File(s) under permanent embargo
Noise reduction in ECG signals using wavelet transform and dynamic thresholding
chapter
posted on 2017-01-01, 00:00 authored by Diptangshu Pandit, Li Zhang, Chengyu Liu, Nauman Aslam, Samiran Chattopadhyay, Chee Peng LimChee Peng LimBiomedical signals produced by mobile sensors usually carry various noises. This poses great challenges for the subsequent signal processing and disease analysis. Thus, noise removal becomes an important step of signal processing. This research proposes a noise reduction algorithm which can be applied to noisy ECG (electrocardiogram) signals to obtain a higher signal-to-noise ratio (SNR) for further processing. The proposed algorithm utilises wavelet transform and dynamic thresholding to reduce specific types of noise embedded in raw ECG signals. To prove the efficiency of the proposed algorithm, we employ a half-hour-long real ECG signal and add different types of noise for the evaluation of the proposed algorithm. We also compare the results obtained using different families of wavelets and different decomposition levels. The experimental results show that the proposed algorithm is able to produce a higher SNR in the output signal than that in the raw test signals.
History
Title of book
Emerging trends in neuro engineering and neural computationSeries
Series in BioEngineeringChapter number
10Pagination
193 - 206Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
2196-8861ISBN-13
978-981-10-3955-3Language
engPublication classification
B1 Book chapterExtent
15Editor/Contributor(s)
Asim Bhatti, Kendall Lee, Hamid Garmestani, Chee LimUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC