Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Autism spectrum disorder (ASD) is a serious mental disorder affecting social behavior. Some children also face intellectual delay. In people with ASD, the signals detected have abnormalities compared to normal people. This can be a reference in diagnosing the disorder with electroencephalography (EEG). This study will analyze the effect of Power spectral density (PSD) on the EEG of autistic children and also compare it with the PSD value on the EEG of normal children using the Welch Periodogram method approach. In the preprocessing stage, the Independent Component Analysis (ICA) method will be applied to remove artifacts, and a Finite Impulse Response (FIR) filter to reduce noise in the EEG signal. The study results indicate differences in the PSD values ​​obtained in the autistic and normal EEG signals. The PSD value obtained in the autistic EEG signal is higher than the normal EEG signal in all frequency sub-bands. From the study results, the highest PSD value obtained by the autistic EEG signal is in the delta sub-band, which is 54.06 dB/Hz, while the normal EEG signal is only 33.14 dB/Hz at the same frequency sub-band. And in the Alpha and Beta sub-bands, the normal EEG signal increases the PSD value, while in the autistic EEG signal, the PSD value decreases in the Alpha and Beta sub-bands. In addition, FIR and ICA methods can also reduce noise and artifacts contained in autistic and normal EEG signals.

This paper studied the epilepsy signal in different frequency bands based on the average phase-synchronization time matrix (APSTM). APSTM algorithm can probe the change in the synchrony of the underlying system with high sensitivity, and be applied to multichannel, nonlinear time series in noisy. This paper uses the EEG filter provided by the EEGLAB to extract the four common waves : theta, alpha, beta and gamma, which are from the normal and epilepsy EEG signals. The four frequency bands of EEG signals are analysed based on the APSTM algorithm, and the results show that the APSTM determinant can distinguish the epilepsy from the normal EEG signals clearly. And when APSTM determinant detects epileptic discharge ,the theta wave is the most significant, and the beta wave can only generally detect epileptic discharge, while alpha and gamma waves are ineffective.

Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

In this paper, a method for classification of epileptic EEG signals based on k-NN classifier is discussed. This scheme provides an improved performance in terms of sensitivity, specificity and accuracy. EEG signal is decomposed into sub bands using multi-wavelet transform and spectral features such as mean spectral magnitude, spectral entropy, and spectral squared entropies are extracted. The distributions of these features for normal and epileptic EEG signal are clustered in different regions which can be utilized to yield good classification result. The results show that classification of these features using k-NN achieved an accuracy of 98.585%. In order to evaluate the efficiency of the proposed method, classification is also done using ANN and SVM which yielded an accuracy of 98.415% and 87.5% respectively.

Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

The electroencephalogram (EEG) signal is an electrical representation of brain's working that reflects various physiological and pathological activities such as alcoholism. Alcohol can affect whole parts of the body but, it particularly affects the brain, heart, liver, and the immune system; its effects on the brain are called brain disorders. Nowadays, automatic identification of alcoholic subjects based on EEG signals has become one of the challenging problems in biomedical research. In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed. The uniqueness of EMD method is to decompose non‐stationary and non‐linear signals into a set of stationary intrinsic mode functions (IMFs) that are band limited signals. These IMFs are transformed into analytic representations by applying the Hilbert transform. From these analytic IMFs, various features namely mean, kurtosis, skewness, entropy, and negentropy are extracted; these features are used as input to least squares support vector machines (LS‐SVMs) classifier with radial basis function (RBF) kernel and polynomial kernel. The accuracy results achieved for LS‐SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92%, respectively, which are found to be better as compared with other state‐of‐the‐art methods.

The research employs EEG in combination with the 1DCNN-LSTM algorithm for the purposes of identifying and classifying people with varying risks of schizophrenia. This collection of data for schizophrenia's high-risk and low-risk groups comprises EEG signals from 98 people, the division was made between a group of 50 with normal EEG signals and another group of 48 with high-risk indicators for schizophrenia. The duration of measurement for every participant stood at 12 minutes. The data set was processed in advance, encompassing tasks like channel exclusion, re-referencing, dividing the dataset, performing independent component analysis (ICA), windowing, normalizing, and segregating it into the testing, training, and validation sets. Subsequently, the processed EEG data was integrated into the 1DCNN-LSTMclassification model, where post-extensive learning, the model's weights were derived. The categorization system attained a 94.26% precision rate in the identification of the complete-channel EEG data collection. This research utilized the 1DCNN-LSTM algorithm to classify and identify high-risk and low-risk groups for schizophrenia, showcasing an adequate recognition capability that satisfies real-world application criteria. The system precisely categorizes populations at varying risk levels for schizophrenia through comprehensive EEG data, thus facilitating precise schizophrenia detection and offering prompt diagnosis and treatment for those at high risk for the condition.

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach

Among the major brain abnormalities that have been identified, various remedial strategies are proposed to tackle most of such conditions. One of the serious abnormalities of the nervous system is epilepsy, which causes electrical distraction and strains the neural system. Usually, epilepsy is determined by the neurologist by analyzing the EEG signals grabbed from the brain. The task is very challenging as it requires continuous examination and connotation of the EEG signal of an epileptic patient. Hence, the development of efficient automatic systems is currently a bottom neck issue, which can recognize the epileptic seizure attack and distinguish between normal condition and epileptic seizure condition. Over the years, efforts have been made to establish an automatic system for precise detection and classification of an epileptic seizure. In the present work, a range of statistical, entropy, and fractal-based attributes are calculated from the coefficients of selected wavelet. The classifiers used in the paper are trained on the selected features as well as full features. The objective of the study is the selection of appropriate features and applying Tree, SVM, KNN, BPANN algorithm for the classification. Features are ranked with the help of Information Gain, Relief F, and Correlation attributes ranking techniques. Using Information Gain and Relief F feature ranking technique with 10-fold cross-validation, the classification accuracy of 100% was achieved by Backpropagation Artificial Neural Network (BPANN) for classifying epileptic and normal EEG signals.

Automated detection of abnormal EEG signals using localized wavelet filter banks

EEG (Electroencephalograph) is a technique for identifying neurological disorders. Epilepsy is a disorder of the central nervous system characterized by loss of consciousness and convulsions. EEG is the recording of electrical activity of the brain signals that can be used to diagnose the epilepsy condition. Epilepsy occurs irregularly and unpredictably manner due to temporary electrical disturbance of the brain. The aim of this project is Epileptic seizure detection in multichannel EEG. In this paper, the normal, pre-ictal (background) and ictal (epileptic) EEG signals are studied using higher order spectra (HOS). This paper deals with the study of the normal, pre-ictal (background) and ictal (epileptic) EEG signals using Higher Order Spectra (HOS). The measures obtained were shown which distinguish epileptic EEG from normal and background EEG with high confident level.

Epilepsy is a neurological disorder that affects people of any age, which can be detected by Electroencephalogram (EEG) signals. This paper proposes a novel method called Volume of Phase Space Representation (VOPSR) to classify seizure and seizure-free EEG signals automatically. Primarily, the recorded EEG signal is disintegrated into several Intrinsic Mode Functions (IMFs) using the Empirical Mode Decomposition (EMD) method and the three-dimensional phase space have been reconstructed for the obtained IMFs. The volume is measured for the obtained 3D-PSR for different IMFs called VOPSR, which is used as a feature set for the classification of Epileptic seizure EEG signals. Support vector machine (SVM) is used as a classifier for the classification of epileptic and epileptic-free EEG signals. The classification performance of the proposed method is evaluated under different kernels such as Linear, Polynomial and Radial Basis Function (RBF) kernels. Finally, the proposed method outperforms noteworthy state-of-the-art classification methods in the context of epileptic EEG signals, achieving 99.13% accuracy (average) with the Linear, Polynomial, and RBF kernels. The proposed technique can be used to detect epilepsy from the EEG signals automatically without human intervention.

A new approach for automatic detection of focal EEG signals using wavelet packet decomposition and quad binary pattern method

The EEG signals prove to be an efficient tool in analyzing Epileptic seizure. The parameters like mean, standard deviation and their confidence interval of the seizure EEG signal are compared with the normal EEG signal. The seizure EEG signals have higher values of parameter estimates when compared to the normal EEG signals. In this paper Morlet wavelet transform is also performed on the EEG signals. Significant variations are observed in the Morlet coefficient of the seizure EEG when compared to the normal EEG signal.KeywordsEpileptic SeizureEEG signalsparameter estimationwavelet transform