scholarly journals Wavelets for EEG Analysis

2020 ◽  
Author(s):  
Nikesh Bajaj
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Time Frequency ◽  
Analysis Technique ◽  
Artefact Removal ◽  
Short Time ◽  
Electroencephalogram Eeg

This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

Research on Goniometer Fault Detection of a Certain Missile Based on Wavelet Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1600-1604
Author(s):  
Zhu Lin Wang ◽  
Jiang Kun Mao ◽  
Zi Bin Zhang ◽  
Xi Wei Guo
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Discrete Wavelet Transform ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Analysis Method ◽  
Frequency Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Fault Characteristic

Aiming at the problem of existing time-frequency analysis methods was not effective to goniometer keeping fault of a certain missile, combined time -frequency analysis method of CWT and DWT for the fault was put forward based on the fault characteristic. The process of the method proposed was given and the time-frequency method of continuous and discrete wavelet transform was analysed. The signal when goniometer keeping fault occurred was analysed by the method that was put forward. The simulation showed that the method which was effective to the fault detecting could accurately detect the time and location of goniometer fault occurred.

REDUNDANT MORPHOLOGICAL WAVELET AND LOCAL PATTERN SPECTRUM

2013 ◽  
Vol 11 (04) ◽  
pp. 1360001
Author(s):  
TOMONARI YAMAGUCHI ◽  
MITSUHIKO FUJIO ◽  
KATSUHIRO INOUE
Keyword(s):  
Wavelet Analysis ◽  
Discrete Wavelet ◽  
Short Time Fourier Transform ◽  
Shape Information ◽  
Local Pattern ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
Stationary Signals ◽  
Shift Invariance ◽  
Short Time

Time-frequency analysis methods such as wavelet analysis are applied to investigate characteristic from non-stationary signals. In this study, we proposed redundant morphological wavelet analysis that was a kind of nonlinear discrete wavelet and redundant wavelet. This method analyzes a transition of shape information from signals in detail since this method keeps property of shift invariance though information of decomposition includes redundancy. Local pattern spectrum which corresponds to nonlinear short time Fourier transform is derived from this nonlinear wavelet. The characteristics of these methods were confirmed by applying to simulation data and actual data.

scholarly journals Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199226
Author(s):  
Jannatul Ferdous ◽  
Sujan Ali ◽  
Ekramul Hamid ◽  
Khademul Islam Molla
Keyword(s):  
Wavelet Transform ◽  
Performance Metrics ◽  
Wavelet Packet ◽  
Reference Signal ◽  
Computational Cost ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Finite Set ◽  
Artifact Suppression

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

scholarly journals Performance of Support Vector Machine in Classifying EEG Signal of Dyslexic Children using RBF Kernel

2018 ◽  
Vol 9 (2) ◽  
pp. 403
Author(s):  
AZA Zainuddin ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
Z. Mahmoodin
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Reading And Writing ◽  
Theta Waves ◽  
Rbf Kernel ◽  
Electroencephalogram Eeg ◽  
Kernel Parameters

Dyslexia is referred as learning disability that causes learner having difficulties in decoding, reading and writing words. This disability associates with learning processing region in the human brain. Activities in this region can be examined using electroencephalogram (EEG) which record electrical activity during learning process. This study looks into performance of Support Vector Machine (SVM) using RBF kernel in classifying EEG signal of Normal, Poor and Capable Dyslexic children during writing words and non-words. Discrete Wavelet Transform (DWT) with Daubechies order 2 was employed to extract the power of beta and theta waves of EEG signal. Beta and Theta/Beta ratio form the input features for classifier.  Multiclass one versus one SVM was used in the classification where RBF kernel parameters and box constraint values were varied with the factor of 10 to analyze performance of the classifier. It was found that the best performance of SVM with 91% overall accuracy was obtained when both kernel scale and box constraint are set to one.

EVALUATION OF A WAVELET BASED ASR FRONT-END

2007 ◽  
Vol 05 (04) ◽  
pp. 641-654 ◽  
Author(s):  
OMAR FAROOQ ◽  
SEKHARJIT DATTA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recognition Performance ◽  
Wavelet Packet ◽  
Additive White Gaussian Noise ◽  
Discrete Wavelet ◽  
Mel Frequency Cepstral Coefficients ◽  
Admissible Wavelet ◽  
Transform Coefficients ◽  
Short Time

In this paper, we propose the use of the wavelet transform for the extraction of features for phonemes in order to overcome some of the shortcomings of short time Fourier transform. New log-energy based features are proposed using discrete wavelet transform as well as wavelet packets and their recognition performance has been evaluated. These features overcome the problem of shift variance as encountered in the features based on the discrete wavelet transform coefficients. The effect on the recognition performance by choosing different mother wavelets for the decomposition and window duration is also studied. Finally, a scheme based on the admissible wavelet packet has also been proposed and the results are discussed and compared with the frequently used Mel Frequency Cepstral Coefficients based features. The recognition performance of these features is further evaluated in the presence of different level of additive white Gaussian noise.

scholarly journals Evaluation of the Technical Condition of the Active Part of the High Power Transformer Based on Measurements and Analysis of Vibroacoustic Signals

2017 ◽  
Vol 42 (2) ◽  
pp. 313-320 ◽  
Author(s):  
Sebastian Borucki ◽  
Andrzej Cichoń ◽  
Henryk Majchrzak ◽  
Dariusz Zmarzły
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
High Power ◽  
Power Transformer ◽  
Technical Condition ◽  
Active Part ◽  
Discrete Wavelet ◽  
Idle Speed ◽  
Time Frequency ◽  
Short Time

Abstract This article presents the results of research connected with the development and industrial use of vibroacoustic methods for the evaluation of the technical condition of the active part of transformers. The article presents the results of the analysis of vibrations generated by the high power transformer in which a defect was found on the basis of tests of oil carried out using the chromatography tests. In order to confirm the damage of the active part of this transformer, vibroacoustic measurements were performed in three states of its operation. The measurement using the classical vibroacoustic method included the registration of vibrations at the idle speed and during the load operation of the transformer. The original diagnostic method, so-called the modified vibroacoustic method, was also used during the measurement. The analysis of signals recorded using the classical vibroacoustic method was carried out in the frequency domain by indicating the amplitude of even harmonic vibrations. However, the analysis of signals measured during the commissioning of the transformer was conducted in the time-frequency domain using the short-time Fourier transform (STFT), continuous wavelet transform (CWT), and discrete wavelet transform (DWT). On the basis of the analysis of the results obtained it was stated that the increased level of vibrations of this transformer is a consequence of the loss of rigidity of the mechanical structure of its core.

scholarly journals Epilepsy Detection Using DWT Based Hurst Exponent and SVM, K-NN Classifiers

2018 ◽  
Vol 19 (4) ◽  
pp. 311-319 ◽  
Author(s):  
Ashok Sharmila ◽  
Saiby Madan ◽  
Kajri Srivastava
Keyword(s):  
Wavelet Transform ◽  
Healthy Subject ◽  
Classification Accuracy ◽  
Hurst Exponent ◽  
Sensory System ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eyes Open ◽  
Electroencephalogram Eeg

Abstract Epilepsy is a typical neurological issue which influence the focal sensory system and can make individuals have seizure. It can be surveyed by electroencephalogram (EEG). A wavelet based HURST EXPONENT strategy is displayed for the analysis of epilepsy. This strategy deals with the nonlinear analysis of EEG signals. Discrete wavelet transform is used to disintegrate the original EEG signal into specific subbands. The hurst exponent of different sub-bands is employed and then fed into two classifiers, namely SVM and KNN. The highest classification accuracy obtained in the presented work is 99% for healthy subject data versus epileptic data is obtained by SVM. However, the corresponding accuracy between normal subject data and epileptic data using SVM is obtained as 99% and 93% for the eyes open and eyes shut conditions, respectively. The detailed analysis of the methodology and results has been discussed in the paper.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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