Open AccessA SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION
Author(s) -
Hao Wen,
Wenjian Yu,
Yuanqing Wu,
Shuai Yang,
Xiaolong Liu
Publication year - 2021
Publication title -
journal of mechanics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 1793-6810
pISSN - 0219-5194
DOI - 10.1142/s0219519421400212
Subject(s) - computer science , scalability , convolutional neural network , atrial fibrillation , wearable computer , artificial intelligence , pattern recognition (psychology) , machine learning , real time computing , data mining , medicine , embedded system , database
In this work, a scalable hybrid model is proposed for the purpose of screening and continuous monitoring of atrial fibrillation (AF) using electrocardiogram (ECG) signals collected from wearable ECG devices. The time series of RR intervals (with units in seconds) extracted from the ECG signal is fed into a recurrent neural network (RNN), and the bandpass filtered and scaled signal itself is fed into a convolutional neural network (CNN). At the post-processing stage, these two predictions are merged. An additional logistic regression model using statistical features of “pseudo” PR interval sequence is applied to aid making the final prediction. The proposed model is trained and validated on several datasets from PhysioNet and achieves a precision of 98.28% and a specificity of 99.82% on a dataset collected from several PhysioNet databases. This hybrid model has already been deployed through a WeChat applet, providing services those using wearable ECG devices, thus helping the screening and continuous out-of-hospital monitoring of the disease of AF.