Key result
A One-Dimensional Convolutional Neural Network integrating advanced feature extraction achieved a classification accuracy of 95.09% and an AUC of 0.958 for detecting 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.
Effect estimate: AUC 0.958
A 1D-CNN model using advanced feature extraction demonstrated high accuracy and AUC for detecting coronary artery disease from ECG signals, though the manuscript exhibits severe inconsistencies between the abstract and full text.
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. One-Dimensional Convolutional Neural Network (1D-CNN) and advanced feature extraction vs. Control class (Normal ECGs) was evaluated on Diagnostic accuracy and Area Under the Curve (AUC) for CAD classification (AUC 0.958). A One-Dimensional Convolutional Neural Network integrating advanced feature extraction achieved a classification accuracy of 95.09% and an AUC of 0.958 for detecting coronary artery disease from ECG signals.
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