Automated diagnosis of epileptic electroencephalogram using independent component analysis and discrete wavelet transform for different electroencephalogram durations

2012 ◽  
Vol 227 (3) ◽  
pp. 234-244 ◽  
Author(s):  
Acharya UR ◽  
Yanti R ◽  
Swapna G ◽  
Sree VS ◽  
Martis RJ ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Discrete Wavelet Transform ◽  
Component Analysis ◽  
Independent Component ◽  
Discrete Wavelet ◽  
Automated Diagnosis

scholarly journals High impedance fault detection in 11 kV overhead line with discrete wavelet transform and independent component analysis

2021 ◽  
Vol 24 (2) ◽  
pp. 661
Author(s):  
Md Ferdouse Hossain Bhuiya ◽  
Rohaiza Hamdan ◽  
Dur Mohammad Soomro ◽  
Abdelrehman Omer Idris ◽  
Hussain Sharif
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Discrete Wavelet Transform ◽  
Component Analysis ◽  
Independent Component ◽  
Discrete Wavelet ◽  
Advanced Technique ◽  
High Impedance ◽  
High Impedance Fault

This paper proposes an analysis of high-impedance fault detection algorithms for medium voltage distribution lines based on the discrete wavelet transform (DWT) technique and a more advanced technique named independent component analysis (ICA) independently. Three-phase distribution line model and two diodes high impedance fault model, which represents the unsymmetrical fault current of electric arc, simulated using MATLAB/Simulink. High impedance fault (HIF) detection algorithm initially analyzes the sampled current waveforms through DWT and the resultant third level high-frequency components “d3” coefficients are analyzed through one cycle moving window approach. The proposed algorithm successfully detects any HIF in the distribution current even if there is a slight or no difference in the amplitude of the HIF and the waveform of the phase current. On the other hand, the ICA more developed algorithm than DWT successfully separated the noise signals from the obtained current waveforms and HIF noise signals can be differentiated with non-HIF noise signals. Because of this reason ICA is chosen in this research. The detected HIF current can be from 50 ma and up.

BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 595-608 ◽  
Author(s):  
SATOSHI HORIHATA ◽  
ZHONG ZHANG ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Signal Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Emg Signal ◽  
Electrocardiogram Ecg

Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.

Separation and Reproduction of Mixed Images Using 2-D Complex Wavelet Transform

2010 ◽  
Vol 36 ◽  
pp. 466-475
Author(s):  
Tsutomu Matsuura ◽  
Amirul Faiz ◽  
Kouji Kiryu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Wavelet Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Complex Wavelet ◽  
Complex Valued

The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.

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