Automation of Digital Processing of Electrocardiogram Signals

Today, the main signal used to detect and diagnose any cardiovascular changes in the medical field is the electrocardiogram. However, various background noises that are added to the signal interfere with accurate diagnosis. In order to solve these problems, the development of a long-term ECG monitoring system, remote monitoring of patients, the widespread use of new technologies, and the collection of ECG data are of great importance for the early detection of heart diseases. In this scientific study, a personal cloud platform for data storage and a portable gadget for long-term monitoring of heart activity and ECG data collection were created. Wavelet Transform was used to filter and extract features to detect arrhythmia diseases from the collected ECG data. Highfrequency, short-duration signals (QRS complex and RR interval) were filtered and features extracted using Symlet4 wavelet transform, while low-frequency, long-duration signals (ST segment, P-wave, T-wave, PR interval, and QT interval) were filtered and features extracted using Coiflet2 wavelet transform. The findings showed that, compared with other components, Symlet4 produced higher values for QRS complex and RR interval in SNR and MSE calculations, while Coiflet2 produced higher values for ST segment, T-wave, P-wave, PR interval, and QT interval. Compared with other components, Symlet4 showed lower values for QRS complex and RR interval in RMSE calculations, while Coiflet2 showed lower values for ST segment, T-wave, P-wave, PR interval, and QT interval.

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