ECG signal denoising and baseline wander correction based on the empirical mode decomposition

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

“…The solution would be to filter the maximum quantity of noise while keeping as much of the effective signal frequency spectrum as possible. Wavelet denoising algorithms have been received extensive consideration in the processing of white Gaussian noise in biological signals, especially for the Electrocardiogram [12]- [14]. Most wavelet based denoising literatures suggest the use of the Donoho's method [15], [16], that makes an estimation of the thresholds by maximizing a risk function in terms of quadratic loss at the sample points.…”

Biomedical Time Series Processing and Analysis Methods: The Case of Empirical Mode Decomposition

“…Literature references' variety reveals the extensive range of EMD applications in several areas of the biomedical engineering field. Particularly there are publications concerning the application of EMD in the study of Heart Rate Variability (HRV) [8], analysis of respiratory mechanomyographic signals [9], ECG enhancement artifact and baseline wander correction [10], R-peak detection [11], Crackle sound analysis in lung sounds [12] and enhancement of cardiotocograph signals [13]. The method is employed for filtering electromyographic (EMG) signals in order to perform attenuation of the incorporated background activity [14].…”

Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

“…Effective TWA detections require increased heart rate which can be obtained by application of the exercise stress test. However, the signal-to-noise ratio (SNR) of ECG signals recorded during the stress test, is decreasing considerably mainly because of patient and cable movements, baseline drifts due to respiration and power line interferences (50 Hz) [16], [17]. Under this condition, the segmentation of the T-waves, as well as the QRS complex, becomes more difficult and in some cases impossible.…”