Aplicação de redes neurais artificiais para o diagnóstico de arritmias cardíacas usando processamento de sinais de eletrocardiograma.
Data
2022-10-14
Autores
Lima, Victor Mendes Cunha 
Orientadores
Santos, Sergio Ronaldo Barros dos
Tipo
Trabalho de conclusão de curso
Título da Revista
ISSN da Revista
Título de Volume
Resumo
Este trabalho trata da aplicação de redes neurais artificiais (RNAs) como
método de diagnóstico de arritmias cardíacas, a exemplo de infarto no miocárdio,
utilizando técnicas de processamento de sinais de eletrocardiograma obtidos a partir
de um conjunto de dados com informações de diagnóstico de pacientes saudáveis e
de pacientes com diversas patologias cardíacas. Os dados utilizados para
treinamento e teste das RNAs foram obtidos a partir da extração dos espectros das
derivações dos sinais de eletrocardiograma. Em seguida os espectros obtidos foram
filtrados e, a partir deles, foram calculadas as potências espectrais em sete bandas
de frequências distintas pelo método de Burg. Após a obtenção das potências
espectrais fez-se a separação dos dados em grupo de treinamento e grupo de teste,
usados para, respectivamente, treinar e testar as redes. Os treinamentos foram
realizados por meio do algoritmo backpropagation e do algoritmo LMS. A rede com
melhor desempenho apresentou taxa de acerto de 80,97%, enquanto a de pior
desempenho apresentou taxa de acerto de 78,94%. Mesmo com valores abaixo do
esperado, de acordo com outras literaturas, o método apresentou resultados
próximos com diferentes separações de dados, revelando boa precisão.
This project brings the application of Artificial Neural Networks (ANN's) for the diagnosis of heart diseases, such as myocardial infarction, by using signal processing techniques on electrocardiogram signals taken from a database with diagnostic informations of a group with healthy patients and other patients with different cardiac pathologies. The data used for training and testing the ANN’s were obtained by extracting the spectrums from the electrocardiogram derivation signals. The spectrums were then filtered and the spectral powers on seven frequency bands were calculated by Burg’s method. After calculating the spectral powers the data were separated into training group and testing group, used for training and testing the ANN’s. The trainings were made by backpropagation algorithm and by LMS algorithm. The network with the best performance presented a hit rate of 80.97%, while the worst one presented a hit rate of 78.94%. Even if according to other literatures these are below expectations the method has shown close results for various data separations, thus showing good precision.
This project brings the application of Artificial Neural Networks (ANN's) for the diagnosis of heart diseases, such as myocardial infarction, by using signal processing techniques on electrocardiogram signals taken from a database with diagnostic informations of a group with healthy patients and other patients with different cardiac pathologies. The data used for training and testing the ANN’s were obtained by extracting the spectrums from the electrocardiogram derivation signals. The spectrums were then filtered and the spectral powers on seven frequency bands were calculated by Burg’s method. After calculating the spectral powers the data were separated into training group and testing group, used for training and testing the ANN’s. The trainings were made by backpropagation algorithm and by LMS algorithm. The network with the best performance presented a hit rate of 80.97%, while the worst one presented a hit rate of 78.94%. Even if according to other literatures these are below expectations the method has shown close results for various data separations, thus showing good precision.