Deteção de patologia cardíaca usando machine learning

Segundo a Organização Mundial da Saúde, as doenças cardiovasculares (DCV) representam 32% do número de mortes no mundo. A redução deste valor pode ser atingida através da deteção precoce que pode levar a um tratamento mais preciso, melhorando a expectativa de vida do paciente. A ausculta cardíaca é a principal técnica utilizada pelos profissionais de saúde para identificar muitas DCV. No entanto, a auscultação dos sons cardíacos é um procedimento difícil, já que muitos sons são fracos e difíceis de detetar, sendo necessário um processo de treino contínuo. Os estetoscópios modernos podem amplificar os sons cardíacos, reduzir o ruído de ambiente, melhorar a percepção do usuário e, mais importante, converter um sinal acústico em digital. Isto permitiu o desenvolvimento de sistemas de decisão assistidos por computador baseados na auscultação. Este documento apresenta uma metodologia que pode detectar automaticamente a existência de DCV através de sons cardíacos obtidos de diferentes partes do coração. Diversas tecnologias foram analisadas, assim como projetos que tentam resolver parte do problema em questão e a partir deles, três alternativas diferentes foram elaboradas e documentadas, assim como a divisão do dataset e métricas a serem usadas nos testes. Essas alternativas visam classificar anomalias na auscultação cardíaca dos pacientes. Vários modelos das duas primeiras alternativas foram implementados e seus resultados apresentados. Também é feita uma comparação entre as experiências desenvolvidas entre si, também com experiências básicas que não utilizam mecanismos inteligentes e com outros trabalhos que tenham o mesmo objetivo. O melhor resultado obtido foi pela primeira abordagem com uma exatidão de 94%, precisão de 81% e recall de 67%.According to World Health Organization, the cardiovascular diseases (CVD) represent 32% of the number of deaths worldwide. Early detection leads to a more accurate treatment plan and improves the patient’s life expectancy. Cardiac auscultation is the main technique used by health professionals to identify many CVD. Nevertheless, heart sound auscultation is a difficult procedure, since it requires continuous training and many heart sounds are faint and hard to detect. However, modern stethoscopes can amplify heart sounds, reduce the environment noise, improve the user’s perception and, more importantly, convert an acoustic signal to a digital one. This allowed, the development of computer assisted decision systems based on auscultation. This document presents a methodology that can automatically detect the existence of CVD through cardiac sounds obtained from different parts of the heart. Several technologies were analysed, as well as projects that try to solve part of the problem in question and from them, three different alternatives were elaborated and documented, as well as the division of test data and the metrics for their evaluation. These alternatives are intended to classify anomalies in patients' cardiac auscultation. Several models of the first two alternatives were implemented and their results presented. A comparison is also made between the experiences developed among themselves, also with basic experiments that do not use intelligent mechanisms and with other works that have the same objective. The best result obtained was by the first approach with an accuracy of 94%, precision of 81% and recall of 67%

Source level debugging of dynamically translated programs

The capability to debug a program at the source level is useful and often indispensable. Debuggers usesophisticated techniques to provide a source view of a program, even though what is executing on the hard-ware is machine code. Debugging techniques evolve with significant changes in programming languagesand execution environments. Recently, software dynamic translation (SDT) has emerged as a new execu-tion mechanism. SDT inserts a run-time software layer between the program and the host machine, provid-ing flexibility in execution and program monitoring. Increasingly popular technologies that use thismechanism include dynamic optimization, dynamic instrumentation, security checking, binary translation,and host machine virtualization. However, the run-time program modifications in a SDT environment posesignificant challenges to a source level debugger. Currently debugging techniques do not exist for softwaredynamic translators. This thesis is the first to provide techniques for source level debugging of dynamically translatedprograms. The thesis proposes a novel debugging framework, called Tdb, that addresses the difficult chal-lenge of maintaining and providing source level information for programs whose binary code changes asthe program executes. The proposed framework has a number of important features. First, it does notrequire or induce changes in the program being debugged. In other words, programs are debugged is theirdeployment environment. Second, the framework is portable and can be applied to virtually any SDT sys-tem. The framework requires minimal changes to an SDT implementation, usually just a few lines of code.Third, the framework can be integrated with existing debuggers, such as Gdb, and does not require changesto these debuggers. This improves usability and adoption, eliminating the learning curve associated with anew debugging environment. Finally, the proposed techniques are efficient. The runtime overhead of thedebugged programs is low and comparable to that of existing debuggers. Tdb's techniques have been implemented for three different dynamic translators, on two differenthardware platforms. The experimental results demonstrate that source level debugging of dynamicallytranslated programs is feasible, and our implemented systems are portable, usable, and efficient