Key result
The Real-time ECG signal monitoring system successfully detected 17 out of 20 abnormal ECG signals from the MIT-BIH database, achieving an overall accuracy of 85%.
Why the study?
Holter monitors require patients to carry the device for 24 hours to record and detect abnormal ECG signals, prompting the need for a real-time ECG signal monitoring system.
A proposed real-time ECG monitoring system using Zigbee wireless sensor networks and the Pan-Tomkins algorithm demonstrated 85% accuracy in detecting abnormal ECG signals from a standard database.
Answers are grounded in this paper and cite their sources.
May support wireless ECG system development; leaves open need for larger prospective validation before clinical use.
The Cardiovascular disease (CVD) is the most of death in the world. Electrocardiogram (ECG) is the graph that shows heart electrical activities. The physician record and detect the abnormal Electrocardiogram (ECG) signal by the Holter monitor that patient need to carry on the device for record ECG signal in 24 hours. Pan-Tomkins algorithm was appropriate for Real-time ECG signal recognition because high accuracy and rapidly analysis. This research propose the Real-time ECG Signal monitoring system for detect the abnormal ECG signal by using Pan-Tomkins algorithm with Wireless Sensor Network. The system separated into 2 part; sender module and receiver module. Experimental the system by using the ECG signal data from MIT-BIH database. Selected 20 samples of abnormal ECG signal then experimental at 10 and 20 meters sender module-receiver module distance, calculate R-R interval and R amplitude threshold The results show that the Real-time ECG signal monitoring system detect 17 abnormal ECG signal, the accuracy is 85%. This systems efficient for detect the abnormal of ECG signal in real-time.
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Muankid et al. (2019) studied Abnormal ECG signal (n=20). Real-time ECG Signal monitoring system was evaluated on Accuracy of detecting abnormal ECG signals. The Real-time ECG signal monitoring system successfully detected 17 out of 20 abnormal ECG signals from the MIT-BIH database, achieving an overall accuracy of 85%.
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