title: A spline framework for ECG analysis.
abstract: In this effort we introduce a spline framework for ECG waveform analysis, with initial application to the ECG delineation (segmentation) problem. The framework comprises knot initialization, spline interpolant, error metric, and knot location optimization to parametrically represent the waveform for analysis, classification, or compression. Choice of these constituents is driven by the application of the framework. For our initial application of ECG delineation, we use the framework to identify characteristic points corresponding to waveform onset and offset times, peak values, and junction points. These are represented mathematically as critical points and points of inflection, which serve as knot locations for linear or cubic Hermite interpolants in the framework. Preliminary tests on a limited but diverse set of morphologies from the European ST-T database indicate that the framework obtains knot locations corresponding to characteristic points, and the resultant interpolated waveform represents the original signal well with low mean squared error.
authors.affiliation: Biomedical Signal Processing Laboratory, Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, USA. farzin@ieee.org
authors.forename: Farzin G
authors.initials: FG
authors.lastname: Guilak
authors.forename: James
authors.initials: J
authors.lastname: McNames
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity