Methods Machine Learning Methods for Classifying Eeg Based on Rhythmic and Morphological Features
The article presents an experimental study of the effectiveness of machine learning methods for classifying electrocardiographic signals by rhythmic and morphological features using information tech- nology based on the mathematical apparatus of cyclic random processes. The problem of automated detection of atrial arrhythmias is considered, particularly atrial fibrillation and atrial flutter, which are characterized by complex changes in both ECG wave morphology and cardiac cycle time intervals.