Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.

Electrocardiogram contains a wealth of diagnostic information normally used to guide clinical decision making for proper diagnosis of cardiovascular diseases disorders. ECG is often contaminated by noises and artifacts that can be within the frequency band of interest and can manifest with similar morphologies as the ECG signal itself. Baseline correction and noise suppression are the two important pre-requisites for conditioning of the ECG signal. This paper presents a novel morphological filtering technique for removing baseline drift using non-flat structuring element. Further, to achieve noise suppression an improved median filtering technique is applied using mask of variable sizes. Depending upon the degree of impulse noise contamination the mask size may vary to a maximum of 1x11. The residual noise left after this stage is then filtered out using morphological filtering. Simulation results show noteworthy improvement in baseline correction and noise filtering in comparison to other proposed morphological filtering based approaches in the literature.

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Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.