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 Consciousness Levels Detection Using Discrete Wavelet Transforms on Single Channel EEG Under Simulated Workload Conditions

2017 ◽  
Vol 2 (4) ◽  
pp. 391
Author(s):  
S.N. Kartik ◽  
Mohanavelu K. ◽  
M.V. Mallikarjuna Reddy ◽  
M. Anandan
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Transforms ◽  
Single Channel ◽  
Average Energy ◽  
Electrode System ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Complex Signals

<p class="p1">EEG signal is one of the most complex signals having the lowest amplitude which makes it challenging for analysis in real-time. The different waveforms like alpha, beta, theta and delta were studied and selected features were related with the consciousness levels. The consciousness levels detection is useful for estimating the subjects’ performance in certain selected tasks which requires high alertness. This estimation was performed by analyzing signal properties of the EEG using features extracted through discrete wavelet transform with a moving window of 10 seconds with 90% overlap. The EEG signal is decomposed in to wavelets and the average energy and power of the coefficients related to the EEG bands is taken as the features. The data is collected from standard EEG machine from the volunteers as per the protocol. C3 and C4 locations (unipolar) of the standard 10-20 electrode system were selected. The central region of the brain is most optimal location for the consciousness levels detection. The estimation of the data using Discrete Wavelet Transform (DWT) energy, power features provided better accuracy when the central regions were chosen. An accuracy of 99% was achieved when the algorithm was implemented using a classifier based on linear kernel support vector machines (SVM).</p>

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%).

scholarly journals Identification of optimum segment in single channel EEG biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1847
Author(s):  
Muhammad Afif Hendrawan ◽  
Pramana Yoga Saputra ◽  
Cahya Rahmad
Keyword(s):  
Single Channel ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Alpha Band ◽  
Biometric System ◽  
Linear Discriminant ◽  
Power Spectral ◽  
Eyes Open ◽  
High Level

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

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.

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