ECG Baseline Estimation and Denoising With Group Sparse Regularization
Baseline wander (BW) and electrocardiogram (ECG) noise reduction play an important role in ECG data analysis and disease diagnosis. This article introduces a sparse optimization method, which takes into account the group sparse characteristics of the signal, and combines low-pass filter to denoise t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9344703/ |
| _version_ | 1828176485047861248 |
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| author | Hao Shi Ruixia Liu Changfang Chen Minglei Shu Yinglong Wang |
| author_facet | Hao Shi Ruixia Liu Changfang Chen Minglei Shu Yinglong Wang |
| author_sort | Hao Shi |
| collection | DOAJ |
| description | Baseline wander (BW) and electrocardiogram (ECG) noise reduction play an important role in ECG data analysis and disease diagnosis. This article introduces a sparse optimization method, which takes into account the group sparse characteristics of the signal, and combines low-pass filter to denoise the ECG signal and estimate the baseline. Derived from the classic total variation (TV) denoising method, a denoising method considering the structural characteristics of ECG signals is proposed. This method uses a band matrix to represent the sparse optimization problem, and adopts majorization-minimization (MM) algorithm to optimize the solution of the convergence problem. Through data comparison and detailed analysis, we first compares the method with two TV denoising methods. Then, the proposed method is validated in the MIT-BIH arrhythmia database of ECG signals, and compared with nonlocal means (NLM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The simulation experiment results show that the proposed algorithm has lower root mean square error (RMSE) and higher signal-to-noise ratio improvement (<inline-formula> <tex-math notation="LaTeX">$\mathrm {SNR\_{}imp}$ </tex-math></inline-formula>). |
| first_indexed | 2024-04-12T04:45:36Z |
| format | Article |
| id | doaj.art-39eead091b094e7fab0bcc65176fb7fb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:45:36Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-39eead091b094e7fab0bcc65176fb7fb2022-12-22T03:47:31ZengIEEEIEEE Access2169-35362021-01-019235952360710.1109/ACCESS.2021.30564599344703ECG Baseline Estimation and Denoising With Group Sparse RegularizationHao Shi0https://orcid.org/0000-0001-8315-4918Ruixia Liu1https://orcid.org/0000-0002-4044-5384Changfang Chen2https://orcid.org/0000-0001-7136-2525Minglei Shu3https://orcid.org/0000-0002-7136-1538Yinglong Wang4https://orcid.org/0000-0002-8350-7186Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaBaseline wander (BW) and electrocardiogram (ECG) noise reduction play an important role in ECG data analysis and disease diagnosis. This article introduces a sparse optimization method, which takes into account the group sparse characteristics of the signal, and combines low-pass filter to denoise the ECG signal and estimate the baseline. Derived from the classic total variation (TV) denoising method, a denoising method considering the structural characteristics of ECG signals is proposed. This method uses a band matrix to represent the sparse optimization problem, and adopts majorization-minimization (MM) algorithm to optimize the solution of the convergence problem. Through data comparison and detailed analysis, we first compares the method with two TV denoising methods. Then, the proposed method is validated in the MIT-BIH arrhythmia database of ECG signals, and compared with nonlocal means (NLM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The simulation experiment results show that the proposed algorithm has lower root mean square error (RMSE) and higher signal-to-noise ratio improvement (<inline-formula> <tex-math notation="LaTeX">$\mathrm {SNR\_{}imp}$ </tex-math></inline-formula>).https://ieeexplore.ieee.org/document/9344703/ECG denoisingbaseline estimationsparse optimizationgroup sparsity penalty |
| spellingShingle | Hao Shi Ruixia Liu Changfang Chen Minglei Shu Yinglong Wang ECG Baseline Estimation and Denoising With Group Sparse Regularization IEEE Access ECG denoising baseline estimation sparse optimization group sparsity penalty |
| title | ECG Baseline Estimation and Denoising With Group Sparse Regularization |
| title_full | ECG Baseline Estimation and Denoising With Group Sparse Regularization |
| title_fullStr | ECG Baseline Estimation and Denoising With Group Sparse Regularization |
| title_full_unstemmed | ECG Baseline Estimation and Denoising With Group Sparse Regularization |
| title_short | ECG Baseline Estimation and Denoising With Group Sparse Regularization |
| title_sort | ecg baseline estimation and denoising with group sparse regularization |
| topic | ECG denoising baseline estimation sparse optimization group sparsity penalty |
| url | https://ieeexplore.ieee.org/document/9344703/ |
| work_keys_str_mv | AT haoshi ecgbaselineestimationanddenoisingwithgroupsparseregularization AT ruixialiu ecgbaselineestimationanddenoisingwithgroupsparseregularization AT changfangchen ecgbaselineestimationanddenoisingwithgroupsparseregularization AT mingleishu ecgbaselineestimationanddenoisingwithgroupsparseregularization AT yinglongwang ecgbaselineestimationanddenoisingwithgroupsparseregularization |