Congestive Heart Failure Detection in ECG Using LSTM and CNN

Luiz Ribeiro (), Nathan Guerreiro (), Mohamed Khalil Chaabani (), Luiz E. Luiz (), André Eugenio Lazzaretti () and João Paulo Teixeira ()
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Luiz Ribeiro: CeDRI, SusTEC, Polytechnic Institute of Bragança
Nathan Guerreiro: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
Mohamed Khalil Chaabani: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
Luiz E. Luiz: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
André Eugenio Lazzaretti: Graduate Program in Electrical and Computer Engineering, Federal University of Technology – Paraná
João Paulo Teixeira: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)

A chapter in Health Technologies and Demographic Challenges, 2025, pp 147-156 from Springer

Abstract: Abstract Congestive Heart Failure (CHF) is a chronic condition in 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.

Keywords: Digital health; Deep learning; ECG classification (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-94901-2_12

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DOI: 10.1007/978-3-031-94901-2_12

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