Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum.

Abstract

Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a convolutional neural network (CNN) was proposed, and particularly two cardiac rhythm features of R-R intervals and F-wave frequency spectrum were combined into the CNN for a good applicability of mobile application. Over 23 patients' ten-hours of Electrocardiogram (ECG) records were collected from the MIT-BIH database, and each of which was segmented into 10s-data fragments to train the designed CNN and evaluate the performance of the proposed method. Specifically, a total of 83,461 fragments were collected, 49,952 fragments of which are the normal fragments (type-N) and the others are the AF fragments. As results, the obtained average accuracy of the proposed method combining the two proposed features is 97.3%, which is shown a relative higher accuracy comparing with either that of the detection with the feature of R-R intervals (95.7%) or that with the feature of F-wave frequency spectrum (93.9%). Additionally, the sensitivity and the specificity of the present method are both of a high level of 97.4% and 97.2%, respectively. In conclusion, the CNN based approach by combining the R-R interval series and the F-wave frequency spectrum would be effectively to improve the performance of AF detection. Moreover, the proposed classification of AF with 10s-data fragments also could be potentially useful for a wearable real-time monitoring application for a pre-hospital screening of AF.