An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors
In bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3527 |
| _version_ | 1797607061054291968 |
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| author | Rodrigo Aviles-Espinosa Henry Dore Elizabeth Rendon-Morales |
| author_facet | Rodrigo Aviles-Espinosa Henry Dore Elizabeth Rendon-Morales |
| author_sort | Rodrigo Aviles-Espinosa |
| collection | DOAJ |
| description | In bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of the noise signals or to have certain periodicity and stability, making the noise estimation difficult to predict. In this paper, we solve these challenges through the development of an experimental method applied to bio-signal denoising using a combined approach. This is based on the implementation of unconventional electric field sensors used for creating a noise replica required to obtain the ideal Wiener filter transfer function and achieve further noise reduction. This work aims to investigate the suitability of the proposed approach for real-time noise reduction affecting bio-signal recordings. The experimental evaluation presented here considers two scenarios: (a) human bio-signals trials including electrocardiogram, electromyogram and electrooculogram; and (b) bio-signal recordings from the MIT-MIH arrhythmia database. The performance of the proposed method is evaluated using qualitative criteria (i.e., power spectral density) and quantitative criteria (i.e., signal-to-noise ratio and mean square error) followed by a comparison between the proposed methodology and state of the art denoising methods. The results indicate that the combined approach proposed in this paper can be used for noise reduction in electrocardiogram, electromyogram and electrooculogram signals, achieving noise attenuation levels of 26.4 dB, 21.2 dB and 40.8 dB, respectively. |
| first_indexed | 2024-03-11T05:24:59Z |
| format | Article |
| id | doaj.art-9d3525eaefae46289f3e5ba0f6a6e009 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:24:59Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9d3525eaefae46289f3e5ba0f6a6e0092023-11-17T17:33:52ZengMDPI AGSensors1424-82202023-03-01237352710.3390/s23073527An Experimental Method for Bio-Signal Denoising Using Unconventional SensorsRodrigo Aviles-Espinosa0Henry Dore1Elizabeth Rendon-Morales2Robotics and Mechatronics Systems Research Group, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UKRobotics and Mechatronics Systems Research Group, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UKRobotics and Mechatronics Systems Research Group, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UKIn bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of the noise signals or to have certain periodicity and stability, making the noise estimation difficult to predict. In this paper, we solve these challenges through the development of an experimental method applied to bio-signal denoising using a combined approach. This is based on the implementation of unconventional electric field sensors used for creating a noise replica required to obtain the ideal Wiener filter transfer function and achieve further noise reduction. This work aims to investigate the suitability of the proposed approach for real-time noise reduction affecting bio-signal recordings. The experimental evaluation presented here considers two scenarios: (a) human bio-signals trials including electrocardiogram, electromyogram and electrooculogram; and (b) bio-signal recordings from the MIT-MIH arrhythmia database. The performance of the proposed method is evaluated using qualitative criteria (i.e., power spectral density) and quantitative criteria (i.e., signal-to-noise ratio and mean square error) followed by a comparison between the proposed methodology and state of the art denoising methods. The results indicate that the combined approach proposed in this paper can be used for noise reduction in electrocardiogram, electromyogram and electrooculogram signals, achieving noise attenuation levels of 26.4 dB, 21.2 dB and 40.8 dB, respectively.https://www.mdpi.com/1424-8220/23/7/3527ECGnoise removalWienerfilteringsignal processing and electric field sensing |
| spellingShingle | Rodrigo Aviles-Espinosa Henry Dore Elizabeth Rendon-Morales An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors Sensors ECG noise removal Wiener filtering signal processing and electric field sensing |
| title | An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors |
| title_full | An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors |
| title_fullStr | An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors |
| title_full_unstemmed | An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors |
| title_short | An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors |
| title_sort | experimental method for bio signal denoising using unconventional sensors |
| topic | ECG noise removal Wiener filtering signal processing and electric field sensing |
| url | https://www.mdpi.com/1424-8220/23/7/3527 |
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