Principal component analysis based algorithms for ECG recordings

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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.