Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

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ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, methodology): “used SVM and the generalized discriminant analysis (GDA) feature reduction scheme to classify cardiac arrhythmia from the heart rate variability (HRV) signal [18].”
Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. (2016, methodology): “learning has been used for performing complex classification tasks in cardiology, including classification of constrictive pericarditis and restrictive pericarditis (6), classification of arrhythmia (7), quantitative prognosis of mortality in patients with heart failure (8), and for risk stratificat”
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, methodology): “LS-SVM is a well-known classifier and is used in the classification of many biomedical signals, like electrocardiograph (ECG) signals [70], heart rate variability signals [71], cardiac sound signals [72], brain MRI classification [73], EEG signals [74,75], etc.”
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning (2021, methodology): “[22] 2008 Generalised discriminant analysis and SVM were used for feature reduction and classification 27 Avendaño-Valencia et al.”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “[85] 6 HVR, GDA SVM Acc = 100% Ceylan et al.”