Atrial fibrillation detection using Poincare geometry and heart beat intervals

Abstract Detection of atrial fibrillation (AF) remains one of the major concerns in the field of medical research. AF is one of the main cause for stroke. AF is characterized by irregular heartbeats and absence of P waves in electrocardiogram (ECG) signal. In this article, we propose a method, combining Poincare plot derived and RR interval‐based features to classify given ECG signal into normal, AF and other rhythms. Classification and regression tree, K‐nearest neighbor, support vector machine, artificial neural network, ResNet18, convolutional neural network (CNN)‐long short term memory (LSTM) are implemented for classification of ECG signal. The class specific accuracies for the three rhythms are computed. Physionet challenge 2017 database is used for evaluation and testing of the developed algorithm. The database has 5154 normal, 771 AF, 2557 other rhythm and 46 noisy signals. Three Poincare derived features viz: SD1, SD2, and ratio of SD1 to SD2, three RR interval features viz: Mean stepping increment of RR interval, approximate entropy and sample entropy are computed and are given to classifiers. During fivefold cross‐validation, CNN‐LSTM classifier showed best result with class specific accuracy for normal of 96.65%, AF of 97.55%, other rhythms of 94.87%, overall accuracy of 96.17% and F 1 score of 0.9589. The developed technique can bring change in conventional practice in AF diagnosis and can aid the physician as an assisted tool.