Detection and classification of electrocardiography using hybrid deep learning models

Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. Precision of...

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Vydané v:Hellenic journal of cardiology Ročník 81; s. 75
Hlavní autori: Selvam, Immaculate Joy, Madhavan, Moorthi, Kumarasamy, Senthil Kumar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands 01.01.2025
Predmet:
Cardiovascular Diseases - classification
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - physiopathology
Deep Learning
Electrocardiography - classification
Electrocardiography - methods
Humans
Neural Networks, Computer
Electrocardiography
Variational autoencoder
PTB-XL database
Cardiovascular diseases
Convolutional neural network
ISSN:2241-5955, 2241-5955
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Shrnutí:Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. Precision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared. The classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07% and 1.87%, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record. When compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:2241-5955
2241-5955
DOI:10.1016/j.hjc.2024.08.011