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@article{168498,
        author = {SHAIK KHAJA UMAR and B MURALI},
        title = {DETECTION OF HEART FAILURE USING A CONVOLUTIONAL NEURAL NETWORK VIA ECG SIGNALS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1024-1029},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168498},
        abstract = {“Heart failure (HF) is a chronic heart condition that increases mortality, morbidity, and healthcare costs. The electrocardiogram (ECG) is a noninvasive and straightforward diagnostic tool that can reveal detectable changes in HF. Because of their small amplitude and duration, these changes can be subtle and potentially misclassified during manual interpretation or when analyzed by clinicians. This paper reports a 7-layer deep convolutional neural network (CNN) model for HF automatic detection. The proposed CNN model requires only minimal preprocessing of ECG signals and does not require any engineered features.},
        keywords = {Heart failure, ECG, Convolutional Neural Network, chronic Heart condition.},
        month = {October},
        }