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

Abnormal ECG Classification using Empirical Mode Decomposition and Entropy

Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis

Person authentication using electroencephalogram (EEG) brainwaves signals

This chapter starts with the introduction to various types of authentication modalities, before discussing on the implementation of electroencephalogram (EEG) signals for person authentication task in more details. In general, the EEG signals are unique but highly uncertain, noisy, and difficult to analyze. Event-related potentials, such as visual-evoked potentials, are commonly used in the person authentication literature work. The occipital area of the brain anatomy shows good response to the visual stimulus. Hence, a set of eight selected EEG channels located at the occipital area were used for model training. Besides, feature extraction methods, i.e., the WPD, Hjorth parameter, coherence, cross-correlation, mutual information, and mean of amplitude have been proven to be good in extracting relevant information from the EEG signals. Nevertheless, different features demonstrate varied performance on distinct subjects. Thus, the Correlation-based Feature Selection method was used to select the significant features subset to enhance the authentication performance. Finally, the Fuzzy-Rough Nearest Neighbor classifier was proposed for authentication model building. The experimental results showed that the proposed solution is able to discriminate imposter from target subjects in the person authentication task

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007.[Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.This work was partially supported by the Research Project RTC-2016-5143-1, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). Also, this work has received financial support from the ERDF and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION

Religious lectures are activities that are identical to the religious presentation, delivered verbally by a person who has religious knowledge and then delivered to the community with the aim of the knowledge delivered can be understood. Ustadz Abdul Somad was one of the preachers who had been known to various levels of society, but his lectures were not all acceptable to the people who liked or disliked those who came from various positive and negative comments on social media. To solve these problems, Sentiment Analysis was used by applying the Support Vector Machine Algorithm method. The purpose of this study is to compile using the selection of feature Particle Swarm Optimization and Information Gain. The results for Particle Swarm Optimization Selection Feature resulted in Accuracy of 80.57%, Precision of 85.45%, and Recall of 79.52%, Selection Feature Information Gain resulted in Accuracy of 79.78%, Precision of 78.47%, and Recall of 78, 43%, Based on the results of this study, it can be concluded that using the Particle Swarm Optimization selection feature is better at the level of accuracy when compared to using the Information Gain selection feature

Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64-100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (pvalue ≤ 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia

Desarrollo de una metodología para la caracterización y clasificación de señales no estacionarias usando mediciones de entropía de permutación multiescalar

Una serie de temporal es estacionaria si conserva sus descriptores constantes a lo largo del tiempo. Por lo cual, los modelos y análisis de series temporales clásicos basan sus modelos en procesos estocásticos que necesitan de manera indispensable que las series sean estacionarias. Sin embargo, la mayoría de los fenómenos de la vida real se caracterizan por dinámicas dependientes del tiempo, por lo cual no son estacionarias. Debido a esto, se han creado diferentes metodologías para el análisis efectivo de estas señales, ya que requieren métodos de representación apropiados capaces de describir con precisión la dinámica de la señal mientras evoluciona en el tiempo. Además, estudios anteriores han demostrado que los métodos de análisis clásicos de tiempo y frecuencia no pueden representar efectivamente las características de las señales, ya que los espectros de las de las mismas varían con el tiempo..

Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares

La Universidad Tecnológica de Pereira a través de la Vicerrectoría de Investigaciones, Innovación y Extensión tiene como propósito “Definir y direccionar los lineamientos para la investigación institucional que fortalezcan los grupos y semilleros de investigación, a través de la formación de investigadores, el desarrollo de programas o proyectos de ciencia, tecnología e innovación, así como la generación de redes y alianzas estratégicas que contribuyan a la creación y apropiación del conocimiento para la sociedad.” Y es por ello que, anualmente, entre otras se realiza la CONVOCATORIA PARA FOMENTAR LA PUBLICACIÓN DE CAPÍTULOS DE LIBRO RESULTADO DE INVESTIGACIÓN CATEDRÁTICOS AÑO 2022, en la cual pueden postular los resultados de los proyectos de investigación finalizados en los últimos cinco años. En esta oportunidad, se publicarán dos capítulos de las Facultades: Ciencias de la Educación y de Ingeniería Mecánica en los cuales se darán a conocer dos tesis de Maestría. Para la Vicerrectoría de Investigaciones, Innovación y Extensión es de suma importancia socializar por medio de este libro por capítulos el conocimiento, teniendo en cuenta que este debe transferirse a través de diferentes medios, puesto que no solo fortalece la academia sino también a la sociedad en general.CONTENIDO Introducción ..................................................................................................................5 Capitulo 1. Desarrollo de una metodología para la caracterización y clasificación de señales no estacionarias usando mediciones de Entropía de Permutación Multiescalar / Development of a methodology for the characterization and classification of non-stationary signals using Multiscalar Permutation Entropy measurements.................................9 Juan Camilo Mejía Hernández Capitulo 2. Reinaldo Arenas: escritura disidente y reescritura distópica / Reinaldo Arenas: dissident writing and dystopian rewrite......................................51 Diego Fernando Hernández Aria

Learning EEG Biometrics for Person Identification and Authentication

EEG provides appealing biometrics by presenting some unique attributes, not possessed by common biometric modalities like fingerprints, retina and face scan, in terms of robustness against forgery, secrecy and privacy compliance, aliveness detection and potential of continuous authentication. Meanwhile, the use of EEG to provide cognitive indicators for human workload, fatigue and emotions has created an environment where EEG is well-integrated into systems, making it readily available for biometrics purposes. Yet, still, many challenges need to be properly addressed before any actual deployment of EEG-based biometric systems in real-life scenarios: 1) subjects' inconvenience during the signal acquisition process, 2) the relatively low recognition rates, and 3) the lack of robustness against diverse human states. To address the aforementioned issues, this thesis is devoted to learn biometric traits from EEG signals for stable person identification and authentication. State of the art studies of EEG biometrics are mainly divided into two categories, the event-related potential (ERP) category, which relies on a tight control of the cognitive states of the subjects, and the ongoing EEG category, which uses continuous EEG signals (mainly in resting state) naturally produced by the brain without any particular sensory stimulation. Studies in the ERP category focus more on the design of proper signal elicitation protocols or paradigms which usually require repetitive sensory stimulation. Ongoing EEG, on the contrary, is more flexible in terms of signal acquisition, but needs more advanced computational methods for feature extraction and classification. This study focuses on EEG biometrics using ongoing signals in diverse states. Such a flexible system could lead to an effective deployment in the real world. Specifically, this work focuses on ongoing EEG signals under diverse human states without strict task-specific controls in terms of brain response elicitation during signal acquisition. This is in contrast to previous studies that rely on specific sensory stimulation and synthetic cognitive tasks to tightly control the cognitive state of the subject being reflected in the resulting EEG activity, or to use resting state EEG signals. The relaxation of the reliance of the user's cognitive state makes the signal acquisition process streamlined, which in turn facilitates the actual deployment of the EEG biometrics system. Furthermore, not relying on sensory stimulation and cognitive tasks also allows for flexible and unobtrusive biometric systems that work in the background without interrupting the users, which is especially important in continuous scenarios. However, relaxing the system's reliance on the human state also means losing control of the EEG activity produced. As a result, EEG signals captured from the scalp may be contaminated by the active involvement of the tasks and cognitive states such as workload and emotion. Therefore, it becomes a challenge to learn identity-bearing information from the complicated signals to support high stability EEG biometrics. Possible solutions are proposed and investigated from two main perspectives, feature extraction and pattern classification. Specifically, graph features and learning models are proposed based on the brain connectivity, graph theory, and deep learning algorithms. A comprehensive investigation is conducted to assess the performance of proposed methods and existing methods in biometric identification and authentication, including in continuous scenarios. The methods and experiments are reported and detailed in the corresponding chapters, with the results obtained from data analysis