A de Jesus Kozakevicius, C. Ramos Rodrigues, R. Ceretta Nunes, and R. Guerra Filho (Brazil)
Wavelet Filtering, Sure Thresholding, QRS Detection
Biomedical signals like heart wave commonly change their statistical properties over time, tending to be nonstationary. For analysing this kind of signal wavelet transforms are a powerful tool. In this paper we utilize orthogonal wavelets to filter and analyse ECG signals. First, we use compactly supported wavelets associated to the statistical Stein's Unbiased Risk Estimator (SURE) in order to obtain an adaptive thresholding strategy to filter ECG signals. Second, we analyse the filtered signals by using the Haar wavelet transform in order to detect the positions of the occurrence of the QRS complex during the period of analysis. As results we obtain a more efficient filter, since the threshold value depends on the magnitude of wavelet coefficients in each level, and also a lightweight QRS detection algorithm.
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