127 research outputs found
Epileptic seizure prediction using machine learning techniques
Epileptic seizures affect about 1% of the world’s population, thus making it the fourth most
common neurological disease, this disease is considered a neurological disorder characterized by the abnormal activity of the brain.
Part of the population suffering from this disease is unable to avail themselves of any
treatment, as this treatment has no beneficial effect on the patient.
One of the main concerns associated with this disease is the damage caused by uncontrollable seizures. This damage affects not only the patient himself but also the people around
him. With this situation in mind, the goal of this thesis is, through methods of Machine
Learning, to create an algorithm that can predict epileptic seizures before they occur.
To predict these seizures, the electroencephalogram (EEG) will be employed, since it is the
most commonly used method for diagnosing epilepsy. Of the total 23 channels available,
only 8 will be used, due to their location.
When a seizure occurs, besides the visible changes in the EEG signal, at the moment of
the seizure, the alterations before and after the epileptic seizure are also noticeable. These
stages have been named in the literature:
• Preictal: the moment before the epileptic seizure;
• Ictal: the moment of the seizure;
• Postictal: the moment after the seizure;
• Interictal: space of time between seizures.
The goal of the predictive algorithm will be to classify the different classes and study different classification problems by using supervised learning techniques, more precisely a
classifier. By performing this classification when indications are detected that a possible
epileptic seizure will occur, the patient will then be warned so that he can prepare for the
seizure.Crises epiléticas afetam cerca de 1% da população mundial, tornando-a assim a quarta
doença neurológica mais comum. Esta é considerada uma doença caracterizada pela atividade anormal do cérebro.
Parte da população que sofre desta condição não consegue recorrer a qualquer tratamento,
pois este não apresenta qualquer efeito benéfico no paciente.
Uma das principais preocupações associadas com este problema são os danos causados
pelas convulsões imprevisíveis. Estes danos não afetam somente o próprio paciente, como
também as pessoas que o rodeiam. Com esta situação em mente, o objetivo desta dissertação consiste em, através de métodos de Machine Learning, criar um algoritmo capaz de
prever as crises epiléticas antes da sua ocorrência.
Para proceder à previsão destas convulsões, será utilizado o eletroencefalograma (EEG),
uma vez que é o método mais usado para o diagnóstico de epilepsia. Serão utilizados
apenas 8 dos 23 canais disponíveis, devido à sua localização.
Quando ocorre uma crise, além das alterações visíveis no sinal EEG, não só no momento
da crise, são também notáveis alterações antes e após a convulsão. A estas fases a literatura nomeou:
• Pre-ictal: momento anterior à crise epilética;
• Ictal: momento da convulsão;
• Pós-ictal: momento posterior à crise;
• Interictal: espaço de tempo entre convulsões.
O objetivo do algoritmo preditivo será fazer a classificação das diferentes classes e o estudo
de diferentes problemas de classificação, através do uso de técnicas de machine learning,
mais precisamente um classificador. Ao realizar esta classificação, quando forem detetados indícios de que uma possível crise epilética irá ocorrer, o paciente será então avisado,
podendo assim preparar-se para esta
Fractional Order Fault Tolerant Control - A Survey
In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies
Nonlinear Systems
Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification
Electrocardiogram (ECG) signals, which capture the heart's electrical
activity, are used to diagnose and monitor cardiac problems. The accurate
classification of ECG signals, particularly for distinguishing among various
types of arrhythmias and myocardial infarctions, is crucial for the early
detection and treatment of heart-related diseases. This paper proposes a novel
approach based on an improved differential evolution (DE) algorithm for ECG
signal classification for enhancing the performance. In the initial stages of
our approach, the preprocessing step is followed by the extraction of several
significant features from the ECG signals. These extracted features are then
provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are
still widely used for ECG signal classification, using gradient-based training
methods, the most widely used algorithm for the training process, has
significant disadvantages, such as the possibility of being stuck in local
optimums. This paper employs an enhanced differential evolution (DE) algorithm
for the training process as one of the most effective population-based
algorithms. To this end, we improved DE based on a clustering-based strategy,
opposition-based learning, and a local search. Clustering-based strategies can
act as crossover operators, while the goal of the opposition operator is to
improve the exploration of the DE algorithm. The weights and biases found by
the improved DE algorithm are then fed into six gradient-based local search
algorithms. In other words, the weights found by the DE are employed as an
initialization point. Therefore, we introduced six different algorithms for the
training process (in terms of different local search algorithms). In an
extensive set of experiments, we showed that our proposed training algorithm
could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure
Machine Learning-Based Data and Model Driven Bayesian Uncertanity Quantification of Inverse Problems for Suspended Non-structural System
Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and control strategies based on simulation or prediction results. However, in the surrogate model, preventing overfitting and incorporating reasonable prior knowledge of embedded physics and models is a challenge. Suspended Nonstructural Systems (SNS) pose a significant challenge in the inverse problem. Research on their seismic performance and mechanical models, particularly in the inverse problem and uncertainty quantification, is still lacking. To address this, the author conducts full-scale shaking table dynamic experiments and monotonic & cyclic tests, and simulations of different types of SNS to investigate mechanical behaviors. To quantify the uncertainty of the inverse problem, the author proposes a new framework that adopts machine learning-based data and model driven stochastic Gaussian process model calibration to quantify the uncertainty via a new black box variational inference that accounts for geometric complexity measure, Minimum Description length (MDL), through Bayesian inference. It is validated in the SNS and yields optimal generalizability and computational scalability
- …