Classificação de episódios de fibrilação atrial por análise do ECG com redes neuronais artificiais MLP e LSTM

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáA fibrilação atrial (AF) é uma doença cardíaca que afeta aproximadamente 1% da população mundial, sendo a anomalia cardíaca mais comum. Apesar de não ser uma causa direta de morte, frequentemente está associada ou gera outros problemas que ameaçam a vida humana, como o derrame e a doença da artéria coronária. As principais características da AF são: a alta variação do ritmo cardíaco, o enfraquecimento ou desaparecimento da contração atrial e a ocorrência de irregularidades nas atividades dos ventrículos. O diagnóstico da AF é realizado por um médico especialista, principalmente através da inspeção visual de gravações de eletrocardiograma (ECG) de longo termo. Tais gravações podem chegar a várias horas, e são necessárias pois a AF pode ocorrer a qualquer momento do dia. Dessa forma surgem os problemas quanto ao grande volume de dados e as dependências de longo termo. Além disso, as particularidades e as variabilidades dos padrões de deformação de cada sujeito fazem com que o problema esteja também relacionado com a experiência do cardiologista. Assim, a proposta de um sistema computacional de auxílio ao diagnóstico médico baseado em inteligência artificial se torna muito interessante, uma vez que não sofre com a fadiga e é fortemente indicado para lidar com dados em grande quantidade e com alta variabilidade. Portanto, neste trabalho foi proposta a exploração de modelos de aprendizagem de máquina para análise e classificação de sinais ECG de longo termo, para auxiliar no diagnóstico da AF. Os modelos foram baseados em redes neuronais artificiais do tipo Multi-Layer Perceptron (MLP) e Long Short-Term Memory (LSTM). Utilizam-se os sinais da base de dados MIT-BIH Atrial Fibrillation, sem remoção de ruído, tendências ou artefatos, numa etapa de extração de características temporais, morfológicas, estatísticas e em tempo-frequência sobre segmentos de contexto variável (duração em segundos ou contagem de intervalos entre picos R). As características do sinal ECG utilizadas, foram: duração dos intervalos R-R (RRi) consecutivos, perturbação Jitter, perturbação Shimmer, entropias de Shannon e energia logarítmica, frequências instantâneas, entropia espectral e transformada Scattering. Sobre estes atributos foram aplicadas diferentes estratégias de normalização por Z-score e valor máximo absoluto, de forma a normalizar os indicadores de acordo com o contexto do sujeito ou local do segmento. Após a exploração de várias combinações destas características e dos parâmetros das redes MLP, obteve-se uma acurácia de classificação para a metodologia 10-fold cross-validation de 80,67%. Entretanto, notou-se que as marcações do pico das ondas R advindas da base de dados eram imprecisas. Dessa forma, desenvolveu-se um algoritmo de detecção do pico das ondas R baseado na combinação entre a derivada do sinal, a energia de Shannon e a transformada de Hilbert, resultado em uma acurácia de marcação dos picos R de 98,95%. A partir das novas marcações, determinou-se todas as características e em seguida foram exploradas diversas estruturas de redes neuronais MLP e LSTM, sendo que os melhores resultados em acurácia/exatidão para estas arquiteturas foram, respectivamente, 91,96% e 98,17%. Em todos os testes, a MLP demonstrou melhora de desempenho à medida que mais características foram sendo agregadas nos conjuntos de dados. A LSTM por outro lado, obteve os melhores resultados quando foram combinados 60 RRi e as respectivas entropias das ondas P, T e U.Atrial fibrillation (AF) is a heart disease that affects approximately 1% of the world population, being the most common cardiac anomaly. Although it is not a direct cause of death, it is often associated with or generates other problems that threaten human life, such as stroke and coronary artery disease. The main characteristics of AF are the high variation in heart rate, the weakening or disappearance of atrial contraction and the occurrence of irregularities in the activities of the ventricles. The diagnosis of AF is performed by a specialist doctor, mainly through visual inspection of long-term electrocardiogram (ECG) recordings. Such recordings can take several hours and are necessary because AF can occur at any time of the day. Thus, problems arise regarding the large amount of data and long-term dependencies. In addition, the particularities and variability of the deformation patterns of each subject make the problem also related to the cardiologist's experience. Thus, the proposal for a computational system to aid medical diagnosis based on artificial intelligence becomes very interesting, since it does not suffer from fatigue and is strongly indicated to deal with data in large quantities and with high variability. Therefore, in this work it was proposed to explore machine learning models for the analysis and classification of long-term ECG signals, to assist in the diagnosis of AF. The models were based on artificial neural networks Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM). The signals from the MIT-BIH Atrial Fibrillation database are used, without removing noise, trends or artifacts, in a stage of extracting temporal, morphological, statistical and time-frequency features over segments of variable context (duration in seconds or counting intervals between peaks R). The features of the ECG signal used were: duration of consecutive R-R (RRi) intervals, Jitter disturbance, Shimmer disturbance, Shannon entropies and logarithmic energy, instantaneous frequencies, spectral entropy and Scattering transform. On these attributes, different normalization strategies were applied by Z-score and absolute maximum value, to normalize the indicators according to the context of the subject or location of the segment. After exploring various combinations of these features and the parameters of the MLP networks, the accuracy of classification for the 10-fold cross-validation methodology was 80.67%. However, it was noted that the annotations of the peak of R waves from the database were inaccurate. Thus, an algorithm for detecting the peak of R waves was developed based on the combination of the derivative of the signal, the Shannon energy, and the Hilbert transform, resulting in an accuracy of marking the R peaks of 98.95%. From the new markings, all features were determined and then several structures of neural networks MLP and LSTM were explored, and the best results in accuracy for these architectures were, respectively, 91.96% and 98.17%. In all tests, MLP showed improvement in performance as more features were added to the data sets. LSTM, on the other hand, obtained the best result when 60 RRi and the respective entropies of the P, T and U waves were combined

Brief review on electrocardiogram analysis and classification techniques with machine learning approaches

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.info:eu-repo/semantics/publishedVersio

A COVID-19 time series forecasting model based on MLP ANN

With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic.This work has been supported by Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters

Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the 10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented to compose the network input sets, indicating a slight improvement in the model's performance. Added to these, Shannon's and logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

EVOLUÇÃO TECTONOESTRATIGRÁFICA DA FORMAÇÃO BARRA VELHA NA ÁREA DOS CAMPOS DE LAPA E SAPINHOÁ, BACIA DE SANTOS – BRASIL.: Tectonostratigraphic evolution of Barra Velha Formation in the Lapa and Sapinhoá oil fields, Santos Basin - Brazil

The Sedimentary section of the so-called post-rift phase in the Santos basin, Brazil, consists of the Barra Velha and Ariri formations. The Barra Velha formation, composed essentially by carbonates interpreted as microbial, which is the main hydrocarbon reservoir in the pre-salt play is the research object of this work. During the westernern Gondwana Cretaceous taphrogenesis that culminated with its breakup and the generation of the South American and African continents, the Santos basin, inserted in this context underwent tectonophysical processes over three distinct phases of tectonic and stratigraphic evolution: lithospheric stretching, lithospheric thinning and mantle exhumation. The Barra Velha formation was deposited in the lithospheric thinning phase in a mixed tectonic context that combined basin generalized thermal subsidence with localized mechanical subsidence due to some rift faults remaining active throughout the post-rift phase.A seção Sedimentar da chamada fase pós-rifte na bacia de Santos, Brasil, é constituída pelas formações Barra Velha e Ariri. A formação Barra Velha, composta essencialmente por carbonatos interpretados como microbiais e que é o principal reservatório para hidrocarbonetos no play pré-sal, é o objeto de pesquisa deste trabalho. Durante a tafrogenia cretácea da porção oeste do supercontinente Gondwana que culminou com sua ruptura e a geração dos continentes Sulamericano e Africano a bacia de Santos, inserida neste contexto, foi submetida a processos tectonofísicos que definiram três fases de evolução tectônica e estratigráfica bem distintas: estiramento litosférico, afinamento litosférico e exumação do manto. A formação Barra Velha foi depositada na fase de afinamento litosférico em um contexto tectônico misto que combinou subsidência termal generalizada da bacia com subsidência mecânica localizada devido a algumas falhas da fase rifte permanecerem ativas durante toda a chamada fase pós-rifte

Multivariate analysis of soil resistance to penetration in no-tillage

In order to increase agricultural production, Brazil opted for the expansion of farming areas and the intensive use of mechanization, causing serious damage to soils. At the same time, conservation practices such as no-tillage farming are also in use. Nevertheless, soil density assessments are necessary in both situations. Practical and controversial, the methodology of penetrometry is the most utilized in such assessments. The objective of this study was to identify the vertical stratification of soil resistance to penetration. Data was collected in a Typic Hapludox under no-tillage. Samples were collected in forty points of a 50 x 50m grid. Soil resistance was evaluated in three different positions in each point: plant rows, between the plant rows, and in the rows of the tractor path. Measurements were taken using a hydraulic-electronic penetrometer. Factorial analysis with orthogonal rotation, a Multivariate Analysis technique, was used to analyze soil resistance of soil layers. Four layers were identified in the plant rows, when more superficial layers had higher relative variance. Three layers were identified in the plant rows and in rows of the tractor path, points of anthropic action; in this case, deeper layers had higher relative variance.Com vistas ao aumento da produção, optou-se, no Brasil, pelo acréscimo de área cultivada e pelo intenso uso da motomecanização, o que acarretou prejuízos aos solos agrícolas. Paralelamente, práticas conservacionistas têm sido adotadas, a exemplo do sistema plantio direto. Em ambos os casos, estudos do estado de compactação do solo se fazem necessários. Prática e controversa, a penetrometria é a metodologia mais utilizada para o dimensionamento desse parâmetro. Assim, realizou-se trabalho em um Latossolo Vermelho sob plantio direto visando a identificar a estratificação vertical da resistência do solo à penetração. Foram amostrados 40 pontos, em malha de 50mx50m. Em cada ponto, analisou-se o comportamento do solo em três posições: na linha de semeadura, entre as linhas de semeadura e entre as linhas de semeadura com tráfego de trator agrícola. Os dados foram obtidos com penetrômetro hidráulico-eletrônico. Para identificação das camadas de solo com resistência à penetração semelhante, utilizou-se técnica de análise multivariada, denominada análise fatorial, com rotação ortogonal. Entre as linhas de semeadura, identificaram-se quatro camadas, sendo que as mais superficiais se mostraram com variância relativa maior. Nas entrelinhas com tráfego de trator e nas linhas de semeadura, locais de ação antrópica, identificaram-se três camadas, sendo as mais profundas as de variância relativa mais elevada.1186119

Android-based ECG monitoring system for atrial fibrillation detection using a BITalino® ECG sensor

Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphical representation of the electrical activity of the heart. Atrial fibrillation (AF) is the most present type of arrhythmia in the world population. In this context, this work deals with the implementation of a system for automatic analysis of ECG signals aiming to identify AF episodes. The system consists of a signal acquisition step performed by an ECG sensor connected to an acquisition platform. The acquired signal is transmitted via bluetooth to a smartphone with Android™ operating system. The signal processing is carried out through an application developed using the IDE Android™ Studio. When assessed over signals from the MIT-BIH Atrial Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity and positive predictivity of 98.99% and 95.95%, respectively. The classification model used is based on a long short-term memory (LSTM) neural network and had an average accuracy of 94.94% for identifying AF episodes.This work has supported by Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020, and by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Portugal 2020 in the framework of the NanoID (NORTE-01-0247-FEDER-046985) Project.info:eu-repo/semantics/publishedVersio

Optimization of glottal onset peak detection algorithm for accurate Jitter measurement

Jitter is an acoustic parameter used as input for intelligent systems for the diagnosis of speech related pathologies. This work has the objective to improve an algorithm that allows to extract vocal parameters, and thus improve the accuracy measurement of absolute jitter parameter. Some signals were analyzed, where signal to signal was compared in order to try to understand why the values are different in some signal between the original algorithm and the reference software. In this way, some problems were found that allowed to adjust the algorithm, and improve the measurement accuracy for those signals. Subsequently, a comparative analysis was performed between the values of the original algorithm, the adjusted algorithm and the Praat software (assumed as reference). By comparing the results, it was concluded that the adjusted algorithm allows the extraction of the absolute jitter with values closer to the reference values for several speech signals. For the analysis, sustained vowels of control and pathological subjects were used.This work was supported by Funda¸c˜ao para a Ciˆencia e Tecnologia within the Project Scope: UIDB/05757/2020info:eu-repo/semantics/publishedVersio

Análise multivariada da resistência do solo à penetração sob plantio direto

Com vistas ao aumento da produção, optou-se, no Brasil, pelo acréscimo de área cultivada e pelo intenso uso da motomecanização, o que acarretou prejuízos aos solos agrícolas. Paralelamente, práticas conservacionistas têm sido adotadas, a exemplo do sistema plantio direto. Em ambos os casos, estudos do estado de compactação do solo se fazem necessários. Prática e controversa, a penetrometria é a metodologia mais utilizada para o dimensionamento desse parâmetro. Assim, realizou-se trabalho em um Latossolo Vermelho sob plantio direto visando a identificar a estratificação vertical da resistência do solo à penetração. Foram amostrados 40 pontos, em malha de 50mx50m. Em cada ponto, analisou-se o comportamento do solo em três posições: na linha de semeadura, entre as linhas de semeadura e entre as linhas de semeadura com tráfego de trator agrícola. Os dados foram obtidos com penetrômetro hidráulico-eletrônico. Para identificação das camadas de solo com resistência à penetração semelhante, utilizou-se técnica de análise multivariada, denominada análise fatorial, com rotação ortogonal. Entre as linhas de semeadura, identificaram-se quatro camadas, sendo que as mais superficiais se mostraram com variância relativa maior. Nas entrelinhas com tráfego de trator e nas linhas de semeadura, locais de ação antrópica, identificaram-se três camadas, sendo as mais profundas as de variância relativa mais elevada