Spectral analysis of bioelectric signals by adapted wavelet transforms

2002 ◽  
Author(s):  
U. Wiklund ◽  
M. Akay
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Bioelectric Signals

Time-Local Spectral Analysis for Non-Stationary Time Series: The S-Transform for Noisy Signals

2003 ◽  
Vol 03 (03) ◽  
pp. L357-L364 ◽  
Author(s):  
C. R. Pinnegar ◽  
L. Mansinha
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Gaussian White Noise ◽  
Stationary Time Series ◽  
Time Frequency ◽  
S Transform ◽  
Undesirable Characteristic ◽  
Noisy Signals ◽  
Short Time

The S-transform is a method of time-local spectral analysis (also known as time-frequency analysis), a modified short-time Fourier Transform, in which the width of the analyzing window scales inversely with frequency, in analogy with continuous wavelet transforms. If the time series is non-stationary and consists of a mix of Gaussian white noise and a deterministic signal, though, this type of scaling leads to larger apparent noise amplitudes at higher frequencies. In this paper, we introduce a modified S-transform window with a different scaling function that addresses this undesirable characteristic.

scholarly journals USING WAVELETS IN ECONOMICS. AN APPLICATION ON THE ANALYSIS OF WAGE-PRICE RELATION

2017 ◽  
Vol 2 (1) ◽  
pp. 32-42
Author(s):  
Vasile-Aurel Caus ◽  
Daniel Badulescu ◽  
Mircea Cristian Gherman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Fourier Transforms ◽  
Nominal Wage ◽  
Economic Issues ◽  
Wavelet Methods ◽  
Theoretical Considerations ◽  
Analysis Of Time Series

In the last decades, more and more approaches of economic issues have used mathematical tools, and among the most recent ones, spectral and wavelet methods are to be distinguished. If in the case of spectral analysis the approaches and results are sufficiently clear, while the use of wavelet decomposition, especially in the analysis of time series, is not fully valorized. The purpose of this paper is to emphasize how these methods are useful for time series analysis. After theoretical considerations on Fourier transforms versus wavelet transforms, we have presented the methodology we have used and the results obtained by using wavelets in the analysis of wage-price relation, based on some empirical data. The data we have used is concerning the Romanian economy - the inflation and the average nominal wage denominated in US dollars, between January 1991 and May 2016, highlighting that the relation between nominal salary and prices can be revealed more accurately by use of wavelets

Local Spectral Analysis of Turbulent Flows using Wavelet Transforms

1993 ◽  
pp. 1-26 ◽  
Author(s):  
C. Basdevant ◽  
V. Perrier ◽  
T. Philipovitch ◽  
M. Do Khac
Keyword(s):  
Spectral Analysis ◽  
Turbulent Flows ◽  
Wavelet Transforms

scholarly journals Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 6654-6659
Keyword(s):  
Feature Extraction ◽  
Spectral Analysis ◽  
Power Quality ◽  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Renewable Energy Resources ◽  
Statistical Parameters ◽  
Singular Spectral Analysis

In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.

EEG Signal of Epiliptic Patient by Fast Fourier and Wavelet Transforms

2013 ◽  
Vol 61 (1) ◽  
Author(s):  
Goh Chien Yong ◽  
Normah Maan ◽  
Tahir Ahmad
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Graphical Form ◽  
Valuable Insight ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Time Frequency ◽  
C Programming ◽  
Better Than

Electroencephalography (EEG) is one of the field in diagnosing g epilepsy. Analysis of the EEG records can provide valuable insight and improve understanding of the mechanisms causing epileptic disorders. In this paper, the fast Fourier transform (FFT) and wavelet transform are used as spectral analysis tools of the EEG signals. These methods are chosen because they provide time–frequency shifted on the EEG signals. Since the frequency characteristics are important information that can be observed from the signals, FFT and wavelet transform are among a the best methods in analysis of EEG signals. The comparisons between these two methods are also carried out. Result showed that the wavelet transform is better than FFT in analysis of EEG signals. A software for analysing EEG signal is also developed using C++ programming. The software is able to compute and show the results of the analysis signal data by both of the two methods in graphical form.

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