）中，大多数与VF有关。相比之下，心房颤动（AF）起源于心脏的上腔，尽管不像VF那样致命，但可以严重影响生活质量并增加患者中风的风险。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不齐相关的死亡率。 VF和AF背后的机制是由于该过程的非平稳性质以及研究人类心律不齐的伦理/实际局限性。多年来，信号处理方法有助于医学界从电子学（来自心脏表面的选举信号）和心电图（来自身体表面的心脏电信号），优化的治疗选择以及开发智力医疗设备的信息。拟议的研究计划将旨在确定量化这些心律不齐的新方法，以便以短期和长期治疗选择。具体而言，这项研究将与多伦多综合医院和圣迈克尔医院合作，将开发高级的电子和心电图信号和图像处理技术，以提高长期集中的医疗疗法在识别和消除负责这些心律失常的来源方面的效率，并提高植入式设备的智能，并提高紧急医疗服务人员的努力，以提高努力的努力。