Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals

Detection of abnormalities in ECG using Deep Learning

A significant part of healthcare is focused on the information that the physiological signals offer about the health state of an individual. The Electrocardiogram (ECG) cyclic behaviour gives insight on a subject’s emotional, behavioral and cardiovascular state. These signals often present abnormal events that affects their analysis. Two examples are the noise, that occurs during the acquisition, and symptomatic patterns, that are produced by pathologies. This thesis proposes a Deep Neural Networks framework that learns the normal behaviour of an ECG while detecting abnormal events, tested in two different settings: detection of different types of noise, and; symptomatic events caused by different pathologies. Two algorithms were developed for noise detection, using an autoencoder and Convolutional Neural Networks (CNN), reaching accuracies of 98,18% for the binary class model and 70,74% for the multi-class model, which is able to discern between base wandering, muscle artifact and electrode motion noise. As for the arrhythmia detection algorithm was developed using an autoencoder and Recurrent Neural Networks with Gated Recurrent Units (GRU) architecture. With an accuracy of 56,85% and an average sensitivity of 61.13%, compared to an average sensitivity of 75.22% for a 12 class model developed by Hannun et al. The model detects 7 classes: normal sinus rhythm, paced rhythm, ventricular bigeminy, sinus bradycardia, atrial fibrillation, atrial flutter and pre-excitation. It was concluded that the process of learning the machine learned features of the normal ECG signal, currently sacrifices the accuracy for higher generalization. It performs better at discriminating the presence of abnormal events in ECG than classifying different types of events. In the future, these algorithms could represent a huge contribution in signal acquisition for wearables and the study of pathologies visible in not only in ECG, but also EMG and respiratory signals, especially applied to active learning

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

Permutation entropy and its main biomedical and econophysics applications: a review

Entropy is a powerful tool for the analysis of time series, as it allows describing the probability distributions of the possible state of a system, and therefore the information encoded in it. Nevertheless, important information may be codified also in the temporal dynamics, an aspect which is not usually taken into account. The idea of calculating entropy based on permutation patterns (that is, permutations defined by the order relations among values of a time series) has received a lot of attention in the last years, especially for the understanding of complex and chaotic systems. Permutation entropy directly accounts for the temporal information contained in the time series; furthermore, it has the quality of simplicity, robustness and very low computational cost. To celebrate the tenth anniversary of the original work, here we analyze the theoretical foundations of the permutation entropy, as well as the main recent applications to the analysis of economical markets and to the understanding of biomedical systems.Facultad de Ingenierí

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaOne of the biggest challenges when analysing data is to extract information from it, especially if we dealing with very large sized data, which brings a new set of barriers to be overcome. The extracted information can be used to aid physicians in their diagnosis since biosignals often carry vital information on the subjects. In this research work, we present a signal-independent algorithm with two main goals: perform events detection in biosignals and, with those events, extract information using a set of distance measures which will be used as input to a parallel version of the k-means clustering algorithm. The first goal is achieved by using two different approaches. Events can be found based on peaks detection through an adaptive threshold defined as the signal’s root mean square (RMS) or by morphological analysis through the computation of the signal’s meanwave. The final goal is achieved by dividing the distance measures into n parts and by performing k-means individually. In order to improve speed performance, parallel computing techniques were applied. For this study, a set of different types of signals was acquired and annotated by our algorithm. By visual inspection, the L1 and L2 Minkowski distances returned an output that allowed clustering signals’ cycles with an efficiency of 97:5% and 97:3%, respectively. Using the meanwave distance, our algorithm achieved an accuracy of 97:4%. For the downloaded ECGs from the Physionet databases, the developed algorithm detected 638 out of 644 manually annotated events provided by physicians. The fact that this algorithm can be applied to long-term raw biosignals and without requiring any prior information about them makes it an important contribution in biosignals’ information extraction and annotation

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Real-time vital signs monitoring system for livestock

The focus on the application of information and communication technologies & electronics (ICTE) in agriculture has proved to be very efficient and revolutionary in several ways. With the adoption of increasingly efficient and modern technologies, agriculture, in general, improves its competitiveness and production is carried out in a more sustainable way. The intensive use of ICTE in this sector has been aimed at creating integrated solutions that generate efficiency gains in productivity, sustainability and economic, social and environmental quality. This type of technologies, when aimed at monitoring livestock, have some characteristics in common. Precise positioning and geolocation from GPS, geographic mapping, sensors and communication systems are some of the tools that will allow the development of a complete and extremely accurate system for monitoring vital signs. This proposal for a cattle or equine ICTE-based monitoring system is developed as a belt. It contains a microcontroller that is used to evaluate the animal's heart rate and detect abnormal mobility. The correct evaluation of these two parameters proves very useful for the detection of many of the pathologies and anomalies that constitute economic losses for the producers. With accurate monitoring, it is possible to circumvent these events that are detrimental to animal production.O foco na aplicação de tecnologias de informação e comunicação e eletrónica (TICE) na agricultura provou ser muito eficiente e revolucionário de várias maneiras. Com a adoção de tecnologias cada vez mais eficientes e modernas, a agricultura em geral melhora sua competitividade e a produção é realizada de forma mais sustentável. O uso intensivo de TICE neste setor tem por objetivo criar soluções integradas que gerem ganhos efetivos em produtividade, sustentabilidade e qualidade económica, social e ambiental. Este tipo de tecnologias, quando destinadas à monitoração de gado, têm algumas características em comum. Posicionamento preciso e geolocalização de GPS, mapeamento geográfico, sensores e sistemas de comunicação são algumas das ferramentas que permitirão o desenvolvimento de um sistema completo e preciso para monitorar sinais vitais de animais. Esta proposta para um sistema de monitorização de gado ou equinos, baseado em TICE, é desenvolvido como um cinto. Este contém um dispositivo microcontrolador que é usado para avaliar a frequência cardíaca do animal e detetar mobilidade anormal. A avaliação correta desses dois parâmetros mostra-se muito útil para a deteção de muitas das patologias e anomalias que constituem perdas económicas para os produtores. Com uma monitorização precisa, é possível contornar estes eventos prejudiciais a uma produção animal

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved

Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..