A Smart IoT Gateway Capable of Prescreening for Atrial Fibrillation

Atrial fibrillation (AF) is a cardiac arrhythmia occurring when the atria lose their normal rhythm causing the heart to beat erratically. The estimated number of individuals with atrial fibrillation globally in 2010 was 33.5 million. Despite continued research in this area there is no universal standard for detecting atrial fibrillation. The majority of published detectors rely on manual classification techniques that are implemented on standalone devices. This paper proposes a dual convolutional neural network (CNN) based AF detection system. The proposed system transforms 5 s windows of electrocardiogram data to two-dimensional images via a stationary wavelet transform to serve as CNN inputs. The dual CNN system implements a model tailored for an IoT gateway device to prescreen arrhythmia cases locally. Less obvious arrhythmia cases are transferred to a secondary model hosted on a cloud server for further prediction. Local classification of AF reduces the overheads for cloud storage capacity and transfer of data. The proposed runtime system ultimately received an F1 score of 0.94 when evaluated using previously unseen data.