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 Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

2021 ◽  
Author(s):  
Türker Tuncer ◽  
Sengul Dogan ◽  
Ganesh R. Naik ◽  
Paweł Pławiak
Keyword(s):  
Wavelet Transform ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Diagnostic Systems ◽  
Feature Generation ◽  
Eeg Signals ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Eeg Classification

AbstractElectroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.

scholarly journals Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features

2021 ◽  
Vol 25 (1) ◽  
pp. 13-24
Author(s):  
Zabir Al Nazi ◽  
◽  
A. B. M. Aowlad Hossain ◽  
Md. Monirul Islam ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Physical Disabilities ◽  
Variable Length ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Random Subspace ◽  
Complex Wavelet Transform ◽  
Eeg Signals ◽  
Random Subspace Ensemble

Classification of electroencephalography (EEG) signals for brain-computer interface has great impact on people having various kinds of physical disabilities. Motor imagery EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, random subspace ensemble network with variable length feature sampling has been proposed for improving the prediction accuracy of motor imagery EEG signal classification. The method has been tested on eight different subjects and a hybrid dataset of two subjects data combined. Discrete wavelet transform based de-noising scheme has been adopted to remove artifacts from the EEG signal. For sub-band selection, dual-tree complex wavelet Transform has been employed. Mutual information scoring has been used for univariate feature selection from the feature space. A comparative analysis has been carried out where random subspace ensemble network outperformed other classification models. The maximum accuracy obtained by the model was 90.00%. Furthermore, the model showed better performance on the hybrid dataset with an average accuracy of 86.00%. The findings of this study are expected to be useful in artificial limb movements through brain-computer interfacing for rehabilitation of people with such physical disabilities.

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.

scholarly journals EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2505 ◽  
Author(s):  
Fahd A. Alturki ◽  
Khalil AlSharabi ◽  
Akram M. Abdurraqeeb ◽  
Majid Aljalal
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Single Channel ◽  
Autism Spectrum ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.

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.

VIGILANCE ANALYSIS BASED ON EEG SIGNALS: SEEKING FOR SUITABLE FEATURES

2010 ◽  
Vol 18 (spec01) ◽  
pp. 81-99
Author(s):  
TIAN OUYANG ◽  
HONG-TAO LU ◽  
BAOLIANG LU
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Large Set ◽  
Eeg Signal ◽  
Simulation Environment ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Eeg Signal Processing

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. EEG signals are recorded and divided into five-second long trials. From these EEG trials, we extract feature vectors containing a large set of features. Random forest is used to rank the plenty of features and select the most important ones for later classification. After dimension reduction, sample vectors are trained and classified by Support Vector Machine (SVM). The proposed framework explores different methods of EEG signal processing to discover the most suitable features for a real-time vigilance monitoring system. We investigate and compare three different kinds of features which are based on Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Fractal Dimension (FD), respectively. On datasets acquired from 5 subjects, our result shows the CWT-based features reveal the highest classification accuracy (may reach over 96%). The DWT and FD-based features are less time-consuming in computation, and also reveal good result of classification accuracy (over 90%).

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

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.

ARTIFACT REMOVAL AND BRAIN RHYTHM DECOMPOSITION FOR EEG SIGNAL USING WAVELET APPROACH

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Syarifah Noor Syakiylla Sayed Daud ◽  
Rubita Sudirman
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Biomedical Signal ◽  
Brain Rhythm ◽  
Time And Energy ◽  
Eeg Signal Processing

This recent study introduces and discusses briefly the use of wavelet approach in removing the artifacts and extraction of features for electroencephalography (EEG) signal. Many of new approaches have been discovered by the researcher for processing the EEG signal. Generally, the EEG signal processing can be divided into pre-processing and post-processing.  The aim of processing is to remove the unwanted signal and to extract important features from the signal.  However, the selections of non-suitable approach affect the actual result and wasting the time and energy.  Wavelet is among the effective approach that can be used for processing the biomedical signal.  The wavelet approach can be performed in MATLAB toolbox or by coding, that require a simple and basic command. In this paper, the application of wavelet approach for EEG signal processing is introduced. Moreover, this paper also discusses the effect of using db3 mother wavelet with 5th decomposition level of stationary wavelet transform and db4 mother wavelet with 7th decomposition level of discrete wavelet transform in removing the noise and decomposing of the brain rhythm. Besides, the simulation result are also provided for better configuration.

Efficient and Adaptive Rotation Invariant Wavelet Transform

2003 ◽  
Vol 01 (03) ◽  
pp. 353-372
Author(s):  
Chi-Man Pun
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Information Cost ◽  
Image Size ◽  
Rotation Invariant ◽  
Adaptive Wavelet

It is well known that the sensitivity to translations and orientations is a major drawback in 2D discrete wavelet transform (DWT). In this paper, we have proposed an effective scheme for rotation invariant adaptive wavelet packet transform. During decomposition, the wavelet coefficients are obtained by applying a polar transform (PT) followed by a row-shift invariant wavelet packet decomposition (RSIWPD). In the first stage, the polar transform generates a row-shifted image and is adaptive to the image size to achieve complete and minimum sampling rate. In the second stage, the RSIWPD is applied to the row-shifted image to generate rotation invariant but over completed subbands of wavelet coefficients. In order to reduce the redundancy and computational complexity, we adaptively select some subbands to decompose and form a best basis representation with minimal information cost with respect to an appropriate information cost function. With this best basis representation, the original image can be reconstructed easily by applying a row-shift invariant wavelet packet reconstruction (RSIWPR) followed by an inverse polar transform (IPT). In the experiments, we study the application of this representation for texture classification and achieve 96.5% classification accuracy.

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