Matrix analysis
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Wavelet domain Wiener filtering for ECG denoising using improved signal estimate
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Application of independent component analysis in removing artefacts from the electrocardiogram
Neural Computing and Applications
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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The ECG is the standard noninvasive test used to measure the electrical activity of the heart. Unfortunately, ECG signal is corrupted by several kinds of noise and artifacts that may negatively affect any subsequent analysis. In this work, we present a fast and effective algorithm for smoothing and denoising ECG records. The algorithm is the closed-form solution to a constrained convex optimization problem, where smoothing and denoising are achieved by locally reducing the quadratic variation of different portions of the ECG. Such a reduction is inversely related to the local SNR. The computational complexity of the algorithm is linear in the size of the vector under analysis, thus making it suitable for real-time applications. Simulation results confirm the effectiveness of the approach and highlight a notable ability to smooth and denoise ECG signals.