Principal component analysis based algorithms for ECG recordings

基于主成分分析的心电图记录算法

• 批准号: 524089-2018
• 负责人: Lu, WuSheng
• 金额: $ 1.82万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 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=658032
• 关键词: Principal; component; analysis; based; algorithms

The P-QRS-T time waves recorded in electrocardiogram (ECG) conceal information vital for detecting the**cardiovascular disease, and much effort has been made to develop ECG-based methods that distinguish regular**from irregular heartbeats, and detect and classify heart arrhythmia. Research and development in this field have**stayed active for decades as any improvement in accuracy, speed, and robustness in detection and classification**capabilities is highly desirable for enhancing cardiac health monitoring systems.**CardioComm Solutions Inc. has been in medical diagnostic industry as an FDA cleared, ISO certified, and**Health Canada/CE approved company for development, sales, and marketing of medical software and devices.**The company's specialization is in the software engineering of computer based ECG management and reporting**software. The company is currently looking to enhance and extend its software for ECG analysis, and is**especially interested in developing algorithms for automatic analysis of ECG recordings coming from a variety**of ECG devices with different data sizes of varying quality and sampling rates. A suite of techniques originated**from multivariate analysis in statistics, known as principal component analysis (PCA), has been selected by the**company as a foundational tool for ECG analysis. This proposal will solve the major issues arising from the**company's development and practice in this area include (i) universality of the PCA subspaces trained using**MIT-BIH arrhythmia database; (ii) techniques to handle ECG recordings with different sampling rates; (iii)**identification of optimal methods for clustering QRS complexes; and (iv) existence of intrinsic connections, if**any, between certain parts (in terms shape and size) of PCA feature space and known QRS morphologies. The**expected outcome will significantly enhance CardioComm's solution portfolios and provide automated and**accurate ECG analysis to consumers.

心电图记录的P-QRS-T时间波隐藏了对检测心血管疾病至关重要的信息，人们已经努力开发基于心电图的方法来区分正常和不规则的心跳，并检测和分类心律失常。这一领域的研究和发展已经活跃了几十年，因为在检测和分类能力的准确性、速度和稳健性方面的任何改进都是增强心脏健康监测系统所迫切需要的。**CardioComm Solutions Inc.作为FDA批准，ISO认证和**加拿大卫生部/CE批准的医疗软件和设备开发，销售和营销公司，一直在医疗诊断行业。**公司专业从事基于计算机的心电管理和报告软件工程**软件。该公司目前正在寻求增强和扩展其ECG分析软件，并对开发用于自动分析来自各种ECG设备的ECG记录的算法特别感兴趣，这些设备具有不同的数据大小，不同的质量和采样率。一套源自统计学多变量分析的技术，被称为主成分分析（PCA），已被**公司选择作为心电图分析的基础工具。该提案将解决**公司在该领域的发展和实践中出现的主要问题，包括：(i)使用**MIT-BIH心律失常数据库训练的PCA子空间的通用性；（ii）处理不同采样率心电记录的技术；**确定QRS复合物聚类的最佳方法；（iv） PCA特征空间的某些部分（就形状和大小而言）与已知QRS形态之间存在内在联系（如果有的话）。预期结果将显著增强CardioComm的解决方案组合，并为消费者提供自动化和准确的心电图分析。