Effectiveness of Wavelet Denoising on Electroencephalogram Signals
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2013
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| _version_ | 1876456512587563008 |
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| author | Md. Mamun |
| author_facet | Md. Mamun |
| contents | Effectiveness of Wavelet Denoising on Electroencephalogram Signals Md. Mamun Mahmoud Al-Kadi Mohd. Marufuzzaman Ingeniería denoising root mean square Electroencephalogram discrete wavelet transform Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram, eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filtering suffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employed discrete wavelet transform. Root mean square difference has been used to find the usefulness of the noise elimination. In this research, four different discrete wavelet functions have been used to remove noise from the Electroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show the effectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one for noise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noise elimination from the EEG signal on healthy subjects. 2013 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47426212014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.11 |
| format | Artículo científico |
| id | redalyc_47426212014 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Effectiveness of Wavelet Denoising on Electroencephalogram Signals Md. Mamun Ingeniería denoising root mean square Electroencephalogram discrete wavelet transform Effectiveness of Wavelet Denoising on Electroencephalogram Signals Md. Mamun Mahmoud Al-Kadi Mohd. Marufuzzaman Ingeniería denoising root mean square Electroencephalogram discrete wavelet transform Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram, eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filtering suffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employed discrete wavelet transform. Root mean square difference has been used to find the usefulness of the noise elimination. In this research, four different discrete wavelet functions have been used to remove noise from the Electroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show the effectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one for noise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noise elimination from the EEG signal on healthy subjects. 2013 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47426212014 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.11 |
| title | Effectiveness of Wavelet Denoising on Electroencephalogram Signals |
| topic | Ingeniería denoising root mean square Electroencephalogram discrete wavelet transform |
| url | https://www.redalyc.org/articulo.oa?id=47426212014 |