Electrocardiogram signal denoising by clustering and soft thresholding

Electrocardiogram signal denoising by clustering and soft thresholding

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Separating signal from unwanted noise is a major problem when analysing biomedical data, such as electrocardiography. Electrocardiogram (ECG) data are typically a mixture of real signal and various sources of noise, including baseline wander, power line interference, and electromagnetic interference. Since ECG signals are non-stationary physiological signals, the wavelet transform has been proposed to be an effective tool for eliminating unwanted noise from the ECG data. Here, the authors proposed a new noise reduction method for ECG data based on the discrete wavelet transform and hidden Markov model. They performed Monte Carlo simulations to compare the performance of this new method with seven other well-known denoising techniques.

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