Accuracy and Characterization of Electrocardiographic Signal from Mobile Biomedical Sensor Praditya Rizky Rahmansyah (a), Siti Nurul Khotimah (a*), Freddy Haryanto (a), Ridwan Sofyansyah (b)
a) Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung Jalan Ganesha 10, Bandung 40132, Indonesia
* nurul[at]itb.ac.id
b) PKPN Clinic, Jalan Ciledug 79, Garut 44114, Indonesia
Abstract
Based on the Global Burden of Disease and the Institute for Health Metrics and Evaluation 2014-2019, heart disease is the highest cause of death in Indonesia. One way to deal with this disease is through early detection by reading the electrical signals of the heart. Therefore, the technology for recording the electrocardiographic signal is developing rapidly. Recently, there have been many uses of biomedical sensors to record the electrical activity of the heart by utilizing internet facilities and without cables. This study discusses the accuracy and characterization of cardiac signals recorded by the portable KardiaMobile 6L against the Fukuda M.E Cardisuny type C100 clinical electrocardiograph (ECG). The data are taken for each patient, with a total of 9 patients using both devices simultaneously. ECG signals from the two devices are digitized using a web plot digitizer with time interval of 0.0001 s to obtain the RR interval values for each lead, except lead III which the amplitude is very low. The clinical ECG produces 6 ECG signals (as short data). Meanwhile, the KardiaMobile produces ECG signals for 30 seconds (as long data- and five sequential ECG signals can be sampled as short data). Accuracy is done through linear regression, percent difference, and root mean squared error (RMSE) for the heart rate (HR) displayed by the two devices and RR interval from ECG signals. They provide excellent goodness of fit measures for the linear regression. The percent difference is still in the reliability of the device. The RMSE is very low. Characterization of ECG signals is done by t-test between two array RR interval data from two leads for the same device. Using KardiaMobile, the RR interval value of short data is not significantly different from long data for a subject with normal sinus rhythm. The RR interval value of long data between two leads is not significantly different, likewise for short data between two leads. Using clinical ECG, the RR interval value of short data between two leads is not significantly different for a subject with normal sinus rhythm. Therefore, KardiaMobile has an accuracy similar with a clinical electrocardiograph in determining HR and is effective for analyzing dynamic changes based on RR intervals.
Keywords: Electrocardiographic signals- Accuracy- Mobile biomedical sensor- Clinical ECG- Characterization
