EEG Signal Denoising Using Haar Transform and Maximal Overlap Discrete Wavelet Transform (MODWT) for the Finding of Epilepsy
Wavelet transform filters the signal without changing the pattern of the signal. The transformation techniques have been applied to the continuous time domain signals. The chapter is devoted to the study of the EEG (ElectroEncephaloGram) Signal processing using Haar wavelet transform and Maximal ove...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2020
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/73163 |
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| Sumario: | Wavelet transform filters the signal without changing the pattern of the signal. The transformation techniques have been applied to the continuous time domain signals. The chapter is devoted to the study of the EEG (ElectroEncephaloGram) Signal processing using Haar wavelet transform and Maximal overlap discrete wavelet transform (MODWT) for the analyzing of Epilepsy. Haar transform returns the approximation coefficients and detail coefficients. Detail coefficients are generally referred to as the wavelet coefficients and are a highpass representation of the input. In this chapter, with the help of Haar transform, the detailed coefficients of the input signal have been analyzed for the detection of Epilepsy. Maximal overlap discrete wavelet transform filters the noise coefficients of the input signal in each and every level, and it has displayed the filtered output signal. |
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| ISBN: | 978-1-83962-288-5 |
| DOI: | 10.5772/intechopen.93180 |