Key result
A piecewise Gaussian derivative model for ECG delineation achieved the smallest mean error for 3 out of 9 fiducial points compared to state-of-the-art algorithms.
Why the study?
Piecewise Gaussian derivative models may fit electrocardiography wave morphology well for QRS, P-, and T-wave delineation, but required thorough investigation.
Answers are grounded in this paper and cite their sources.
A piecewise Gaussian derivative model provides competitive accuracy for automatic ECG delineation compared to existing state-of-the-art algorithms.
Automatic methods for the detection of characteristic points in electrocardiography signals support cardiologists in assessing the state of a patient's cardiovascular system. In this work, we apply a general method for parameter estimation to the specific problem of QRS complex, P-, and T-wave delineation, i.e. the computation of their on- and offset points in time. As input the method expects a piecewise Gaussian derivative model that is potentially a good fit for the morphology of electrocardiography waves, but a thorough investigation is needed. The model parameters are estimated by substituting zero-crossings of the input signals' scale-space representation into scale-dependent algebraic expressions and are further refined by fitting the model to the electrocardiography signal in a least-squares sense. Validating the results on the QT database and comparing to state-of-the-art algorithms shows smallest mean error for 3 out of 9 fiducial points and for the others only small differences to the respective best competitors.
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Spicher et al. (2019) studied Electrocardiography signal analysis. Piecewise Gaussian derivative model for ECG delineation vs. State-of-the-art algorithms was evaluated on Mean error for fiducial points. A piecewise Gaussian derivative model for ECG delineation achieved the smallest mean error for 3 out of 9 fiducial points compared to state-of-the-art algorithms.
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