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（ECG）记录的P-QRS-T时间波掩盖了对检测**心血管疾病至关重要的信息，并且已经为开发基于ECG的方法而付出了很大的努力，这些方法将基于ECG的方法与不规则的心跳区分开，并检测和分类心力缺血。该领域的研发**一直保持活跃数十年，因为检测和分类的准确性，速度和鲁棒性的任何改善**在增强心脏健康监测系统方面的能力是高度希望的。在基于计算机的心电图管理和报告**软件的软件工程中。该公司目前正在寻求增强和扩展其用于ECG分析的软件，并且特别有兴趣开发算法，以自动分析来自各种ECG设备的ECG录音，其质量和采样率的不同数据大小。 **公司已选择了一套来自统计数据的多元分析（称为主成分分析（PCA））的一套技术。该提案将解决**公司在该领域的发展和实践引起的主要问题，包括（i）使用** MIT-BIH心律失常数据库培训的PCA子空间的通用性； （ii）处理具有不同采样率的ECG记录的技术； （iii）**识别用于聚类QRS复合物的最佳方法； （iv）某些部分（以形状和大小）在PCA特征空间和已知的QRS形态之间存在固有连接（如果**）。 **预期的结果将显着增强CardioComm的解决方案组合，并为消费者提供自动化和**准确的ECG分析。