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

Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform

Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological information about the activity of the human brain which can be used to diagnose epilepsy. However, visual inspection of a large number of electroencephalogram signals is very time-consuming and can often lead to inconsistencies in physicians' diagnoses. Quantification of abnormalities in brain signals can indicate brain conditions and pathology so the electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. In this article, an attempt has been made to create a single instruction for diagnosing epilepsy, which consists of two steps. In the first step, a low-pass filter was used to preprocess the data and three separate mid-pass filters for different frequency bands and a multilayer neural network were designed. In the second step, the wavelet transform technique was used to process data. In particular, this paper proposes a multilayer perceptron neural network classifier for the diagnosis of epilepsy, that requires normal data and epilepsy data for education, but this classifier can recognize normal disorders, epilepsy, and even other disorders taught in educational examples. Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform. Finally, the evaluation results indicate a relatively uniform impact factor on the use or non-use of wavelet transform on the improvement of epilepsy data functions, but in the end, it was shown that the use of perceptron multilayer neural network can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure

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

Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset

Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons\u27 abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus, automatically classifying epilepsy into different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. This PhD thesis contributes to the epileptic seizure detection problem using Machine Learning (ML) techniques. Machine learning algorithms have been implemented to automatically classifying epilepsy from EEG data. Imbalance class distribution problems and effective feature extraction from the EEG signals are the two major concerns towards effectively and efficiently applying machine learning algorithms for epilepsy classification. The algorithms exhibit biased results towards the majority class when classes are imbalanced, while effective feature extraction can improve classification performance. In this thesis, we presented three different novel frameworks to effectively classify epileptic states while addressing the above issues. Firstly, a deep neural network-based framework exploring different sampling techniques was proposed where both traditional and state-of-the-art sampling techniques were experimented with and evaluated for their capability of improving the imbalance ratio and classification performance. Secondly, a novel integrated machine learning-based framework was proposed to effectively learn from EEG imbalanced data leveraging the Principal Component Analysis method to extract high- and low-variant principal components, which are empirically customized for the imbalanced data classification. This study showed that principal components associated with low variances can capture implicit patterns of the minority class of a dataset. Next, we proposed a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different machine learning classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification. Finally, we applied traditional machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors along with Deep Neural Networks to classify epilepsy. We experimented the frameworks with a benchmark dataset through rigorous experimental settings and displayed the effectiveness of the proposed frameworks in terms of accuracy, precision, recall, and F-beta score

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population

Background: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. Method: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient’s embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. Results: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. Conclusion: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient’s embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide