Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithm for Chest X-Ray Images Classification: COVID-19 as a Case Study

Covid-19 virus has led to a tremendous pandemic in more than 200 countries across the globe, leading to severe impacts on the lives and health of a large number of people globally. The emergence of Omicron (SARS-CoV-2), which is a coronavirus 2 variant, an acute respiratory syndrome which is highly mutated, has again caused social limitations around the world because of infectious and vaccine escape mutations. One of the most significant steps in the fight against covid-19 is to identify those who were infected with the virus as early as possible, to start their treatment and to minimize the risk of transmission. Detection of this disease from radiographic and radiological images is perhaps one of the quickest and most accessible methods of diagnosing patients. In this study, a computer aided system based on deep learning is proposed for rapid diagnosis of COVID-19 from chest x-ray images. First, a dataset of 5380 Chest x-ray images was collected from publicly available datasets. In the first step, the deep features of the images in the dataset are extracted by using the dataset pre-trained convolutional neural network (CNN) model. In the second step, Differential Evolution (DE), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) algorithms were used for feature selection in order to find the features that are effective for classification of these deep features. Finally, the features obtained in two stages, Decision Tree (DT), Naive Bayes (NB), support vector machine (SVM), k-Nearest Neighbours (k-NN) and Neural Network (NN) classifiers are used for binary, triple and quadruple classification. In order to measure the success of the models objectively, 10 folds cross validation was used. As a result, 1000 features were extracted with the SqueezeNet CNN model. In the binary, triple and quadruple classification process using these features, the SVM method was found to be the best classifier. The classification successes of the SVM model are 96.02%, 86.84% and 79.87%, respectively. The results obtained from the classification process with deep feature extraction were achieved by selecting the features in the proposed method in less time and with less features. While the performance achieved is very good, further analysis is required on a larger set of COVID-19 images to obtain higher estimates of accuracy

Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT

Die Entwicklung von Synchrotronlichtquellen der dritten Generation hat die Grundlage für die Untersuchung der 3D-Struktur opaker Proben mit einer Auflösung im Mikrometerbereich und höher geschaffen. Dies führte zur Entwicklung der Röntgen-Synchrotron-Mikro-Computertomographie, welche die Schaffung von Bildgebungseinrichtungen zur Untersuchung von Proben verschiedenster Art förderte, z.B. von Modellorganismen, um die Physiologie komplexer lebender Systeme besser zu verstehen. Die Entwicklung moderner Steuerungssysteme und Robotik ermöglichte die vollständige Automatisierung der Röntgenbildgebungsexperimente und die Kalibrierung der Parameter des Versuchsaufbaus während des Betriebs. Die Weiterentwicklung der digitalen Detektorsysteme führte zu Verbesserungen der Auflösung, des Dynamikbereichs, der Empfindlichkeit und anderer wesentlicher Eigenschaften. Diese Verbesserungen führten zu einer beträchtlichen Steigerung des Durchsatzes des Bildgebungsprozesses, aber auf der anderen Seite begannen die Experimente eine wesentlich größere Datenmenge von bis zu Dutzenden von Terabyte zu generieren, welche anschließend manuell verarbeitet wurden. Somit ebneten diese technischen Fortschritte den Weg für die Durchführung effizienterer Hochdurchsatzexperimente zur Untersuchung einer großen Anzahl von Proben, welche Datensätze von besserer Qualität produzierten. In der wissenschaftlichen Gemeinschaft besteht daher ein hoher Bedarf an einem effizienten, automatisierten Workflow für die Röntgendatenanalyse, welcher eine solche Datenlast bewältigen und wertvolle Erkenntnisse für die Fachexperten liefern kann. Die bestehenden Lösungen für einen solchen Workflow sind nicht direkt auf Hochdurchsatzexperimente anwendbar, da sie für Ad-hoc-Szenarien im Bereich der medizinischen Bildgebung entwickelt wurden. Daher sind sie nicht für Hochdurchsatzdatenströme optimiert und auch nicht in der Lage, die hierarchische Beschaffenheit von Proben zu nutzen. Die wichtigsten Beiträge der vorliegenden Arbeit sind ein neuer automatisierter Analyse-Workflow, der für die effiziente Verarbeitung heterogener Röntgendatensätze hierarchischer Natur geeignet ist. Der entwickelte Workflow basiert auf verbesserten Methoden zur Datenvorverarbeitung, Registrierung, Lokalisierung und Segmentierung. Jede Phase eines Arbeitsablaufs, die eine Trainingsphase beinhaltet, kann automatisch feinabgestimmt werden, um die besten Hyperparameter für den spezifischen Datensatz zu finden. Für die Analyse von Faserstrukturen in Proben wurde eine neue, hochgradig parallelisierbare 3D-Orientierungsanalysemethode entwickelt, die auf einem neuartigen Konzept der emittierenden Strahlen basiert und eine präzisere morphologische Analyse ermöglicht. Alle entwickelten Methoden wurden gründlich an synthetischen Datensätzen validiert, um ihre Anwendbarkeit unter verschiedenen Abbildungsbedingungen quantitativ zu bewerten. Es wurde gezeigt, dass der Workflow in der Lage ist, eine Reihe von Datensätzen ähnlicher Art zu verarbeiten. Darüber hinaus werden die effizienten CPU/GPU-Implementierungen des entwickelten Workflows und der Methoden vorgestellt und der Gemeinschaft als Module für die Sprache Python zur Verfügung gestellt. Der entwickelte automatisierte Analyse-Workflow wurde erfolgreich für Mikro-CT-Datensätze angewandt, die in Hochdurchsatzröntgenexperimenten im Bereich der Entwicklungsbiologie und Materialwissenschaft gewonnen wurden. Insbesondere wurde dieser Arbeitsablauf für die Analyse der Medaka-Fisch-Datensätze angewandt, was eine automatisierte Segmentierung und anschließende morphologische Analyse von Gehirn, Leber, Kopfnephronen und Herz ermöglichte. Darüber hinaus wurde die entwickelte Methode der 3D-Orientierungsanalyse bei der morphologischen Analyse von Polymergerüst-Datensätzen eingesetzt, um einen Herstellungsprozess in Richtung wünschenswerter Eigenschaften zu lenken

Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future

Clinical microbiology with multi-view deep probabilistic models

Clinical microbiology is one of the critical topics of this century. Identification and discrimination of microorganisms is considered a global public health threat by the main international health organisations, such as World Health Organisation (WHO) or the European Centre for Disease Prevention and Control (ECDC). Rapid spread, high morbidity and mortality, as well as the economic burden associated with their treatment and control are the main causes of their impact. Discrimination of microorganisms is crucial for clinical applications, for instance, Clostridium difficile (C. diff ) increases the mortality and morbidity of healthcare-related infections. Furthermore, in the past two decades, other bacteria, including Klebsiella pneumoniae (K. pneumonia), have demonstrated a significant propensity to acquire antibiotic resistance mechanisms. Consequently, the use of an ineffective antibiotic may result in mortality. Machine Learning (ML) has the potential to be applied in the clinical microbiology field to automatise current methodologies and provide more efficient guided personalised treatments. However, microbiological data are challenging to exploit owing to the presence of a heterogeneous mix of data types, such as real-valued high-dimensional data, categorical indicators, multilabel epidemiological data, binary targets, or even time-series data representations. This problem, which in the field of ML is known as multi-view or multi-modal representation learning, has been studied in other application fields such as mental health monitoring or haematology. Multi-view learning combines different modalities or views representing the same data to extract richer insights and improve understanding. Each modality or view corresponds to a distinct encoding mechanism for the data, and this dissertation specifically addresses the issue of heterogeneity across multiple views. In the probabilistic ML field, the exploitation of multi-view learning is also known as Bayesian Factor Analysis (FA). Current solutions face limitations when handling high-dimensional data and non-linear associations. Recent research proposes deep probabilistic methods to learn hierarchical representations of the data, which can capture intricate non-linear relationships between features. However, some Deep Learning (DL) techniques rely on complicated representations, which can hinder the interpretation of the outcomes. In addition, some inference methods used in DL approaches can be computationally burdensome, which can hinder their practical application in real-world situations. Therefore, there is a demand for more interpretable, explainable, and computationally efficient techniques for highdimensional data. By combining multiple views representing the same information, such as genomic, proteomic, and epidemiologic data, multi-modal representation learning could provide a better understanding of the microbial world. Hence, in this dissertation, the development of two deep probabilistic models, that can handle current limitations in state-of-the-art of clinical microbiology, are proposed. Moreover, both models are also tested in two real scenarios regarding antibiotic resistance prediction in K. pneumoniae and automatic ribotyping of C. diff in collaboration with the Instituto de Investigación Sanitaria Gregorio Marañón (IISGM) and the Instituto Ramón y Cajal de Investigación Sanitaria (IRyCIS). The first presented algorithm is the Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). This algorithm uses a kernelised formulation to handle non-linear data relationships while providing compact representations through the automatic selection of relevant vectors. Additionally, it uses an Automatic Relevance Determination (ARD) over the kernel to determine the input feature relevance functionality. Then, it is tailored and applied to the microbiological laboratories of the IISGM and IRyCIS to predict antibiotic resistance in K. pneumoniae. To do so, specific kernels that handle Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) mass spectrometry of bacteria are used. Moreover, by exploiting the multi-modal learning between the spectra and epidemiological information, it outperforms other state-of-the-art algorithms. Presented results demonstrate the importance of heterogeneous models that can analyse epidemiological information and can automatically be adjusted for different data distributions. The implementation of this method in microbiological laboratories could significantly reduce the time required to obtain resistance results in 24-72 hours and, moreover, improve patient outcomes. The second algorithm is a hierarchical Variational AutoEncoder (VAE) for heterogeneous data using an explainable FA latent space, called FA-VAE. The FA-VAE model is built on the foundation of the successful KSSHIBA approach for dealing with semi-supervised heterogeneous multi-view problems. This approach further expands the range of data domains it can handle. With the ability to work with a wide range of data types, including multilabel, continuous, binary, categorical, and even image data, the FA-VAE model offers a versatile and powerful solution for real-world data sets, depending on the VAE architecture. Additionally, this model is adapted and used in the microbiological laboratory of IISGM, resulting in an innovative technique for automatic ribotyping of C. diff, using MALDI-TOF data. To the best of our knowledge, this is the first demonstration of using any kind of ML for C. diff ribotyping. Experiments have been conducted on strains of Hospital General Universitario Gregorio Marañón (HGUGM) to evaluate the viability of the proposed approach. The results have demonstrated high accuracy rates where KSSHIBA even achieved perfect accuracy in the first data collection. These models have also been tested in a real-life outbreak scenario at the HGUGM, where successful classification of all outbreak samples has been achieved by FAVAE. The presented results have not only shown high accuracy in predicting each strain’s ribotype but also revealed an explainable latent space. Furthermore, traditional ribotyping methods, which rely on PCR, required 7 days while FA-VAE has predicted equal results on the same day. This improvement has significantly reduced the time response by helping in the decision-making of isolating patients with hyper-virulent ribotypes of C. diff on the same day of infection. The promising results, obtained in a real outbreak, have provided a solid foundation for further advancements in the field. This study has been a crucial stepping stone towards realising the full potential of MALDI-TOF for bacterial ribotyping and advancing our ability to tackle bacterial outbreaks. In conclusion, this doctoral thesis has significantly contributed to the field of Bayesian FA by addressing its drawbacks in handling various data types through the creation of novel models, namely KSSHIBA and FA-VAE. Additionally, a comprehensive analysis of the limitations of automating laboratory procedures in the microbiology field has been carried out. The shown effectiveness of the newly developed models has been demonstrated through their successful implementation in critical problems, such as predicting antibiotic resistance and automating ribotyping. As a result, KSSHIBA and FA-VAE, both in terms of their technical and practical contributions, signify noteworthy progress both in the clinical and the Bayesian statistics fields. This dissertation opens up possibilities for future advancements in automating microbiological laboratories.La microbiología clínica es uno de los temas críticos de este siglo. La identificación y discriminación de microorganismos se considera una amenaza mundial para la salud pública por parte de las principales organizaciones internacionales de salud, como la Organización Mundial de la Salud (OMS) o el Centro Europeo para la Prevención y Control de Enfermedades (ECDC). La rápida propagación, alta morbilidad y mortalidad, así como la carga económica asociada con su tratamiento y control, son las principales causas de su impacto. La discriminación de microorganismos es crucial para aplicaciones clínicas, como el caso de Clostridium difficile (C. diff ), el cual aumenta la mortalidad y morbilidad de las infecciones relacionadas con la atención médica. Además, en las últimas dos décadas, otros tipos de bacterias, incluyendo Klebsiella pneumoniae (K. pneumonia), han demostrado una propensión significativa a adquirir mecanismos de resistencia a los antibióticos. En consecuencia, el uso de un antibiótico ineficaz puede resultar en un aumento de la mortalidad. El aprendizaje automático (ML) tiene el potencial de ser aplicado en el campo de la microbiología clínica para automatizar las metodologías actuales y proporcionar tratamientos personalizados más eficientes y guiados. Sin embargo, los datos microbiológicos son difíciles de explotar debido a la presencia de una mezcla heterogénea de tipos de datos, tales como datos reales de alta dimensionalidad, indicadores categóricos, datos epidemiológicos multietiqueta, objetivos binarios o incluso series temporales. Este problema, conocido en el campo del aprendizaje automático (ML) como aprendizaje multimodal o multivista, ha sido estudiado en otras áreas de aplicación, como en el monitoreo de la salud mental o la hematología. El aprendizaje multivista combina diferentes modalidades o vistas que representan los mismos datos para extraer conocimientos más ricos y mejorar la comprensión. Cada vista corresponde a un mecanismo de codificación distinto para los datos, y esta tesis aborda particularmente el problema de la heterogeneidad multivista. En el campo del aprendizaje automático probabilístico, la explotación del aprendizaje multivista también se conoce como Análisis de Factores (FA) Bayesianos. Las soluciones actuales enfrentan limitaciones al manejar datos de alta dimensionalidad y correlaciones no lineales. Investigaciones recientes proponen métodos probabilísticos profundos para aprender representaciones jerárquicas de los datos, que pueden capturar relaciones no lineales intrincadas entre características. Sin embargo, algunas técnicas de aprendizaje profundo (DL) se basan en representaciones complejas, dificultando así la interpretación de los resultados. Además, algunos métodos de inferencia utilizados en DL pueden ser computacionalmente costosos, obstaculizando su aplicación práctica. Por lo tanto, existe una demanda de técnicas más interpretables, explicables y computacionalmente eficientes para datos de alta dimensionalidad. Al combinar múltiples vistas que representan la misma información, como datos genómicos, proteómicos y epidemiológicos, el aprendizaje multimodal podría proporcionar una mejor comprensión del mundo microbiano. Dicho lo cual, en esta tesis se proponen el desarrollo de dos modelos probabilísticos profundos que pueden manejar las limitaciones actuales en el estado del arte de la microbiología clínica. Además, ambos modelos también se someten a prueba en dos escenarios reales relacionados con la predicción de resistencia a los antibióticos en K. pneumoniae y el ribotipado automático de C. diff en colaboración con el IISGM y el IRyCIS. El primer algoritmo presentado es Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). Este algoritmo utiliza una formulación kernelizada para manejar correlaciones no lineales proporcionando representaciones compactas a través de la selección automática de vectores relevantes. Además, utiliza un Automatic Relevance Determination (ARD) sobre el kernel para determinar la relevancia de las características de entrada. Luego, se adapta y aplica a los laboratorios microbiológicos del IISGM y IRyCIS para predecir la resistencia a antibióticos en K. pneumoniae. Para ello, se utilizan kernels específicos que manejan la espectrometría de masas Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) de bacterias. Además, al aprovechar el aprendizaje multimodal entre los espectros y la información epidemiológica, supera a otros algoritmos de última generación. Los resultados presentados demuestran la importancia de los modelos heterogéneos ya que pueden analizar la información epidemiológica y ajustarse automáticamente para diferentes distribuciones de datos. La implementación de este método en laboratorios microbiológicos podría reducir significativamente el tiempo requerido para obtener resultados de resistencia en 24-72 horas y, además, mejorar los resultados para los pacientes. El segundo algoritmo es un modelo jerárquico de Variational AutoEncoder (VAE) para datos heterogéneos que utiliza un espacio latente con un FA explicativo, llamado FA-VAE. El modelo FA-VAE se construye sobre la base del enfoque de KSSHIBA para tratar problemas semi-supervisados multivista. Esta propuesta amplía aún más el rango de dominios que puede manejar incluyendo multietiqueta, continuos, binarios, categóricos e incluso imágenes. De esta forma, el modelo FA-VAE ofrece una solución versátil y potente para conjuntos de datos realistas, dependiendo de la arquitectura del VAE. Además, este modelo es adaptado y utilizado en el laboratorio microbiológico del IISGM, lo que resulta en una técnica innovadora para el ribotipado automático de C. diff utilizando datos MALDI-TOF. Hasta donde sabemos, esta es la primera demostración del uso de cualquier tipo de ML para el ribotipado de C. diff. Se han realizado experimentos en cepas del Hospital General Universitario Gregorio Marañón (HGUGM) para evaluar la viabilidad de la técnica propuesta. Los resultados han demostrado altas tasas de precisión donde KSSHIBA incluso logró una clasificación perfecta en la primera colección de datos. Estos modelos también se han probado en un brote real en el HGUGM, donde FA-VAE logró clasificar con éxito todas las muestras del mismo. Los resultados presentados no solo han demostrado una alta precisión en la predicción del ribotipo de cada cepa, sino que también han revelado un espacio latente explicativo. Además, los métodos tradicionales de ribotipado, que dependen de PCR, requieren 7 días para obtener resultados mientras que FA-VAE ha predicho resultados correctos el mismo día del brote. Esta mejora ha reducido significativamente el tiempo de respuesta ayudando así en la toma de decisiones para aislar a los pacientes con ribotipos hipervirulentos de C. diff el mismo día de la infección. Los resultados prometedores, obtenidos en un brote real, han sentado las bases para nuevos avances en el campo. Este estudio ha sido un paso crucial hacia el despliegue del pleno potencial de MALDI-TOF para el ribotipado bacteriana avanzado así nuestra capacidad para abordar brotes bacterianos. En conclusión, esta tesis doctoral ha contribuido significativamente al campo del FA Bayesiano al abordar sus limitaciones en el manejo de tipos de datos heterogéneos a través de la creación de modelos noveles, concretamente, KSSHIBA y FA-VAE. Además, se ha llevado a cabo un análisis exhaustivo de las limitaciones de la automatización de procedimientos de laboratorio en el campo de la microbiología. La efectividad de los nuevos modelos, en este campo, se ha demostrado a través de su implementación exitosa en problemas críticos, como la predicción de resistencia a los antibióticos y la automatización del ribotipado. Como resultado, KSSHIBA y FAVAE, tanto en términos de sus contribuciones técnicas como prácticas, representan un progreso notable tanto en los campos clínicos como en la estadística Bayesiana. Esta disertación abre posibilidades para futuros avances en la automatización de laboratorios microbiológicos.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Jerónimo Arenas García.- Vocal: María de las Mercedes Marín Arriaz

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