ECG smoothing and denoising by local quadratic variation reduction

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. © 2011 ACM.