Automated Detection of Atrial Fibrillation Using Deep Convolutional Neural Networks: Advancing Cardiac Diagnostics through AI-Driven ECG Analysis

AFib is the most common cardiac arrhythmia, associated with increased risks of stroke, heart failure, and mortality. Although diagnostic tools are at hand, timely and accurate detection remains one of the most crucial challenges in healthcare and hence requires advanced computational methodologies that can automate this process. In this work, the authors propose a novel convolutional neural network-based model for automated detection of AFib using electrocardiogram signals. The proposed research uses the comprehensive and well-annotated PhysioNet Atrial Fibrillation Database for the development and validation of the proposed system. This covers elaborate data preprocessing, segmentation of long-term ECG signals into fixed-length samples, and augmentation techniques that simulate real-life variability factors such as noise or scale. The architecture of the model contains stacked convolutional layers with batch normalization and dropout regularization and dense layers for binary classification. A generalization and model performance robust evaluation is warranted by a framework of 5-fold cross-validation. The model achieved high accuracy and sensitivity across all validation folds, with a mean area under the receiver operating characteristic curve-ROC-AUC-presenting its capability of distinguishing between normal and arrhythmic ECG signals. While these preliminary results were indeed reassuring, the current study discusses class imbalance issues and presents augmentation-based solutions for mitigating potential biases. This work has bridged important gaps in ECG signal processing and raised the bar high for future AI-driven approaches in cardiac diagnostics. The findings have emphasized deep learning technologies for advancing clinical decision-making and have provided a non-scalable and interpretable framework for deployment in the real world. In this regard, the study furthers AFib detection capabilities and hence contributes to improved cardiovascular health internationally.