A new robust wavelet based algorithm for baseline wandering cancellation in ECG signals

Wavelet transform has been emerged over recent years as a powerful time-frequency analysis and signal coding tool favored for the interrogation of complex non stationary signals. Its application to bio-signal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the Electrocardiogram (ECG). In this paper, the emerging roles of the wavelet transform in the ECG preprocessing and noise removing step is discussed in detail. One of the most important noise sources, baseline wandering, which can be affected ECG signal analysis is introduced and a new method based on wavelet transform is being proposed. The proposed method construct a model of baseline wander with multiresolution analysis of the signal using discrete wavelet transform and then remove the baseline wander from the ECG signal using the constructed model. Simulations were carried out to show the performance of the algorithm using the MIT-BIH noise stress test database and PTB diagnosis database. The quality of the results by the proposed technique is found to meet or exceed that of published results using other conventional methods such as kalman filtering and conventional digital filters.

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