Key result
A combined approach using Generalized Autoregressive Moving Average (GARMA) and Generalized Linear Models (GLM) was proposed to classify ECG signals into normal and five types of arrhythmias.
GARMA and GLM models can be utilized for feature extraction and classification of ECG signals to detect arrhythmias.
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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Raach et al. (2018) studied Arrhythmia. Generalized Autoregressive Moving Average (GARMA) and Generalized Linear Model (GLM) was evaluated on Classification of ECG signals into normal and five types of arrhythmias. A combined approach using Generalized Autoregressive Moving Average (GARMA) and Generalized Linear Models (GLM) was proposed to classify ECG signals into normal and five types of arrhythmias.
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