Key result
An ICT-based clinical trial service platform was successfully developed to detect eight types of arrhythmia from wearable ECG data, demonstrating accurate data transfer without losses or delays.
Why the study?
Wearable electrocardiogram devices and patient monitoring systems have emerged to detect arrhythmias, prompting the need for a clinical trial service platform using self-reporting techniques for real-time arrhythmia detection.
Does an ICT-based clinical trial service platform accurately transfer and save wearable ECG and self-reported data for real-time arrhythmia detection?
Does an ICT-based clinical trial service platform accurately transfer and save wearable ECG and self-reported data for real-time arrhythmia detection?
An ICT-based clinical trial service platform was successfully developed to collect and analyze wearable ECG data and self-reported information for real-time arrhythmia detection.
BACKGROUND 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. Accordingly, several patient monitoring systems have been proposed to automatically detect arrhythmia using wearable ECG data. OBJECTIVE This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. METHODS A mobile application (app), used as an external network, a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed to establish a clinical trial service platform. RESULTS A single-channel, wearable ECG device was used on the platform. The acquired ECG data 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 repeatedly. The entire data, including the raw ECG, arrhythmia detection results, and self-reported data, were saved. The interworking experiment showed that data transfer and saving were performed accurately without losses or delays. CONCLUSIONS An ICT-based clinical trial service platform was developed to detect eight types of arrhythmia from data obtained by a wearable ECG device. The information of multiple participants can be easily collected using a self-reporting technique. These features enable early diagnosis, which is particularly of critical importance for treating painless, sparsely occurring arrhythmias. This platform has the potential to be utilized in decentralized clinical trials, telemedicine services, and for monitoring other diseases. CLINICALTRIAL None
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Kim et al. (2021) studied Arrhythmia. ICT-based clinical trial service platform with wearable ECG and self-reporting app was evaluated on Data transfer and saving accuracy. An ICT-based clinical trial service platform was successfully developed to detect eight types of arrhythmia from wearable ECG data, demonstrating accurate data transfer without losses or delays.
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