ECG diagnosis for arrhythmia detection with a cloud-based service and a wearable sensor network in a smart city environment

Abstract

Irregular heartbeats are a primary indicator of Cardiovascular Disease (CVD), which is the leading cause of death in a developing smart city environment. Wearable devices can reliably monitor cardiac beats by producing Electrocardiogram (ECG) readings. The considerable value gained from a wireless wearable system allows for remote ECG assessment with continuous real-time functionality. The data collected from the wearable sensor network in the smart city platform gives timely alarms and treatment that could save lives. Cloud-based ECG methods can be accurate to a certain extent, as latency is still an existing problem. Cloud-based portals linked immediately to wearable devices can provide numerous advantages, such as reduced latency and a good level of service. Therefore, a novel cloud-based arrhythmia detection using the Recurrent Neural Network (RNN) (NC-RNN) method has been proposed for the ECG diagnosis with a wearable sensor in the smart city environment. The ECG signal collected from the wearable sensor involves three phase diagnosis stage. R-peak detection techniques are used for preliminary diagnostics in edge devices. The ECG signals are then classified using RNN at the edge device, with the severity of irregular beat detected in the ECG signal. Finally, a cloud platform classification method can evaluate the obtained ECG signals. While the proposed method's training session is runnable on the technically rich Cloud data centers, the interpretation unit is deployed over the cloud infrastructure for evaluating the ECG signals and setting off the emergency remedies with minimum latency. The simulation results of the suggested framework can accomplish effective ECG detection via wearable devices with high accuracy and less latency.