Classification of Normal and Pathological Heart Rate Variability Based on Daily ECG Monitoring Using Machine Learning Methods

This paper proposes heart rate variability analysis based on ECG data of the cardiovascular system in normal and pathological conditions by binary classification classical methods: Naive Bayes, Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting and Logistic Regression. The mean classification quality metrics such as the accuracy, the recall, the precision, the F1-score and the AUC score were obtained as a 10-fold cross-validation test. The electrocardiographic records of patients with normal sinus rhythm (the normal group), chronic heart failure of II and III classes according to the NYHA classification and ventricular extrasystole (the pathology group) from the open research resource PhysioNet became the data of the research. According to the results of the HRV analysis using binary classification methods, the most informative indicators for the cardiovascular pathology detection were determined. The constructed models were tested on the ECG of the Krasnoyarsk Territory patients and the classification quality of each model was evaluated. The best results were obtained by Logistic Regression, k-Nearest Neighbors, Gradient Boosting and Random Forest (indicators: SDANN, SDNN and IRRR).