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Congestive heart failure detection in ECG using LSTM and CNN
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Resumo(s)
Congestive Heart Failure (CHF) is a chronic condition inm which the heart does not pump blood efficiently. This pathology causes fatigue, dyspnea, oedema, nausea, and memory problems, affecting patients’ quality of life. The causes include coronary artery disease, cardiomyopathy, arterial hypertension, and myocarditis. The diagnosis is usually based on the patient’s medical history, physical exams, echocardiogram, electrocardiogram, and other methods. Aiming to improve diagnostic tools, this study proposes an artificial intelligence model based on deep learning to classify pathological ECG signals indicative of CHF. The selected models were LSTM and CNN. The training was conducted using a personalised dataset created from the public databases BIDMC Congestive Heart Failure and PTB Diagnostic ECG from Physionet. ECG data from 28 individuals aged 22 to 71 were selected, including 14 with severe CHF (NYHA class 3 and 4) and 14 control samples without ECG abnormalities. The database architecture was designed so that the input to the neural networks was raw ECG signals without filtering or feature extraction. The results showed an accuracy of 98.21% for the CNN model and 92.26% for the LSTM model.
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Contexto Educativo
Citação
Ribeiro, Luiz; Guerreiro, Nathan; Chaabani, Mohamed Khalil; Luiz, Luiz E.; Lazzaretti, André; Teixeira, João Paulo (2025). Congestive heart failure detection in ECG using LSTM and CNN. In International Conference on Demographic Transition, Health and Technologies, ICDTHT 2025. p. 147-156. ISSN 2198-7246. DOI: 10.1007/978-3-031-94901-2_12
Editora
Springer Nature Switzerland