Publications by authors named "Surekha Bhusnur"
Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints.
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- The study of the heart is crucial for understanding human physiology, particularly in cardiovascular health; however, analyzing ECG signals is challenging due to interference from noise and artifacts in real recordings.* -
- The paper presents a method for modeling ECG signals using parametric quartic splines and creating a new dataset, while also evaluating the performance of different machine learning techniques for classifying normal and abnormal sinus rhythms.* -
- Through rigorous quality assessment, including power spectrum and cross-correlation analysis, the research demonstrates that synthetic ECG signals closely resemble real ones, and highlights the high classification accuracies achieved by Decision Tree, Logistic Regression, and Gradient Boosting algorithms.*
This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing.
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