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Baseline Drift Removal of ECG Signal: Comparative Analysis of Filtering Techniques
Abstract
The filtering techniques are primarily used for preprocessing of the signal and have been implemented in a wide variety of systems for Electrocardiogram (ECG) analysis. It should be remembered that filtering of the ECG is contextual and should be performed only when the desired information remains undistorted. Removal of baseline drift is required in order to minimize changes in beat morphology that do not have cardiac origin, which is especially important when subtle changes in the ‘‘low-frequency'' ST segment are analyzed for the diagnosis of ischemia. Here, for baseline drift removal different filters such as Median, Low Pass Butter Worth, Finite Impulse Response (FIR), Weighted Moving Average and Stationary Wavelet Transform (SWT) are implemented. The fundamental properties of signal before and after baseline drift removal are statistically analyzed.
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