GARMA Modeling of ECG and Classification of Arrhythmia

Computer-assisted arrhythmia detection is crucial for the treatment of cardiac disorders. Electrocardiograms (ECG) are used to study the electric heart activity and diagnose abnormalities in the heart. It is a non-invasive method where the electric signal of the heart is captured through electrodes placed on the skin. In this study the ECG signals will be classified according to the heart abnormalities using the Generalized Autoregressive Moving Average (GARMA) and Generalized Linear Model (GLM). The ECG features were extracted using the GARMA model. The coefficients obtained from GARMA model will be classified using the GLM model to detect and classify into normal and five types of arrhythmias.

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