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Abstract
Coronary heart disease is the most deadly heart disease,so for that reason it’s needed early diagnosis of
this disease for medication efficacy. The one way of early diagnosis of Coronary Heart Disease done by a
cardiologist is through the record of electrocardiography (ECG).
Coronary heart disease detection can be automated by creating an ECG pattern recognition software.
This requirement needed a quite high accuracy to obtain accurate diagnose result. In this research,
implementation and performance analysis of RPROP(Resilient Propagation) method will be done to recognize
ECG pattern of coronary heart.
The result show that RPROP method give good performance in accuracy. The accuracy of ECG pattern
recognition reach 100% for training data set and 84.21% for testing data set .
Keywords: Electrocardiography (ECG), Artificial Neural Network, RPROP, Coronary Heart Disease
this disease for medication efficacy. The one way of early diagnosis of Coronary Heart Disease done by a
cardiologist is through the record of electrocardiography (ECG).
Coronary heart disease detection can be automated by creating an ECG pattern recognition software.
This requirement needed a quite high accuracy to obtain accurate diagnose result. In this research,
implementation and performance analysis of RPROP(Resilient Propagation) method will be done to recognize
ECG pattern of coronary heart.
The result show that RPROP method give good performance in accuracy. The accuracy of ECG pattern
recognition reach 100% for training data set and 84.21% for testing data set .
Keywords: Electrocardiography (ECG), Artificial Neural Network, RPROP, Coronary Heart Disease