Técnicas de controlo não-linear baseadas em redes neuronais - do algoritmo à implementação

O presente trabalho analisa soluções de controlo não-linear baseadas em Redes Neuronais e apresenta a sua aplicação a um caso prático, desde o algoritmo de treino até à implementação física em hardware. O estudo inicial do estado da arte da utilização das Redes Neuronais para o controlo leva à proposta de soluções iterativas para a definição da arquitectura das mesmas e para o estudo das técnicas de Regularização e Paragem de Treino Antecipada, através dos Algoritmos Genéticos e à proposta de uma forma de validação dos modelos obtidos. Ao longo da tese são utilizadas quatro malhas para o controlo baseado em modelos, uma das quais uma contribuição original, e é implementado um processo de identificação on-line, tendo por base o algoritmo de treino Levenberg-Marquardt e a técnica de Paragem de Treino Antecipada que permite o controlo de um sistema, sem necessidade de recorrer ao conhecimento prévio das suas características. O trabalho é finalizado com um estudo do hardware comercial disponível para a implementação de Redes Neuronais e com o desenvolvimento de uma solução de hardware utilizando uma FPGA. De referir que o trabalho prático de teste das soluções apresentadas é realizado com dados reais provenientes de um forno eléctrico de escala reduzida.The present work analyses non-linear control solutions based on Neural Networks and presents its application to a case study, from the training algorithm to the hardware implementation. The initial study of the state of the art of Neural Networks use in control led to a proposal of iterative solutions for architecture definition and establishment of the Regularization and Early Stopping parameters, through the use of Genetic Algorithms and to the proposal of a new validation technique for the models. Throughout this thesis, four different loops for model based control are used, one of which is an original contribution, and an on-line identification procedure, based on the Levenberg-Marquardt algorithm with Early Stopping that allows system identification without previous knowledge of its characteristics. The work is finalized with a commercial hardware study for Neural Networks and with the development of a hardware solution based on a FPGA. It is worth mentioning that proposed solutions are tested with real data provided by a reduced scale electric kiln

A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.info:eu-repo/semantics/publishedVersio

A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.info:eu-repo/semantics/publishedVersio

A Review of Approaches for Sleep Quality Analysis

Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term ‘‘sleep quality’’ still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject’s home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, ‘‘what methods for sleep quality assessment have been developed’’ and ‘‘what kind of measures are employed by the devices that have been developed to estimate sleep quality.’’ The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art.info:eu-repo/semantics/publishedVersio

Automatic Detection of a Phases for CAP Classification

The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then applying a finite state machine to implement the final classification. A public database was used to test the algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select the most relevant features and a post processing procedure was used for further improvement of the classification. The classification of the A phases was produced using linear discriminant analysis and the average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM periods, contrary to the method that is used in the majority of the state of the art publications which leads to an increase in the overall performance. However, the approach of this work is more suitable for automatic system implementation since no alteration of the EEG data is needed.info:eu-repo/semantics/publishedVersio

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.info:eu-repo/semantics/publishedVersio

Kinetics and thermodynamics of poly (9,9-dioctylfluorene) beta-phase formation in dilute solution

Poly(9,9-dioctylfluorene) (PFO) adopts a particular type of conformation in dilute solutions of the poor solvent methylcyclohexane (MCH) below 273 K, which is revealed by the appearance of a red-shifted absorption peak at 437−438 nm. The formation of this ordered conformation depends on the temperature but is independent of polymer concentration over the range studied (3−25 μg/mL). On the basis of absorption, steady-state, and time-resolved fluorescence data, the new absorption peak at 437−438 nm is assigned to a highly ordered conformation of PFO chains, analogous to the so-called β-phase first identified in PFO films. From the study of PFO solutions in MCH as a function of temperature, we conclude that these ordered segments (β-conformation) coexist with less ordered domains in the same chain. When the ordered domains are present, they act as efficient energy traps and the fluorescence from the disordered regions is quenched. The transition between the disordered and the ordered PFO conformations is adequately described by a mechanism that involves two steps:  a first, essentially intramolecular, one from a relatively disordered (α) to an ordered conformation (β), followed by aggregation of chains containing β-conformation into anisotropic ordered domains. From the temperature dependence of the 437−438 nm peak intensity, the transition temperature Tβ = 261 K, enthalpy ΔHβ = −18.0 kcal mol-1, and entropy ΔSβ = −68.4 cal K-1 mol-1 were obtained. The formation of the β-conformation domains were also followed as a function of time at 260 K. The rate constants at 260 K were determined, showing an order of magnitude around 10-3 s-1 (kα→β = 5.9 × 10-4 s-1; kβ→α = 9 × 10-4 s-1; kagg = 2.3 × 10-3 M-1 s-1; kdiss = 4.4 × 10-4 s-1). This small magnitude explains the long times required for a “complete” conversion to the β-conformation

Towards automatic EEG cyclic alternating pattern analysis: a systematic review

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical applica tion? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmationinfo:eu-repo/semantics/publishedVersio

A Matlab Tool for Analyzing and Improving Fault Tolerance of Artificial Neural Networks

Abstract: FTSET is a software tool that deals with fault tolerance of Artificial Neural Networks. This tool is capable of evaluating the fault tolerance degree of a previously trained Artificial Neural Network given its inputs ranges, the weights and the architecture. The FTSET is also capable of improving the fault tolerance by applying a technique of splitting the connections of the network that are more important to form the output. This technique improves fault tolerance without changing the network's output. The paper is concluded by two examples that show the application of the FTSET to different Artificial Neural Networks and the improvement of the fault tolerance obtained

A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device

The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.info:eu-repo/semantics/publishedVersio