Denoising and Baseline Correction of ECG Signals using Sparse Representation

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	Denoising and Baseline Correction of ECG Signals using Sparse Representation; Denoising and Baseline Correction of ECG Signals using Sparse Representation
	Zhou, Yichao 1; Hu, Xiyuan2 ; Tang, Zhenmin 1; Ahn, Andrew C.3
	2015-10 ; 2015-10
会议名称	IEEE workshop on Signal Processing System ; IEEE workshop on Signal Processing System
会议日期	2015-10 ; 2015-10
会议地点	Hangzhou, China ; Hangzhou, China
摘要	Removing 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.; Removing 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.
关键词	Sparse Representation Sparse Representation Adaptive Signal Separation Adaptive Signal Separation Ecg Denoising Ecg Denoising Baseline Wandering Correction Baseline Wandering Correction
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19684
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
通讯作者	Ahn, Andrew C.
作者单位	1.School of Computer Science, Nanjing University of Science and Technology 2.Institute of Automation, Chinese Academy of Sciences 3.BIDMC, MGH, Harvard Medical School
推荐引用方式 GB/T 7714	Zhou, Yichao,Hu, Xiyuan,Tang, Zhenmin,et al. Denoising and Baseline Correction of ECG Signals using Sparse Representation, Denoising and Baseline Correction of ECG Signals using Sparse Representation[C],2015, 2015.

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