Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable- Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.7

Seizure Detection Using Deep Learning, Information Theoretic Measures and Factor Graphs

Epilepsy is a common neurological disorder that disrupts normal electrical activity in the brain causing severe impact on patients’ daily lives. Accurate seizure detection based on long-term time-series electroencephalogram (EEG) signals has gained vital importance for epileptic seizure diagnosis. However, visual analysis of these recordings is a time-consuming task for neurologists. Therefore, the purpose of this thesis is to propose an automatic hybrid model-based /data-driven algorithm that exploits inter-channel and temporal correlations. Hence, we use mutual information (MI) estimator to compute correlation between EEG channels as spatial features and employ a carefully designed 1D convolutional neural network (CNN) to extract additional information from raw EEGs. Then, seizure probabilities from combined features of MI estimator and CNN are applied to factor graphs to learn factor nodes. The performance of the algorithm is evaluated through measuring different parameters as well as comparing with previous studies. On CHB-MIT dataset, our generalized algorithm achieves state-of-the-art performance

Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

Using Evolutionary Feature Selection Methods in Classification of EEG Signals

Elektroensefalografi beyindeki elektriksel akımın ölçülmesi ile elde edilen sinyallerdir. Bu sinyallerin sınıflandırılması özellikle beyin sinyalleri ile ilgili rahatsızlıkların teşhis, tanı ve tedavisine katkı sağladığı için önemlidir. Bu çalışmada bu alanda epilepsi hastalığının tanısı için en çok kullanılan veri kümesi olan Bonn Üniversitesi veri kümesi kullanılmıştır. Beş farklı denekten alınan sinyallerden oluşan bu veri kümesinden anlamlı sonuçlar elde edebilmek için öncelikle veri temizleme, öznitelik çıkarma ve öznitelik seçme yöntemleri kullanılmıştır. Daha sonra bu yöntemler sınıflandırma başarısına katkıları açısından kıyaslanmıştır. İlk olarak filtrelenen veriden Ayrık Dalgacık Dönüşümü metodu ile istatistiksel özellikler çıkarılmış, ardından Diferansiyel Evrim Algoritması kullanılarak en iyi sınıflandırma sonucunu veren öznitelik alt kümesi seçilmiştir. Seçilen özniteliklere sahip veri kümesinin sınıflandırma başarısı Destek Vektör Makineleri ile test edilmiştir. Kullanılan yöntem ile bazı sınıfların ayrılmasında literatürdeki benzer çalışmalardan daha iyi sonuçlar elde edilmiştir. Bazı ikili ve üçlü kümelerin sınıflandırılmasında sırasıyla 0,98 ve 0,94 doğruluk oranları elde edilmiştir.Electroencephalography signals are obtained by measuring the electrical current in the brain. The classification of these signals are especially important, as they contribute to the diagnosis, and treatment of disorders related to brain signals. In this study, the data set of the University of Bonn, which is the most widely used data set for the diagnosis of epilepsy, was used in this field. In order to obtain meaningful results from this data set consisting of signals from five different subjects, firstly, data filtering, feature extraction and feature selection methods have been used first. Later, these methods were then compared in terms of their contribution to classification success. First, statistical properties were extracted from the filtered data by the Discrete Wavelet Transform method, and then the subset of the features that gave the best classification result was selected using the Differential Evolution Algorithm. The classification success of the data set with the selected features has been tested with the Support Vector Machines. With the method used, better results were obtained than similar studies in separating some classes. In the classification of some double and triple sets, accuracy rates of 0.98 and 0.94, respectively, were obtained. Keywords: Electroencephalography signal analysis, Differential Evolution Algorithm, Feature Extraction,Feature Selection

Automated Epileptic Seizure Onset Detection

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state

Signal processing and analytics of multimodal biosignals

Ph. D. ThesisBiosignals have been extensively studied by researchers for applications in diagnosis, therapy, and monitoring. As these signals are complex, they have to be crafted as features for machine learning to work. This begs the question of how to extract features that are relevant and yet invariant to uncontrolled extraneous factors. In the last decade or so, deep learning has been used to extract features from the raw signals automatically. Furthermore, with the proliferation of sensors, more raw signals are now available, making it possible to use multi-view learning to improve on the predictive performance of deep learning. The purpose of this work is to develop an effective deep learning model of the biosignals and make use of the multi-view information in the sequential data. This thesis describes two proposed methods, namely: (1) The use of a deep temporal convolution network to provide the temporal context of the signals to the deeper layers of a deep belief net. (2) The use of multi-view spectral embedding to blend the complementary data in an ensemble. This work uses several annotated biosignal data sets that are available in the open domain. They are non-stationary, noisy and non-linear signals. Using these signals in their raw form without feature engineering will yield poor results with the traditional machine learning techniques. By passing abstractions that are more useful through the deep belief net and blending the complementary data in an ensemble, there will be improvement in performance in terms of accuracy and variance, as shown by the results of 10-fold validations.Nanyang Polytechni

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Feature binding of MPEG-7 Visual Descriptors Using Chaotic Series

Due to advanced segmentation and tracking algorithms, a video can be divided into numerous objects. Segmentation and tracking algorithms output different low-level object features, resulting in a high-dimensional feature vector per object. The challenge is to generate feature vector of objects which can be mapped to human understandable description, such as object labels, e.g., person, car. MPEG-7 provides visual descriptors to describe video contents. However, generally the MPEG-7 visual descriptors are highly redundant, and the feature coefficients in these descriptors need to be pre-processed for domain specific application. Ideal case would be if MPEG-7 visual descriptor based feature vector, can be processed similar to some functional simulations of human brain activity. There has been a established link between the analysis of temporal human brain oscillatory signals and chaotic dynamics from the electroencephalography (EEG) of the brain neurons. Neural signals in limited brain activities are found to be behaviorally relevant (previously appeared to be noise) and can be simulated using chaotic series. Chaotic series is referred to as either a finite-difference or an ordinary differential equation, which presents non-random, irregular fluctuations of parameter values over time in a dynamical system. The dynamics in a chaotic series can be high - or low -dimensional, and the dimensionality can be deduced from the topological dimension of the attractor of the chaotic series. An attractor is manifested by the tendency of a non-linear finite difference equation or an ordinary differential equation, under various but delimited conditions, to go to a reproducible active state, and stay there. We propose a feature binding method, using chaotic series, to generate a new feature vector, C-MP7 , to describe video objects. The proposed method considers MPEG-7 visual descriptor coefficients as dynamical systems. Dynamical systems are excited (similar to neuronal excitation) with either high- or low-dimensional chaotic series, and then histogram-based clustering is applied on the simulated chaotic series coefficients to generate C-MP7 . The proposed feature binding offers better feature vector with high-dimensional chaotic series simulation than with low-dimensional chaotic series, over MPEG-7 visual descriptor based feature vector. Diverse video objects are grouped in four generic classes (e.g., has [barbelow]person, has [barbelow]group [barbelow]of [barbelow]persons, has [barbelow]vehicle, and has [barbelow]unknown ) to observe how well C-MP7 describes different video objects compared to MPEG-7 feature vector. In C-MP7 , with high dimensional chaotic series simulation, 1). descriptor coefficients are reduced dynamically up to 37.05% compared to 10% in MPEG-7 , 2) higher variance is achieved than MPEG-7 , 3) multi-class discriminant analysis of C-MP7 with Fisher-criteria shows increased binary class separation for clustered video objects than that of MPEG-7 , and 4) C-MP7 , specifically provides good clustering of video objects for has [barbelow]vehicle class against other classes. To test C-MP7 in an application, we deploy a combination of multiple binary classifiers for video object classification. Related work on video object classification use non-MPEG-7 features. We specifically observe classification of challenging surveillance video objects, e.g., incomplete objects, partial occlusion, background over lapping, scale and resolution variant objects, indoor / outdoor lighting variations. C-MP7 is used to train different classes of video objects. Object classification accuracy is verified with both low-dimensional and high-dimensional chaotic series based feature binding for C-MP7 . Testing of diverse video objects with high-dimensional chaotic series simulation shows, 1) classification accuracy significantly improves on average, 83% compared to the 62% with MPEG-7 , 2) excellent clustering of vehicle objects leads to above 99% accuracy for only vehicles against all other objects, and 3) with diverse video objects, including objects from poor segmentation. C-MP7 is more robust as a feature vector in classification than MPEG-7 . Initial results on sub-group classification for male and female video objects in has [barbelow]person class are also presentated as subjective observations. Earlier, chaos series properties have been used in video processing applications for compression and digital watermarking. To our best knowledge, this work is the first to use chaotic series for video object description and apply it for object classificatio

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline