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

Advanced Telecommunications and Signal Processing Program

Contains an introduction, and reports on seven research projects.Advanced Telecommunications Research ProgramAT&T FellowshipINTEL FellowshipU.S. Navy - Office of Naval Research NDSEG Graduate FellowshipMaryland Procurement Office Contract MDA904-93-C-418

Subband domain coding of binary textual images for document archiving

Cataloged from PDF version of article.In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented

Objective automatic assessment of rehabilitative speech treatment in Parkinson's disease

Vocal performance degradation is a common symptom for the vast majority of Parkinson's disease (PD) subjects, who typically follow personalized one-to-one periodic rehabilitation meetings with speech experts over a long-term period. Recently, a novel computer program called Lee Silverman voice treatment (LSVT) Companion was developed to allow PD subjects to independently progress through a rehabilitative treatment session. This study is part of the assessment of the LSVT Companion, aiming to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as “acceptable” (a clinician would allow persisting during in-person rehabilitation treatment) or “unacceptable” (a clinician would not allow persisting during in-person rehabilitation treatment). We characterize each of the 156 sustained vowel /a/ phonations with 309 dysphonia measures, select a parsimonious subset using a robust feature selection algorithm, and automatically distinguish the two cohorts (acceptable versus unacceptable) with about 90% overall accuracy. Moreover, we illustrate the potential of the proposed methodology as a probabilistic decision support tool to speech experts to assess a phonation as “acceptable” or “unacceptable.” We envisage the findings of this study being a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool

Special issue on signal processing and machine learning for biomedical data

This Special Issue is focused on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial vision systems into a wide range of high-level tasks. Such progress can help address problems in the analysis of biomedical data.This Special Issue placed particular emphasis on contributions dealing with practical, applications-led research, on the use of methods and devices in clinical diagnosis. The works that make up this special issue show a remarkable variety of applications for the detection and classification of medical imaging problems. In particular, the aforementioned works can be divided on the basis of types of techniques used, into three categories—signal processing (SP) methods, traditional machine learning (ML) methods, and deep learning (DL) methods

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference