Principal component analysis based algorithms for ECG recordings

心电图（ECG）中记录的P-QRS-T时间波隐藏了对检测心血管疾病至关重要的信息，并且已经做出了许多努力来开发基于ECG的方法，以区分规则心跳和不规则心跳，以及检测和分类心律失常。该领域的研究和开发已经活跃了几十年，因为检测和分类能力的准确性，速度和鲁棒性 ** 的任何改进对于增强心脏健康监测系统都是非常可取的。CrimComm Solutions Inc.在医疗诊断行业，作为FDA批准、ISO认证和 ** 加拿大卫生部/CE批准的公司，从事医疗软件和设备的开发、销售和营销。**该公司的专业是基于计算机的心电图管理和报告 ** 软件的软件工程。该公司目前正在寻求增强和扩展其心电图分析软件，并且 ** 特别感兴趣的是开发用于自动分析来自各种 ** 心电图设备的心电图记录的算法，这些心电图设备具有不同的数据大小和不同的质量和采样率。一套源自 ** 统计学中多元分析的技术，称为主成分分析（PCA），已被 ** 公司选为心电图分析的基础工具。该提案将解决 ** 公司在该领域的开发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）处理具有不同采样率的ECG记录的技术;（iii）** 识别用于聚类QRS波群的最佳方法;以及（iv）PCA特征空间的某些部分（在形状和大小方面）与已知QRS形态之间的内在连接的存在，如果存在的话。** 预期的结果将大大增强CIMComm的解决方案组合，并为消费者提供自动化和 ** 准确的ECG分析。

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.