Enhanced embedded system for various synthetic electrocardiogram generation using McSharry’s dynamic equation

Abstrak

n electrocardiogram (ECG) is a signal that describes the heart’s electrical activity. Signal processing techniques are necessary to extract meaningful information from ECG signals. Researchers often use large databases like the PhysioNet database to evaluate the performance of algorithms. However, these databases have limitations concerning the lack of temporal or morphological variations. This study addresses this limitation by introducing a synthetic ECG capable of producing both normal 12-lead ECG signals and abnormal ECG signals and implementing it into the microcontroller. The primary contribution involves developing a synthetic ECG model using McSharry's dynamic equation model and implementing it using Mikromedia 5 for STM32F4 Capacitive as a microcontroller. This model enables users to set the desired heart rate and accurately replicates ECG waveforms using parameters 𝑎𝑖, 𝑏𝑖, and 𝜃𝑖, each determines the peak’s magnitude, the peak’s time duration, and the angular velocity of the trajectory. The synthetic ECG was evaluated qualitatively and quantitatively, demonstrating waveform similarity to the ECG signals. This study implies that the synthetic ECG model serves as a valuable tool for researchers and practitioners in electrocardiography. It enables the generation of normal and abnormal ECG signals, aiding in algorithm development and potentially enhancing the understanding and diagnosis of heart conditions.