Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features

Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient\u27s EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system. The first step is a detection problem to find the Epileptiform like transients (ETs) from the Electroencephalograph (EEG) of a patient. A Radial basis function-based neural network has been trained using a training set consisting of examples from both classes (ETs and non-ETs). The ETs are the yellow boxes which are marked by expert neurologists. There are no particular examples of non-ETs and any data not annotated by experts can be considered to be examples of non-ETs. The second step is classification of the detected ETs also known as yellow boxes, into AEPs or non-AEPs. A similar Radial basis function-based neural network has been trained using the ETs marked and classified into AEPs and non-AEPs manually by seven expert neurologists. The annotations or yellow boxes along with the contextual signal was used to extract features using the Hilbert Huang Transform. The system is validated by considering an entire epoch of the patient EEG and potential ETs are identified using the detector. The potential ETs marked by the detector are classified into AEPs and non-AEPs and compared against the annotations marked by the experts

Localization of the epileptogenic foci using Support Vector Machine

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG signals. We used intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. We have been investigating a localization of the focus region at the epileptic seizure based on SVM to detect the onset of seizure activity in EEG data. The SVM is trained on sets of intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. The performance of SVM is measured by using accuracy obtained from a fit between the target value and network output. Our EEG based localization of the focus region at the epileptic seizure approach achieves 97.4% accuracy with using 10-fold cross validation. Therefore, our method can be successfully applied to localization of the epileptogenic foci

MULTI-FEATURE ANALYSIS OF EEG SIGNAL ON SEIZURE PATTERNS AND DEEP NEURAL STRUCTURES FOR PREDICTION OF EPILEPTIC SEIZURES

This work investigates EEG signal processing and seizure prediction based on deep learning architectures. The research includes two major parts. In the first part, we use wavelet decomposition to process the signals and extract signal features from the time-frequency bands. The second part examines the machine learning model and deep learning architecture we have developed for seizure pattern analysis. In our design, the extracted feature maps are processed as image inputs into our convolutional neural network (CNN) model. We proposed a combined CNN-LSTM model to directly process the EEG signals with layers functioning as feature extractors. In cross-validation testing, our CNN feature model can reach an accuracy of 96% and our CNN-LSTM model could reach an accuracy of 98%. We also proposed a matching network architecture that employs two parallel multilayer channels to improve sensitivity

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

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

Identification and Use of PSD-Derived Features for the Contextual Detection and Classification of EEG Epileptiform Transients

Epilepsy is a chronic disorder, which is characterized by seizures. For diagnosis, trained neu-rologists go over the patient’s EEG (Electroencephalograph) records looking for epileptic transients. This is a tedious and long process. The objective of this thesis is to automate the procedure by developing a detector that would pick out epileptic transients containing the ”Abnormal Epileptiform Paroxysmal” (AEP) type. The process was split into detection of potential AEPs and the classification of the detected segments. The detection of potential AEPs (called Yellow Boxing) passed boxed segments of the EEG signal to be classified as to segments that contain paroxysmal activity or not. For yellow boxing potential AEPs, a neural network was trained to determine if the signal contained in a sliding window was to be yellow boxed or not. If yellow boxed, the yellow box was then classified using a neural network trained to handle the classification problem. The networks were trained based on yellow boxes (potential AEPs) marked by trained neurologists. The resulting performance of the networks was studied using sensitivity, specificity and precision as parameters. The overall performance of the detector was verified with respect to expert marked AEPs. An additional parameter, based on the detected AEP length, was also introduced for detection to overcome the drawbacks found in using specificity