# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
###### Abstract  
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. electrocardiogram; arrhythmia; information and communication technology; wearable device; mobile application; self-report; clinical-trial platform +
Footnote †: journal: Applied Sciences
arrhythmias. However, it is difficult to recognize the clinical manifestations of arrhythmias in a timely manner owing to their asymptomatic characteristics [6]. Electrocardiograms (ECGs) have been extensively used to diagnose arrhythmias because the traces contained therein (waves) and heart rates (HRs) can reflect disease states [7; 8; 9]. Over the past decades, Holter recordings have been considered the gold standard for long-term ECG monitoring [10; 11]. However, direct wired connection of the Holter monitor with electrodes may cause inconvenience to patients [12]. Furthermore, real-time arrhythmia detection is not feasible because the measured data are analyzed offline [13; 14]. Thus, this method is not appropriate for detecting intermittent and short-lasting arrhythmias [15]. Recent advancements in information and communications technology (ICT) have promoted the development of various wearable devices. These technical achievements allow patients to measure ECG and easily evaluate their cardiovascular health in their daily lives [16; 17]. Lightweight devices with a one-lead configuration have shown performances comparable with those of conventional multilead devices [18]. Frequent self-examinations with these devices have been reported to improve AF detection [19; 20]. Several algorithms and platforms have been developed to detect arrhythmia with the use of a wearable ECG device [6; 7; 12; 21]. Recently, arrhythmia-detection algorithms based on machine-learning techniques have been proposed, and several studies have shown competitive detection capabilities through them [6; 7; 12]. However, the number of detectable arrhythmias was limited, and their performance has not been evaluated in long-term monitoring settings. A wireless data-acquisition platform was proposed for long-time wearable ECG collection and transmission to the hospital. However, inspection and confirmation of the data by clinicians was essential to ensure proper treatment [21]. Furthermore, other health information was not achievable with this platform. It is almost impossible to review numerous ECG data generated by wearable devices. Thus, an advanced arrhythmia-detection system using a self-reporting technique is required.