An ICT-based clinical trial service platform was developed using a self-reporting technique and wearable ECG devices for real-time arrhythmia detection.
The developed platform integrates wearable ECG data with self-reported patient information to facilitate real-time arrhythmia detection in clinical trials.
The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.
Kim et al. (Sat,) conducted a other in Arrhythmias. ICT-based clinical trial service platform using a self-reporting technique was evaluated. An ICT-based clinical trial service platform was developed using a self-reporting technique and wearable ECG devices for real-time arrhythmia detection.