The use of entropy of recurrence microstates and artificial intelligence to detect cardiac arrhythmia in ECG records

Cardiac arrhythmia is a common clinical problem in cardiology defined as the abnormality in heart rhythm. Bradycardia, atrial fibrillation, tachycardia, supraventricular tachycardia, atrial flutter and sinus irregularity are common different classifications for arrhythmia. In this study, we develop a new approach to distinguishing between these most common heart rhythms. Our approach is based on dynamical system techniques, namely recurrence entropy of microstates, and recurrence vicinity threshold, in association with artificial intelligence. The results are based on a 12-lead electrocardiogram open dataset with more than 10,000 subjects which includes 11 different heart rhythms. The rhythms and other cardiac conditions of the dataset were labeled by more than one licensed physician. The main contributions of this work are the identification of how different heart rhythms affects the entropy of recurrence microstates and recurrence vicinity threshold parameter, and in doing so, this quantifier may be used as a feature extraction to artificial intelligence classifiers. We expect that our freely available methodology and our algorithm will be useful to communities where real-time physician diagnostics are not easily available. We show the 12 signals arising from ECG (12×5000) data points can be pre-treated using the entropy of recurrence microstates and recurrence threshold, so that only 12×2 scalar values may be used in machine learning techniques. So our method involves a significant reduction of the data set to be analyzed by machine learning algorithms and can bring benefits in situations of pre-testing individuals, due to the minimum processing time and hardware required to perform the analysis. The additional information obtained by the two quantifiers may also be put together with the signals, consolidating data from multiple sources, adding more useful information to the dataset.