Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

Healthcare Technology Letters
Udit SatijaM Sabarimalai Manikandan

Abstract

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, compa...Continue Reading

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Citations

Jul 12, 2018·IEEE Reviews in Biomedical Engineering·Udit SatijaM Sabarimalai Manikandan
Jul 25, 2017·Australasian Physical & Engineering Sciences in Medicine·Shayan YazdaniMohammad Hossein Sedaaghi
Mar 20, 2020·Healthcare Technology Letters·Pramendra Kumar, Vijay Kumar Sharma

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Methods Mentioned

BETA
feature extraction

Software Mentioned

lpar
EMD
MITBIHA
eqno
MATLAB

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