Development of Electrocardiogram Signal Generator for Fibrillation Detector - Ponco Siwindarto; Bill Jason; Adharul Muttaqin; Muhammad Nurrohman; Muhram Muis; Zainul Abidin (Journal of Information Technology and Computer Science, 2022)
Fibrillation is one of the abnormalities in the heart where it beats irregularly, which can cause sudden death. Fortunately, these abnormalities can be detected using an electrocardiogram (ECG) fibrillation detector. An ECG can be detected as biopotential body signal that arises as a result of electrical activity of the heart muscle represented by the electrical signals consisting of P, Q, R, S and T waves that carry information about someone’s heart condition through graph pattern. However, a test in humans with the potential for fibrillation is needed to determine whether the fibrillation detector works correctly. Testing this way is ineffective because we cannot predict when and where a person will experience fibrillation. On the other hand, a site called PhysioNet provides various medical records from patients, including ECG signals of normal heartbeats, atrial fibrillations, and ventricular fibrillations. Because of the ineffectiveness of testing a fibrillation detector directly on a living person, this study focused on research to develop an ECG signal generator using microcontroller Arduino Due for reconstructing the heart signal in normal and fibrillated heart conditions collected from the PhysioNet database. The research focused on obtaining an R-peak value and RR interval time that resembled a heart ECG wave. This study compared the reconstructed signal with references from PhysioNet and measured the peak level of R and R-R interval time using an oscilloscope to calculate the accuracy of the ECG signal generator. The generated ECG signal during Atrial Fibrillation and Normal Sinus Rhythm has an overall accuracy of 88.65% for the R-peak level and 97.24% for the RR-Interval time. While Ventricular Fibrillation has been reproduced by achieving amplitude errors of less than 5.63% and 10.22% during first and second samples, respectively.