Algorithm of monitoring (detecting) electrocardiographic QRS complex occurring in arrhythmia patient

At present, electrocardiogram (ECG) is a very useful tool in identifying arrhythmia. ECG automatic detection algorithm has now become the mainstream in academic research. The QRS complex corresponds to the rapid depolarization of left and right ventricles. The left and right ventricles are more muscular than the atrium, thus, the amplitude of QRS complex is larger than the P-wave. Therefore, the primary goal of ECG Automatic Detection is to identify the QRS complex, and then the heart rate variability (HRV) analysis can be performed using the R-R interval in QRS complex. The HRV can be used to detect arrhythmia and also to help diagnose whether there is heart-related disease or not. In this paper, we propose a highly reliable real-time embedded device which is different from the Pan-Tompkins algorithm for QRS detection. The ECG signal is pre-processed by the gradient and threshold to detect the features of QRS complex. The QRS complex morphology is then used to further determine feature. Finally in this paper, a set of user interface detection tools is developed. Several QRS complex detection algorithm based on the first derivative methods are integrated to facilitate the evaluation and testing of the algorithm. The MIT-BIH arrhythmia database was used as the standard in verifying this algorithm, the sensitivity of 93.21%, and the accuracy of 89.28% was achieved. This proved that the algorithm proposed in this paper conforms to the standards of American Heart Association.

Keyword:Signal processing, Electrocardiograms, QRS complex automatic detection, Gradient threshold

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Recommended citation: Cheng-Yan Guo., Advisor: Shi-Ming Lin. (2017). “Algorithm of monitoring (detecting) electrocardiographic QRS complex occurring in arrhythmia patient.” Master’s thesis.