Key result
A One-Dimensional Convolutional Neural Network (1D-CNN) achieved an accuracy of 95.09% and an AUC score of 0.958 for the automatic classification of coronary artery disease from ECG signals.
Why the study?
Early, accurate, and non-invasive diagnostic methods for coronary artery disease are needed to improve detection and reduce morbidity and mortality.
Does a 1D-CNN model with advanced feature extraction accurately detect coronary artery disease from multilead ECG signals?
Does a 1D-CNN model with advanced feature extraction accurately detect coronary artery disease from multilead ECG signals?
Effect estimate: AUC 0.958
An integrated 1D-CNN model achieved high diagnostic accuracy and AUC for automated classification of coronary artery disease using ECG signals.
Coronary artery disease (CAD), which is one of the leading causes of the global morbidity and mortality rates, needs for early, accurate, and non-invasive diagnostic methods. Electro- cardiogram (ECG) signals have been used to obtain information about the electrical activity of the heart and are considered a potentiality in modality for detecting CAD. This paper proposes an enhanced diagnostic methodology that integrates advanced feature extraction and a One-Dimensional Convolutional Neural Network (1D-CNN) for automatic CAD classification. The PTB-XL ECG recordings dataset, which contains annotated multilead ECG signals with standardized diagnostic statements, has been used in this study. A binary classification task has been performed to group the ECGs of coronary artery disease related classifications to a CAD class, while all other ECGs classified as normal are assigned to a control class. The dataset featured class imbalance resembling real clinical distribution with 90% Normal and 10% CAD segments. The proposed model achieved an accuracy of 95.09% and an AUC score of 0.958. The results demonstrate that the proposed method can facilitate accurate ECG-based CAD detection, minimize the chance of manual misinterpretation, and enable real-time clinical screening and early diagnosis in a scalable manner.
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Sujatha et al. (2026) studied Coronary artery disease (CAD). One-Dimensional Convolutional Neural Network (1D-CNN) with advanced feature extraction vs. Normal ECGs (control class) was evaluated on Automatic CAD classification (AUC 0.958). A One-Dimensional Convolutional Neural Network (1D-CNN) achieved an accuracy of 95.09% and an AUC score of 0.958 for the automatic classification of coronary artery disease from ECG signals.
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