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.
Semiperiodic signals possess an underlying periodicity, but their constituent spectral components include stochastic elements which make it impossible to analytically determine locations of the signal's critical points. Mathematically, a signal's critical points are those at which it is not differentiable or where its derivative is zero. In some domains they represent characteristic points, which are locations indicating important changes in the underlying process reflected by the signal.For many applications in healthcare, knowledge of precise locations of these points provides key insight for analytic, diagnostic, and therapeutic purposes. For example, given an appropriate signal they might indicate the start or end of a breath, numerous electrophysiological states of the heart during the cardiac cycle, or the point in a stride at which the heel impacts the ground. The inherent variability of these signals, the presence of noise, and often, very low signal amplitudes, makes accurate estimation of these points challenging.There has been much effort in automatically estimating characteristic point locations. Approaches include algorithms operating in the time domain, on various transformations of the data, and using different models of the signal. These methods apply a wide variety of techniques ranging from simple thresholds and search windows to sophisticated signal processing and pattern recognition algo-
We introduce an implementation of a novel spline framework for parametrically representing electrocardiogram (ECG) waveforms. This implementation enables a flexible means to study ECG structure in large databases. Our algorithm allows researchers to identify key points in the waveform and optimally locate them in long-term recordings with minimal manual effort, thereby permitting analysis of trends in the points themselves or in metrics derived from their locations. In the work described here we estimate the location of a number of commonly-used characteristic points of the ECG signal, defined as the onsets, peaks, and offsets of the P, QRS, T, and R' waves. The algorithm applies Bayesian optimization to a linear spline representation of the ECG waveform. The location of the knots-which are the endpoints of the piecewise linear segments used in the spline representation of the signal-serve as the estimate of the waveform's characteristic points. We obtained prior information of knot times, amplitudes, and curvature from a large manually-annotated training dataset and used the priors to optimize a Bayesian figure of merit based on estimated knot locations. In cases where morphologies vary or are subject to noise, the algorithm relies more heavily on the estimated priors for its estimate of knot locations. We compared optimized knot locations from our algorithm to two sets of manual annotations on a prospective test data set comprising 200 beats from 20 subjects not in the training set. Mean errors of characteristic point locations were less than four milliseconds, and standard deviations of errors compared favorably against reference values. This framework can easily be adapted to include additional points of interest in the ECG signal or for other biomedical detection problems on quasi-periodic signals.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.