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@article{192179,
        author = {B Naga Vyshnavi and NagaBhiravanath K A and Dr Smitha Kurian and Dr Krishna Kumar P R},
        title = {IDENTIFICATION OF ARRHYTHMIA CARDIAC USING ECG BY DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {335-339},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192179},
        abstract = {A heartbeat that strays from its normal rhythm might trigger serious issues - stroke or even sudden heart failure - when overlooked. To spot these glitches, doctors turn to ECG readings. Yet reading those signals by hand takes too long, plus it leans heavily on a clinician's experience. This study presents a signals of ECG. In this proposed system it uses CNN model which helps raw ECG data, this eliminates the data not required for the model and get need for handcraft feature engineering. Standard ECG datasets are used to train and evaluate, with signal preprocessing steps such as normalization, noise removal and segmentation applied to enhance performance. Model trained to classify multiple arrhythmia types, including sinus rhythm and common abnormal rhythms. Experimental results shows that deep learning model achieves high accuracy, sensitivity, specificity, etc. when distinguished with traditional machine learning methods. The findings indicate that deep learning-based ECG analysis can be an effective and reliable tool for early arrhythmia detection, supporting clinical and wearable healthcare applications},
        keywords = {},
        month = {February},
        }