Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification.

The diagnosis of cardiac dysfunctions requires the analysis of longterm ECG signal recordings, often containing hundreds to thousands of heart beats. In this work, automatic inter-patient classification of heart beats following AAMI guidelines is investigated. The prior of the normal class is by far larger than the other classes, and the classifier obtained by a standard SVM training is likely to act as the trivial acceptor. To avoid this inconvenience, a SVM classifier optimizing a convex approximation of the balanced classification rate rather than the standard accuracy is used. First, the assessment of feature sets previously proposed in the litterature is investigated. Second, the performances of this SVM model is compared to those of previously reported inter-patient classification models. The results show that the choice of the features is of major importance, and that some previously reported feature sets do not serve the classification performances. Also, the weighted SVM model with the best feature set selection achieves results better than previously reported inter-patient models with features extracted only from the R spike annotation

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

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases