<![CDATA[Heart disease is the number one cause of death in the world. Knowing the physiological condition of the heart is important to prevent and treat heart disease. The QRS point is a feature on the ECG signal that can be used to retrieve clinical data from cardiac signals. With the detection of QRS points, other components of the ECG signal can be found such as P, T waves, RR intervals, PR and others where these features are very important to know the physiological condition of a person's heart. This study was conducted to compare three QRS point detection algorithm performances, namely the Pan-Tompkins (PT), Stationary Wavelet Transform (SWT), and Adaptive Threshold (AT) algorithms in detecting QRS points on the ECG signal of patients with Premature Ventricula Contractions disease. (PVC), Premature Atrial Contractions, and Atrial Fibrillations (AFIB). The percentage of accuracy tests of the PT, SWT, and AT algorithms for PVC disease were 80.35%, 86.04%, and 62.66%, respectively. In PAC disease, it was 93.20%, 92.08%, and 84.89%. While testing for AFIB disease was 87.21%, 86.35%, and 69.1%, respectively. The PT algorithm is able to detect QRS points in PVC, PAC, and AFIB diseases with an average time of 0.726 while the SWT and AT algorithms are 0.714 and 0.709 seconds, respectively. The memory allocation used by the three algorithms to process PVC, PAC, and AFIB disease signals as a whole is 173.71 MB for PT, 174.74 MB for SWT, and 173.55 MB for the AT algorithm. Based on these results, the SWT algorithm is the best algorithm among the other three, although the results of the computational time and memory usage test are second and third.]]>
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