Adaptive Partition of ECG Diagnosis Between Cloud and Wearable Sensor Net Using Open-Loop and Closed-Loop Switch Mode

Abstract

Remote electrocardiogram (ECG) diagnosis with continuous real-time or near-real-time performance via a wireless wearable computing system would have significant value since it will enable on-time alerts and interventions, leading to life-saving outcomes. In this paper, a novel integration of open-loop and closed-loop switch modes is proposed, where the entire ECG diagnosis process is accomplished in three steps. First, the R-peak detection algorithm for initial diagnosis in local devices is executed, and 100% of the transmission energy is saved when no abnormality is detected. Second, in the case of an abnormality being detected, the edge device performs two-dimensional convolution neural network (2D-CNN) classification on the ECG signals, leading to either open-loop or closed-loop mode transmission based on the seriousness of the ECG signals. Third, in the cloud server, the received ECG signals may be further analyzed with a more sophisticated classification algorithm. The ECG classification accuracy ranges from 92.7 % to 99.1% depending on whether the analysis is executed locally (with reduced communication costs) or remotely (with increased computing resources). Overall, the ECG diagnosis process is partitioned into three components, including 1) irregular heartbeat detection, 2) 2-D CNN classification in the edge device, and 3) further classification in the cloud. The simulation results of such partition of ECG diagnosis in these layers supervised by the open-loop and closed-loop switch modes have demonstrated that the proposed system architecture can achieve efficient ECG diagnosis via wearable technologies with both reliable accuracies and reduced communication energy.