Open AccessReview of noise removal techniques in ECG signals
Author(s) -
Chatterjee Shubhojeet,
Thakur Rini Smita,
Yadav Ram Narayan,
Gupta Lalita,
Raghuvanshi Deepak Kumar
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2020.0104
Subject(s) - noise reduction , computer science , pattern recognition (psychology) , noise (video) , artificial intelligence , mean squared error , signal processing , speech recognition , signal (programming language) , additive white gaussian noise , wavelet , white noise , mathematics , telecommunications , statistics , radar , image (mathematics) , programming language
An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre‐processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state‐of‐the‐art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root‐mean‐square error, percentage‐root‐mean‐square difference, and signal‐to‐noise ratio improvement, thus comparing various ECG denoising techniques on MIT‐BIH databases, PTB, QT, and other databases. It is observed that Wavelet‐VBE, EMD‐MAF, GAN2, GSSSA, new MP‐EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP‐EKF, DLSR, and AKF perform comparatively well. For base‐line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power‐line interference removal, DLSR and EWT perform well. Finally, FCN‐based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.