Classification of Epileptic EEG Signals by Wavelet based CFC

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods

Development of electroencephalogram (EEG) signals classification techniques

Electroencephalography (EEG) is one of the most important signals recorded from humans. It can assist scientists and experts to understand the most complex part of the human body, the brain. Thus, analysing EEG signals is the most preponderant process to the problem of extracting significant information from brain dynamics. It plays a prominent role in brain studies. The EEG data are very important for diagnosing a variety of brain disorders, such as epilepsy, sleep problems, and also assisting disability patients to interact with their environment through brain computer interface (BCI). However, the EEG signals contain a huge amount of information about the brain’s activities. But the analysis and classification of these kinds of signals is still restricted. In addition, the manual examination of these signals for diagnosing related diseases is time consuming and sometimes does not work accurately. Several studies have attempted to develop different analysis and classification techniques to categorise the EEG recordings. The analysis of EEG recordings can lead to a better understanding of the cognitive process. It is used to extract the important features and reduce the dimensions of EEG data. In the classification process, machine learning algorithms are used to detect the particular class of EEG signal based on its extracted features. The performance of these algorithms, in which the class membership of the input signal is determined, can then be used to infer what event in the real-world process occurred to produce the input signal. The classification procedure has the potential to assist experts to diagnose the related brain disorders. To evaluate and diagnose neurological disorders properly, it is necessary to develop new automatic classification techniques. These techniques will help to classify different EEG signals and determine whether a person is in a good health or not. This project aims to develop new techniques to enhance the analysis and classification of different categories of EEG data. A simple random sampling (SRS) and sequential feature selection (SFS) method was developed and named the SRS_SFS method. In this method, firstly, a SRS technique was used to extract statistical features from the original EEG data in time domain. The extracted features were used as the input to a SFS algorithm for key features selection. A least square support vector machine (LS_SVM) method was then applied for EEG signals classification to evaluate the performance of the proposed approach. Secondly, a novel approach that combines optimum allocation (OA) and spectral density estimation methods was proposed to analyse EEG signals and classify an epileptic seizure. In this study, the OA technique was introduced in two levels to determine representative sample points from the EEG recordings. To reduce the dimensions of sample points and extract representative features from each OA sample segment, two power spectral density estimation methods, periodogram and autoregressive, were used. At the end, three popular machine learning methods (support vector machine (SVM), quadratic discriminant analysis, and k-nearest neighbor (k-NN)) were employed to evaluate the performance of the suggested algorithm. Additionally, a Tunable Q-factor wavelet transform (TQWT) based algorithm was developed for epileptic EEG feature extraction. The extracted features were forwarded to the bagging tree, k-NN, and SVM as classifiers to evaluate the performance of the proposed feature extraction technique. The proposed TQWT method was tested on two different EEG databases. Finally, a new classification system was presented for epileptic seizures detection in EEGs blending frequency domain with information gain (InfoGain) technique. Fast Fourier transform (FFT) or discrete wavelet transform (DWT) were applied individually to analyse EEG recording signals into frequency bands for feature extraction. To select the most important feature, the infoGain technique was employed. A LS_SVM classifier was used to evaluate the performance of this system. The research indicates that the proposed techniques are very practical and effective for classifying epileptic EEG disorders and can assist to present the most important clinical information about patients with brain disorders

Developing artificial intelligence models for classification of brain disorder diseases based on statistical techniques

The Abstract is currently unavailable, due to the thesis being under Embargo

Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression

Seizures are a common symptom of this neurological condition, which is caused by the discharge of brain nerve cells at an excessively fast rate. Chaos, nonlinearity, and other nonlinearities are common features of scalp and intracranial Electroencephalogram (EEG) data recorded in clinics. EEG signals that aren't immediately evident are challenging to categories because of their complexity. The Gradient Boost Decision Tree (GBDT) classifier was used to classify the majority of the EEG signal segments automatically. According to this study, the Hurst exponent, in combination with AFA, is an efficient way to identify epileptic signals. As with any fractal analysis approach, there are problems and factors to keep in mind, such as identifying whether or not linear scaling areas are present. These signals were classified as either epileptic or non-epileptic by using a combination of GBDT and a Support Vector Regression (SVR). The combined method's identification accuracy was 98.23%. This study sheds light on the effectiveness of AFA feature extraction and GBDT classifiers in EEG classification. The findings can be utilized to develop theoretical guidance for the clinical identification and prediction of epileptic EEG signals. Doi: 10.28991/ESJ-2022-06-01-011 Full Text: PD

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results

Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms

This dissertation presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive fuzzy logic system was developed to detect seizure onset in a patient specific way. Fuzzy if-then rules were developed to mimic the human reasoning and taking advantage of the combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of several commonly arising artifacts from EEG. Finally, we present a neuro-fuzzy approach of seizure prediction by applying the ANFIS. Patient specific ANFIS classifier was constructed to forecast a seizure followed by postprocessing methods. Three nonlinear seizure predictive features were used to characterize changes prior to seizure. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. The ANFIS classifier was constructed based on these features as inputs. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. In this dissertation, the application of the neuro-fuzzy algorithms in epilepsy diagnosis and treatment was demonstrated by applying the methods on different datasets. Several performance measures such as detection delay, sensitivity and specificity were calculated and compared with results reported in literature. The proposed algorithms have potentials to be used in diagnostics and therapeutic applications as they can be implemented in an implantable medical device to detect a seizure, forecast a seizure, and initiate neurostimulation therapy for the purpose of seizure prevention or abortion

Developing new techniques to analyse and classify EEG signals

A massive amount of biomedical time series data such as Electroencephalograph (EEG), electrocardiography (ECG), Electromyography (EMG) signals are recorded daily to monitor human performance and diagnose different brain diseases. Effectively and accurately analysing these biomedical records is considered a challenge for researchers. Developing new techniques to analyse and classify these signals can help manage, inspect and diagnose these signals. In this thesis novel methods are proposed for EEG signals classification and analysis based on complex networks, a statistical model and spectral graph wavelet transform. Different complex networks attributes were employed and studied in this thesis to investigate the main relationship between behaviours of EEG signals and changes in networks attributes. Three types of EEG signals were investigated and analysed; sleep stages, epileptic and anaesthesia. The obtained results demonstrated the effectiveness of the proposed methods for analysing these three EEG signals types. The methods developed were applied to score sleep stages EEG signals, and to analyse epileptic, as well as anaesthesia EEG signals. The outcomes of the project will help support experts in the relevant medical fields and decrease the cost of diagnosing brain diseases