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@article{176874,
        author = {Dhakshanadevi S and Dharika K and Geevithra G and Dr.S.Balakrishnan},
        title = {ECG And Sound Based Angiography System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {8008-8013},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176874},
        abstract = {Objective: The objective of this study is to develop a non-invasive diagnostic tool that integrates ECG signals with arterial sound data for the detection and analysis of blockages. Methods: The proposed system utilizes ECG and arterial sound signals, processed using complex signal processing techniques. Feature extraction methodologies are employed to drive meaningful data, which is then analyzed using machine learning algorithms to detect blockages. Results: The Detected blockages will be viewed in Dashboard in the form of signals or waveforms. Comparative studies with standard angiographic methods show promising results, validating the system’s effectiveness in a non-invasive manner. Discussion: This innovative method will minimize patient risk with traditional invasive angiographic procedures. The integration of ECG and arterial sound data enhances diagnostic capabilities, offering a cost-effective and reliable alternative for cardiovascular diagnostics.},
        keywords = {Electrocardiogram (ECG), Angiography, Arterial sound data, Signal processing, Machine Learning algorithms, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Feature Extraction, Non-invasive Diagnostics, Cardiovascular Health.},
        month = {May},
        }