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@article{188820,
        author = {Pavithra M U and Rudresh H M and Meghana H G and Preethi M L and Nisha S and Dr. Rajashekar K J},
        title = {Arrhythmia Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3577-3582},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188820},
        abstract = {Arrhythmias, or irregular heart rhythms, pose serious health risks and require timely detection to prevent severe complications. Traditional diagnostic methods such as manual electrocardiogram (ECG) interpretation can be slow and susceptible to human error. This study proposes a deep learning–based arrhythmia detection system designed to improve diagnostic accuracy and efficiency. A Kaggle dataset is preprocessed and augmented to enhance quality and address class imbalance, followed by an 80:10:10 split for training, validation, and testing. Multiple convolutional neural network (CNN) architectures—including ResNet50, Efficient Net, VGG16, VGG19, and a hybrid ResNet50-EfficientNet model—are trained and optimized through hyperparameter tuning and augmentation strategies. The best-performing models are integrated into a user-friendly web application built with HTML/CSS for the frontend and Flask for the backend. The system enables users to upload medical images, receive automated arrhythmia predictions, and access information on possible treatments and precautions. Overall, the project highlights the capability of deep learning to automate and enhance arrhythmia diagnosis, offering a scalable and accessible tool that supports clinical decision-making and improves healthcare outcomes.},
        keywords = {Arrhythmia Detection, ECG Signals, Deep Neural Networks, Transfer Learning, Hybrid Feature Extraction, Medical Image Processing.},
        month = {December},
        }