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@article{168516,
        author = {JANAMALA DHANUNJAYA and D MURALI},
        title = {ECG-Based Heart Failure Detection Using Deep Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1106-1111},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168516},
        abstract = {Heart failure is a major public health concern, affecting millions worldwide. Early detection is crucial for effective treatment and improved patient outcomes. Traditional diagnosis methods rely on clinical evaluation and imaging modalities, which can be invasive, expensive, and time-consuming. This study investigates the feasibility of using convolutional neural networks (CNNs) to detect heart failure from electrocardiogram (ECG) signals. Our CNN model is designed to extract features from ECG recordings and classify them as heart failure or normal. We evaluated our approach on a large dataset of ECG recordings, achieving [insert percentage]% accuracy, [insert percentage]% sensitivity, and [insert percentage]% specificity. Our results demonstrate the potential of CNN-based ECG analysis for heart failure detection, offering a non-invasive, efficient, and accurate diagnostic tool.},
        keywords = {Medical Imaging, Signal Processing, Artificial Intelligence (AI), Machine Learning (ML), Healthcare Technology, Cardiovascular Disease, ECG Analysis},
        month = {October},
        }