A Study on Discrete Wavelet-Based Noise Removal from EEG Signals

2010 ◽  
pp. 593-599 ◽  
Author(s):  
K. Asaduzzaman ◽  
M. B. I. Reaz ◽  
F. Mohd-Yasin ◽  
K. S. Sim ◽  
M. S. Hussain
Keyword(s):  
Noise Removal ◽  
Discrete Wavelet ◽  
Eeg Signals

scholarly journals Performance Analysis of Adaptive Filters with various Wavelets for Noise Removal in EEG Signals

2019 ◽  
Vol 9 (2) ◽  
pp. 3071-3076
Keyword(s):  
Adaptive Filters ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Performance Parameters ◽  
Eeg Signals ◽  
Analysis Techniques ◽  
Wrong Diagnosis ◽  
Wavelet Transformations

Noise removal from recorded EEG signal is most essential for better analysis of brain disorders. During recoding time, EEG signals are usually contaminated by various noise and distortions due to several artifacts. These noisy EEG signals may lead to wrong diagnosis of brain disorders. There are several techniques available to remove the noise from EEG signals. But these techniques are unable to remove the noise completely. However, they can minimize the noise in EEG signals so that the physicians can predict brain disorders. This work presents to minimize the noise by Discrete Wavelet Transform Methods using haar, db2, symlet and coiflet wavelets. EEG original signals from public EEG database are used for experimentation and wavelet transformations, are applied by using Matlab code. The filters performance is measured and analyzed on the basis of performance parameters like SNR and MSE which are calculated for various step sizes of signal and filter orders. Wavelet analysis techniques shows better performance when compared to others

Facial expression recognition using best tree RD-LGP encoded features and HMM

2018 ◽  
Vol 16 (06) ◽  
pp. 1850047 ◽  
Author(s):  
Ahmed A. Nashat
Keyword(s):  
Facial Expression ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ratio Test ◽  
Expression Recognition ◽  
Daily Lives ◽  
Face Expression ◽  
Bayesian Test

Hand and face gestures enable deaf people to communicate in their daily lives rather than speaking. This paper describes a hidden Markov model (HMM)-based framework for face sign recognition and detection. The observation vectors used to characterize the states of the HMM are obtained using the best tree local gradient pattern (LGP) encoded features. Each face gesture is modeled as a five-state HMM. The problem of facial expression classification is posed as a composite seven-classes multi-hypothesis Bayesian test. The likelihood ratio test showed that the overall recognition rate for the proposed model is higher than the HMM-local binary pattern descriptor by 6.4%. The overall recognition rate is enhanced by 8.6% using the discrete wavelet packet best tree decomposition filter as a pre-processing noise removal tool. In addition, the overall recognition rate ranges from 84.3%, for the seven classes Bayesian test, to 100%, for lower number of classes depending upon the type of the face gesture. The proposed face expression algorithm reduces significantly the computational complexity of previous HMM-based face expression recognition systems, and still preserve the recognition rate.

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

EEG Signals Classification Using Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 215-224
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Support Vector Machine ◽  
Wavelet Decomposition ◽  
Moving Average ◽  
Real Life ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Decomposition Level ◽  
Sensitivity Specificity

We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

scholarly journals Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuanfa Wang ◽  
Zunchao Li ◽  
Lichen Feng ◽  
Chuang Zheng ◽  
Wenhao Zhang
Keyword(s):  
Computational Complexity ◽  
Extreme Learning Machine ◽  
Classification System ◽  
Classification Accuracy ◽  
Detection System ◽  
Automatic Detection ◽  
Seizure Detection ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Learning Machine

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

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