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Toplam kayıt 6, listelenen: 1-6
EEG denoising based on empirical mode decomposition and mutual information
(Springer Verlag, 2014)
Empirical mode decomposition (EMD) is a recently introduced decomposition method for non-stationary time series. EMD has an information preserving property so the sum of the decomposed intrinsic mode functions (IMF) can ...
Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats
(Springer London Ltd, 2014)
We explore the effect of using bagged decision tree (BDT) as an ensemble learning method with proposed time-domain feature extraction methods on electrocardiogram (ECG) arrhythmia beat classification comparing with single ...
Detrended Fluctuation Analysis For Empirical Mode Decomposition Based Denoising
(IEEE, 2014)
Empirical mode decomposition (EMD) is a recently proposed method to analyze non-linear and non-stationary time series by decomposing them into intrinsic mode functions (IMFs). One of the most popular application of such a ...
An improved hybrid feature reduction for increased breast cancer diagnostic performance
(Springer Verlag, 2014)
Methods: A hybrid method is proposed using the independent component analysis (ICA) and the discrete wavelet transform (DWT) to reduce feature vectors of Wisconsin diagnostic breast cancer (WDBC) data set. Two independent ...
Detrended fluctuation thresholding for empirical mode decomposition based denoising
(Academic Press Inc Elsevier Science, 2014)
Signal decompositions such as wavelet and Gabor transforms have successfully been applied in denoising problems. Empirical mode decomposition (EMD) is a recently proposed method to analyze non-linear and non-stationary ...
EOG denoising using empirical mode decomposition and detrended fluctuation analysis
(IEEE, 2014)
In this study, a method is presented for the removal of electrooculogram (EOG) noise from electroencephalography (EEG) recordings by using recently proposed data driven approach called Empirical Mode Decomposition (EMD). ...