Cardiac Abnormality Detection Model with Inverted Residual Blocks for Large Electrocardiogram Datasets | ||||
| Advances in Environmental and Life Sciences | ||||
| Articles in Press, Accepted Manuscript, Available Online from 18 December 2024 | ||||
| Document Type: Original research articles | ||||
| DOI: 10.21608/aels.2024.336248.1071 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Haneen A. Elyamani  1; Mohammed A.-M. Salem2; Nashwa M. Yhiea3; Farid Melgani4
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| 1Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 44745, Egypt | ||||
| 2Media Engineering and Technology, German University in Cairo (GUC), Cairo, Egypt | ||||
| 3Department of Mathematics ‑ Computer Science, Faculty of Science, Suez Canal University, Ismailia 44745, Egypt | ||||
| 4Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123 Trento, Italy | ||||
| Abstract | ||||
| Cardiovascular disease is a critical area of focus in deep learning, as it represents a leading cause of global mortality. Early diagnosis and effective management are crucial in mitigating the impact of heart disease. Deep learning (DL) has demonstrated great promise in revolutionizing various aspects of cardiovascular healthcare. With the ability to process vast amounts of data and recognize intricate patterns, DL models can assist in accurately diagnosing conditions, predicting outcomes, and even suggesting personalized treatment plans. DL models, particularly Convolutional Neural Network (CNN), have shown high accuracy in automatically detecting and classifying cardiac abnormalities. In this work, we propose a new CNN model based on modified inverted residual block (IRB) architecture to perform automatic feature extraction and classification of electrocardiogram (ECG) signals. The research were conducted to identify 2 and 5 distinct classes of heart diseases. The study was performed on data in the PTB-XL dataset. The experimental results demonstrated that our proposed model achieved the highest AUC in each classification task, scoring 94.74% and 93.23% for 2 and 5 classes, respectively. Based on the same public dataset, our results surpass the performance of the state-of the art methods in classifying disease entities into 2 and 5 classes on various evaluation metrics. | ||||
| Keywords | ||||
| Cardiovascular disease; Electrocardiogram (ECG); Deep learning (DL); Inverted residual block (IRB); PTB-XL dataset | ||||
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