ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions
The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the D...
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| Format: | Article |
| Language: | English |
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Universidad de Cuenca
2018-06-01
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| Series: | Maskana |
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| Online Access: | https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/1858 |
| _version_ | 1818665306350944256 |
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| author | Marco Gualsaquí Iván Vizcaíno Víctor Proaño Marco Flores |
| author_facet | Marco Gualsaquí Iván Vizcaíno Víctor Proaño Marco Flores |
| author_sort | Marco Gualsaquí |
| collection | DOAJ |
| description | The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the Discrete Wavelet Transform (DWT) theory and a set of thresholds filters for efficient noise filtering. For the assessment process, we used ECG records from MIT-BIH Arrhythmia database (MITDB) and standardized noise signals (muscle activity and electrode-skin contact) database from the Noise Stress Test database. In addition to the ECG signals a white Gaussian noise present in electrical type signals was added. Furthermore, as a first step we considered baseline wander and power line interference reduction. The metrics used are the Signal-to-Noise Ratio (SNR), the Root Mean Squared Error (RMSE), the Percent Root mean square Difference (PRD), and the Euclidian L2 Norm standard (L2N). Results reveal that there is not a single combination of filtering thresholds (function and value) to minimize all types of noise and interference present in ECG signals. Reason why an ECG denoising algorithm is proposed which allows choosing the appropriate combination (function-value) threshold, where the SNR values were the maximum and the error values were the minimum. |
| first_indexed | 2024-12-17T05:46:32Z |
| format | Article |
| id | doaj.art-81dc00cc59f04227b6e066db435c2e33 |
| institution | Directory Open Access Journal |
| issn | 1390-6143 2477-8893 |
| language | English |
| last_indexed | 2024-12-17T05:46:32Z |
| publishDate | 2018-06-01 |
| publisher | Universidad de Cuenca |
| record_format | Article |
| series | Maskana |
| spelling | doaj.art-81dc00cc59f04227b6e066db435c2e332022-12-21T22:01:17ZengUniversidad de CuencaMaskana1390-61432477-88932018-06-0191105114https://doi.org/10.18537/mskn.09.01.10ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functionsMarco Gualsaquí0Iván Vizcaíno1Víctor Proaño2Marco Flores3Universidad de las Fuerzas Armadas (ESPE)Universidad de las Fuerzas Armadas (ESPE)Universidad de las Fuerzas Armadas (ESPE)Universidad de las Fuerzas Armadas (ESPE)The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the Discrete Wavelet Transform (DWT) theory and a set of thresholds filters for efficient noise filtering. For the assessment process, we used ECG records from MIT-BIH Arrhythmia database (MITDB) and standardized noise signals (muscle activity and electrode-skin contact) database from the Noise Stress Test database. In addition to the ECG signals a white Gaussian noise present in electrical type signals was added. Furthermore, as a first step we considered baseline wander and power line interference reduction. The metrics used are the Signal-to-Noise Ratio (SNR), the Root Mean Squared Error (RMSE), the Percent Root mean square Difference (PRD), and the Euclidian L2 Norm standard (L2N). Results reveal that there is not a single combination of filtering thresholds (function and value) to minimize all types of noise and interference present in ECG signals. Reason why an ECG denoising algorithm is proposed which allows choosing the appropriate combination (function-value) threshold, where the SNR values were the maximum and the error values were the minimum.https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/1858ECG signaldenoisingDWTfiltering treshold |
| spellingShingle | Marco Gualsaquí Iván Vizcaíno Víctor Proaño Marco Flores ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions Maskana ECG signal denoising DWT filtering treshold |
| title | ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions |
| title_full | ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions |
| title_fullStr | ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions |
| title_full_unstemmed | ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions |
| title_short | ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions |
| title_sort | ecg signal denoising using discrete wavelet transform a comparative analysis of threshold values and functions |
| topic | ECG signal denoising DWT filtering treshold |
| url | https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/1858 |
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