Key result
A signal representation model using energy thresholding and Gaussian kernels enabled significant noise reduction, compression, and turning point location for biomedical signals such as ECG.
A novel computational framework using Gaussian kernels and energy thresholding improves noise reduction and segmentation for biomedical signals like ECGs.
May enhance ECG QT-analysis robustness; leaves open prospective clinical validation.
A general technique for representing quasi-periodic oscillations, typical of biomedical signals, is described. Using energy thresholding and Gaussian kernels, in conjunction with a nonlinear gradient descent optimization, it is shown that significant noise reduction, compression and turning point location is possible. As such, the signal representation model can be considered a form of correlated source separation. Applications to filtering, modelling and robust ECG QT-analysis are described.
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Gari D. Clifford (2006) studied Biomedical signals (ECG). Signal representation model using energy thresholding and Gaussian kernels was evaluated on Noise reduction, compression and turning point location. A signal representation model using energy thresholding and Gaussian kernels enabled significant noise reduction, compression, and turning point location for biomedical signals such as ECG.
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