A Total Least Squares Approach for Data Reduction of Longterm ECG Recordings

A major problem associated with longterm ECG recordings is the enormous volume of data they contain and the requirement of an efficient procedure for its archival in reduced form is highly desirable. The storage of multiple recordings poses limitations, especially when they are to be used at a later time for applications involving high resolution mode, such as the contextual analysis of ECG. This paper presents an application of the singular value decomposition (SVD) within a total least squares (TLS) framework for data reduction of longterm ECG recordings. We present a combined formulation of denoising and data reduction via a TLS approach. Following beat delineation, archival of the ECG beat is accomplished using the reduced parameter set obtained using the TLS approximation in the discrete cosine transform (DCT) domain. Casting the transform domain ECG signal into a structured form using only the significant DCT coefficients resulted in a substantial reduction of the computational complexity involved in estimating the model parameters. With the reduced parameter set obtained using the proposed TLS approach, it was possible to archive multiple recordings of ambulatory Holter ECG data in a personal computer with only a limited storage capacity.