Key result
A cloud computing algorithm using Pan-Tompkins and Kim-Lee methods found a significant correlation between increased heart rate variability and cardiac arrhythmia (P<0.05).
Why the study?
Cardiovascular disease is a leading cause of death in Indonesia, but standard electrocardiograms limit patient mobility, prompting interest in tele-cardiology via cloud computing for remote arrhythmia detection.
A cloud computing algorithm implementing Pan-Tompkins and Kim-Lee algorithms successfully correlates heart rate variability parameters with cardiac arrhythmias, supporting remote tele-cardiology applications.
Hypothesis-generating for cloud HRV arrhythmia screening; prospective validation required before clinical use.
Cardiovascular disease in Indonesia has become one of the leading causes of death after complications from stroke and diabetes. Therefore, early detection of heart disease is important. Heart abnormalities can be detected using an electrocardiogram device. General electrocardiogram limits the patient’s mobility due to being wired with the instrument. The existence of an electrocardiogram with smart devices that can support cardiac arrhythmia detection is an innovation expected to improve patient comfort. The purpose of this study is to apply the principle of tele-cardiology that can be used for detecting cardiac abnormalities and to classify electrocardiogram data via cloud computing by implementing the developed Pan-Tompkins and Kim-Lee algorithm. In this research, we use a dedicated server after it was proven to be able to run in localhost and Node JS as the runtime-environment for running the application. The test datasets that have been chosen are from MIT-BIH Arrhythmia Database with 48 different recordings and Normal Sinus Rhythm Database with 18 different recordings for the Heart Rate Variability parameters to be extracted which are average heart rate, the standard deviation of average heart rate and root mean square of the average heart rate. Using linear regression method and testing with a 95% confidence interval, it is found that there is a significant correlation between heart rate variability increase and cardiac arrhythmia case where the value of the standard deviation of average heart rate being the most significant (α < 0.05). With this technology, it is expected to improve efficiency in proving cardiac arrhythmia and heart disease remotely.
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Setiawan et al. (2020) studied Cardiac arrhythmia (n=66). Cloud computing algorithm (Pan-Tompkins and Kim-Lee) was evaluated on Correlation between heart rate variability increase and cardiac arrhythmia case (p=< 0.05). A cloud computing algorithm using Pan-Tompkins and Kim-Lee methods found a significant correlation between increased heart rate variability and cardiac arrhythmia (P<0.05).
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