CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Denoising and Baseline Correction of ECG Signals using Sparse Representation
Zhou, Yichao1; Hu, Xiyuan2; Tang, Zhenmin1; Ahn, Andrew C.3
Conference NameIEEE workshop on Signal Processing System
Conference Date2015-10
Conference PlaceHangzhou, China
AbstractRemoving noise and other artifacts in the electrocardiogram (ECG) is a critical preprocessing step for further heart disease analysis and diagnosis. In this paper, we propose a sparse representation based ECG signal denoising and baseline wandering (BW) correction algorithm. Unlike the traditional filtering-based methods, like Fourier orWavelet transform, which use fixed basis, the proposed algorithm models the ECG signal as superposition of few inner structures plus additive random noise, while those structures can be learned from the input signal or a training set. Using those learned inner structures and their properties, we can accurately approximate the original ECG signal and remove noise and other artifacts like baseline wandering. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it to several state-of-the-art algorithms through both simulated and real-life ECG recordings.
KeywordSparse Representation Adaptive Signal Separation Ecg Denoising Baseline Wandering Correction
Document Type会议论文
Corresponding AuthorAhn, Andrew C.
Affiliation1.School of Computer Science, Nanjing University of Science and Technology
2.Institute of Automation, Chinese Academy of Sciences
3.BIDMC, MGH, Harvard Medical School
Recommended Citation
GB/T 7714
Zhou, Yichao,Hu, Xiyuan,Tang, Zhenmin,et al. Denoising and Baseline Correction of ECG Signals using Sparse Representation[C],2015.
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