A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis
Abstract Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily at...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21776-2 |
| _version_ | 1798044526509555712 |
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| author | Shiza Saleem Ahsan H. Khandoker Mohanad Alkhodari Leontios J. Hadjileontiadis Herbert F. Jelinek |
| author_facet | Shiza Saleem Ahsan H. Khandoker Mohanad Alkhodari Leontios J. Hadjileontiadis Herbert F. Jelinek |
| author_sort | Shiza Saleem |
| collection | DOAJ |
| description | Abstract Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10–5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise. |
| first_indexed | 2024-04-11T23:05:40Z |
| format | Article |
| id | doaj.art-c3eb370df4014f9597b99c65710eba5d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T23:05:40Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c3eb370df4014f9597b99c65710eba5d2022-12-22T03:58:02ZengNature PortfolioScientific Reports2045-23222022-11-0112111510.1038/s41598-022-21776-2A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysisShiza Saleem0Ahsan H. Khandoker1Mohanad Alkhodari2Leontios J. Hadjileontiadis3Herbert F. Jelinek4Department of Biomedical Engineering, Khalifa UniversityDepartment of Biomedical Engineering, Khalifa UniversityHealthcare Engineering Innovation Center, Khalifa UniversityDepartment of Biomedical Engineering, Khalifa UniversityDepartment of Biomedical Engineering, Khalifa UniversityAbstract Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10–5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.https://doi.org/10.1038/s41598-022-21776-2 |
| spellingShingle | Shiza Saleem Ahsan H. Khandoker Mohanad Alkhodari Leontios J. Hadjileontiadis Herbert F. Jelinek A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis Scientific Reports |
| title | A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis |
| title_full | A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis |
| title_fullStr | A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis |
| title_full_unstemmed | A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis |
| title_short | A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis |
| title_sort | two step pre processing tool to remove gaussian and ectopic noise for heart rate variability analysis |
| url | https://doi.org/10.1038/s41598-022-21776-2 |
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