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Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients.

Hassani M, Karami MR - J Med Signals Sens (2015 Jul-Sep)

Bottom Line: An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased.Hence, in many applications it is urgent to reduce the complexity of computation.The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.

ABSTRACT
The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

No MeSH data available.


Related in: MedlinePlus

Noisy signal y[n], estimated noise e[n] (it is depicted bigger for the purpose of better representation), and denoised signal d[n], for P = 2, when the data is real
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Figure 9: Noisy signal y[n], estimated noise e[n] (it is depicted bigger for the purpose of better representation), and denoised signal d[n], for P = 2, when the data is real

Mentions: We run the proposed algorithm for real data of length 0.48 s (1000 samples), but for the purpose of better representation, Figure 9 shows 100 samples of an EEG noisy signal, y[n], its estimated noise e[n] and denoised EEG signal d[n].


Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients.

Hassani M, Karami MR - J Med Signals Sens (2015 Jul-Sep)

Noisy signal y[n], estimated noise e[n] (it is depicted bigger for the purpose of better representation), and denoised signal d[n], for P = 2, when the data is real
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528358&req=5

Figure 9: Noisy signal y[n], estimated noise e[n] (it is depicted bigger for the purpose of better representation), and denoised signal d[n], for P = 2, when the data is real
Mentions: We run the proposed algorithm for real data of length 0.48 s (1000 samples), but for the purpose of better representation, Figure 9 shows 100 samples of an EEG noisy signal, y[n], its estimated noise e[n] and denoised EEG signal d[n].

Bottom Line: An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased.Hence, in many applications it is urgent to reduce the complexity of computation.The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.

ABSTRACT
The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

No MeSH data available.


Related in: MedlinePlus