Journal
IET SIGNAL PROCESSING
Volume 12, Issue 9, Pages 1165-1171Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-spr.2018.5162
Keywords
Monte Carlo methods; hidden Markov models; electrocardiography; image denoising; wavelet transforms; discrete wavelet transforms; medical signal processing; signal denoising; electrocardiogram signal; clustering thresholding; soft thresholding; unwanted noise; biomedical data; electrocardiogram data; power line interference; electromagnetic interference; ECG signals; nonstationary physiological signals; effective tool; ECG data; noise reduction method; discrete wavelet
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Funding
- National Counsel and Technological Scientific Development (CNPq)
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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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