| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000370575 |
| ISSN: | 1665-6423 |
| Autores: | Mamun, M1 Al-Kadi, Mahmoud2 Marufuzzaman, M2 |
| Instituciones: | 1Universiti Kebangsaan, Institute of Visual Informatics, Bangi Selangor. Malasia 2Universiti Kebangsaan Malaysia, Department of Electrical, Electronic and Systems Engineering, Bangi Selangor. Malasia |
| Año: | 2013 |
| Periodo: | Feb |
| Volumen: | 11 |
| Número: | 1 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | 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 |
| Disciplinas: | Ingeniería, Medicina |
| Palabras clave: | Diagnóstico, Sistema cardiovascular, Bioingeniería, Electroencefalograma, Transformada Wavelet |
| Keyword: | Engineering, Medicine, Cardiovascular system, Diagnosis, Bioengineering, Electroencephalogram, Wavelet transform |
| Texto completo: | Texto completo (Ver HTML) |
