Loading...
Publication
Aplicação de identificação de episódios de fibrilação atrial
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 4.15 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
As Doenças Cardiovasculares (DCVs) causam cerca de 18 milhões de mortes por ano, segundo a Organização Mundial da Saúde (OMS). Na Europa, mais de 10 milhões de pessoas são afetadas anualmente, com 3 milhões de óbitos registrados em 2021. No Brasil, são aproximadamente 400 mil mortes por ano, resaltando arritmias cardíacas. A fibrilação atrial (FA), arritmia mais comum, é caracterizada por ritmo cardíaco irregular. Diante desse cenário e alinhado ao papel social do engenheiro e ao Objetivo de Desenvolvimento Sustentável (ODS) “Garantir saúde e bem-estar para todos” da Organização das Nações Unidas (ONU), este trabalho apresenta o desenvolvimento de uma interface gráfica do usuário (GUI) para aquisição e classificação de eletrocardiogramas (ECG). O sistema foi implementado em MATLAB, integrando aquisição em tempo real com a plataforma BITalino c (derivação I, via Bluetooth), detecção dos picos R e classificação automática de episódios de FA com redes neurais LSTM. São extraídas quatro características principais a cada 60 ciclos cardíacos: intervalos RR e entropias de Shannon das ondas T, U e P. Após normalização, essas variáveis compõem os vetores de entrada da rede, que classifica como Outro Ritmo, Ritmo Normal e Ritmo FA. A aplicação permite ainda a visualização dos sinais em tempo real e a geração automática de relatórios em PDF. A validação com sinais da base de dados MIT-BIH Atrial Fibrillation demonstrou que a interface é funcional, e a acurácia de 98,17%, obtida em estudo anterior, evidencia seu potencial como ferramenta auxiliar na análise de ECGs em ambientes clínicos e domiciliares.
Cardiovascular diseases (CVDs) cause approximately 18 million deaths per year, according to the World Health Organization (WHO). In Europe, more than 10 million people are affected annually, with 3 million deaths recorded in 2021. In Brazil, around 400,000 deaths occur each year, with a notable prevalence of cardiac arrhythmias. Atrial fibrillation (AF), the most common arrhythmia, is characterized by an irregular heart rhythm. In light of this scenario—and aligned with the social role of engineers and the United Nations Sustainable Development Goal (SDG) “Ensure healthy lives and promote well-being for all at all ages”, this work presents the development of a graphical user interface (GUI) for electrocardiogram (ECG) acquisition and classification. The system was implemented in Matlab, integrating real-time acquisition using the BITalino c platform (lead I, via Bluetooth), R-peak detection, and automatic classification of AF episodes using LSTM neural networks. Four main features are extracted every 60 cardiac cycles: RR intervals and Shannon entropy of T, U, and P waves. After normalization, these variables form the input vectors for the network, which classifies segments as Other Rhythm, Normal Rhythm, or AF Rhythm. The application also enables real-time signal visualization and automatic generation of PDF reports. Validation using signals from the MIT-BIH Atrial Fibrillation Database demonstrated that the interface is functional, and the accuracy of 98.17%, obtained in a previous study, highlights its potential as a support tool for ECG analysis in clinical and home settings.
Cardiovascular diseases (CVDs) cause approximately 18 million deaths per year, according to the World Health Organization (WHO). In Europe, more than 10 million people are affected annually, with 3 million deaths recorded in 2021. In Brazil, around 400,000 deaths occur each year, with a notable prevalence of cardiac arrhythmias. Atrial fibrillation (AF), the most common arrhythmia, is characterized by an irregular heart rhythm. In light of this scenario—and aligned with the social role of engineers and the United Nations Sustainable Development Goal (SDG) “Ensure healthy lives and promote well-being for all at all ages”, this work presents the development of a graphical user interface (GUI) for electrocardiogram (ECG) acquisition and classification. The system was implemented in Matlab, integrating real-time acquisition using the BITalino c platform (lead I, via Bluetooth), R-peak detection, and automatic classification of AF episodes using LSTM neural networks. Four main features are extracted every 60 cardiac cycles: RR intervals and Shannon entropy of T, U, and P waves. After normalization, these variables form the input vectors for the network, which classifies segments as Other Rhythm, Normal Rhythm, or AF Rhythm. The application also enables real-time signal visualization and automatic generation of PDF reports. Validation using signals from the MIT-BIH Atrial Fibrillation Database demonstrated that the interface is functional, and the accuracy of 98.17%, obtained in a previous study, highlights its potential as a support tool for ECG analysis in clinical and home settings.
Description
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
Keywords
Eletrocardiograma Fibrilação atrial LSTM Interface gráfica