Key result
VGG16 deep learning model tops single-lead ECG classification with an ~98% F1-score on lead V4.
Why the study?
There is a critical need for accessible, early, and accurate cardiac diagnostics in resource-limited or remote settings, motivating the exploration of advanced deep learning models for single-lead ECG classification.
Do advanced deep learning models accurately classify cardiac conditions using single-lead ECG data?
Do advanced deep learning models accurately classify cardiac conditions using single-lead ECG data?
Deep learning models like VGG16 and MobileNetV2 can accurately and rapidly classify cardiac conditions from single-lead ECGs, supporting the development of portable diagnostic tools.
Supports single-lead ECG AI deployment in clinics; extends DL model benchmarks for optimized lead selection.
Background/Objectives: This study aims to address the critical need for accessible, early, and accurate cardiac di-agnostics, especially in resource-limited or remote settings. By shifting focus from traditional multi-lead ECG analysis to single-lead ECG data, this research explores the potential of advanced deep learning models for classifying cardiac conditions, including Nor-mal, Abnormal, Previous Myocardial Infarction (PMI), and Myocardial Infarction (MI). Methods: Five state-of-the-art deep learning architectures—Inception, DenseNet201, MobileNetV2, NASNetLarge, and VGG16—were systematically evaluated on individual ECG leads. Key performance metrics, such as model accuracy, inference time, and size, were analyzed to determine the optimal configurations for practical applications. Results: VGG16 emerged as the most accurate model, achieving an F1-score of 98.11% on lead V4 with a prediction time of 4.2 ms and a size of 528 MB, making it suitable for high-precision clinical settings. MobileNetV2, with a compact size of 13.4 MB, offered a balanced performance, achieving a 97.24% F1-score with a faster inference time of 3.2 ms, positioning it as an ideal candidate for real-time monitoring and telehealth applications. Conclusions: This study bridges a critical gap in cardiac diagnostics by demonstrating the feasibility of lightweight, scalable, single-lead ECG analysis using advanced deep learning models. The findings pave the way for deploying portable diagnostic tools across diverse settings, enhancing the accessibility and efficiency of cardiac care globally.
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Mohamed Ezz (2025) studied Cardiac conditions (Normal, Abnormal, Previous Myocardial Infarction, Myocardial Infarction). Deep learning models for single-lead ECG classification vs. Other deep learning architectures was evaluated on Model accuracy (F1-score). The VGG16 deep learning model achieved the highest accuracy for single-lead ECG classification with an F1-score of 98.11% on lead V4.
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