Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis

1996 ◽  
Vol 3 (1) ◽  
pp. 17-26 ◽  
Author(s):  
W.J. Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Vibration Analysis ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Vibration Signals ◽  
Time Frequency ◽  
Special Cases ◽  
Mechanical Faults ◽  
Short Time

The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

A Method for Extracting Characteristic Frequency Components of Blood Flow Signals Based on Wavelet Transform

2013 ◽  
Vol 313-314 ◽  
pp. 1221-1224 ◽  
Author(s):  
Ruo Fei Cui ◽  
Si Te Luo ◽  
Li Qian Lu ◽  
Wei Wei Zhou ◽  
Zeng Yong Li
Keyword(s):  
Blood Flow ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Frequency Resolution ◽  
Peripheral Blood Flow ◽  
Frequency Interval ◽  
Frequency Method ◽  
Doppler Flowmetry ◽  
Time Frequency ◽  
Frequency Components

The objective of this paper is to propose a method for exacting the characteristic frequency components of blood flow signals based on wavelet transform. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry (LDF). In the frequency interval from 0.008 to 2.0 Hz, the LDF signal consists of components with five different characteristic frequenciesmetabolic (0.008-0.02Hz), neurogenic (0.02-0.06Hz), myogenic (0.06-0.15Hz), respiratory (0.15-0.4Hz) and cardiac (0.4-2.0Hz). The five frequency components were extracted in time domain and reconstructed using cubic spline interpolation in this study. The results showed that it was an effective way to extract each component of blood flow signals.

Empirical Wavelet Transform; Stationary and Nonstationary Signals

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Sedigheh Ghofrani
Keyword(s):  
Wavelet Transform ◽  
Digital Signal ◽  
Signal Decomposition ◽  
Early Nineteenth Century ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Empirical Wavelet Transform ◽  
Frequency Components ◽  
The Relationship

Signal decomposition into the frequency components is one of the oldest challenges in the digital signal processing. In early nineteenth century, Fourier transform (FT) showed that any applicable signal can be decomposed by unlimited sinusoids. However, the relationship between time and frequency is lost under using FT. According to many researches for appropriate time-frequency representation, in early twentieth century, wavelet transform (WT) was proposed Wavelet transform (WT) is a well-known method which developed in order to decompose a signal into frequency components. In contrast with original WT which is not adaptive according to the input signal, empirical wavelet transform (EWT) was proposed to overcome this problem. In this paper, the performance of WT and EWT in terms of signal decomposing into basic components are compared. For this purpose, a stationary signal include five sinusoids and ECG as biomedical and nonstationary signal are used. Due to being non-adaptive, WT may remove signal components but EWT because of being adaptive is appropriate. EWT can also extract the baseline of ECG signal easier than WT.

Guided Waves in Steel Rails – Experimental Works and Wavelet Signal Processing

2007 ◽  
Vol 347 ◽  
pp. 115-120
Author(s):  
Magdalena Rucka ◽  
Krzysztof Wilde
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Guided Waves ◽  
Flaw Detection ◽  
Transverse Waves ◽  
Dispersive Waves ◽  
Time Frequency ◽  
Time Signals ◽  
Frequency Components ◽  
Short Time

This paper presents experimental study on dispersive waves propagation in steel rails. The propagation of longitudinal and transverse waves was generated by an impulse hammer and measured in three points. Wavelet transform (WT) and short time Fourier transform (STFT) were applied to analyze the time signals. Analysis of signal by STFT does not provide a proper timefrequency representation due to a fixed size window. The wavelet transform can effectively identify the time-frequency components in waves. The wavelet signal processing of the experimental wave propagation signals is intended to be used for rail flaw detection.

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