BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD
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Heart disease is one disease with high mortality rate in the world. Based on WHO records from 112 countries at 2004, the rate is 29% of all deaths each year. Medical devices are necessary to diagnose one's health as an indication of a disease. Nowadays, Indonesia still imports medical devices, for the diagnosis of heart failure, from abroad. This research aims to assist the monitoring of
cardiac patients with bradycardia and tachycardia appearances of message condition patient's heart rate at the same time. The results were displayed with the output of bradycardia condition of the heart rate (heart rate less than 60 beats per minute) or tachycardia (heart rate over 100 beats per minute). The system displayed the data read from the heart to the PC embedded system to monitor the condition of the patients under decisions based on backpropagation neural network. Classification system could be performed quite well, training data and by testing the 10 pieces, the optimal weight gain was 1727 iteration, the learning rate was 0.1122, and the error was below 0.001 (0.0009997).
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