Analisis Sinyal ECG (Electrocardiogram) pada Aktivitas Duduk dan Berdiri dengan Metode HRV (Heart Rate Variability) pada Domain Waktu - Jane Sihotang; Harlianto Tanudjaja; Kumala Indriati; Sung-Nien Yu (Jurnal Elektro, 2019)
SVM is one of machine leaning is used for pattern recognition classification. In this research, linear SVM methods will be applied to analyze multivariable classification. As an input signal the PQRST signal is used from the measurement results of the ECG cardiac activity device. ECG signals in this study were processed using HRV methods in the time domain. Classification is used to compare ECG signals from measurement result in sitting and standing activities. As a tool designed a heart rate monitoring tool (ECG) with a processor using the Arduino UNO module and the AD8232 ECG amplifier module . The results of ECG signal extraction by the HRV method obtained 11 ECG features (variables). In the traning data contain 10 data and one other file containing 4 ECG data for testing data. In the data transfer process there are 110 features of traning data and 44 features in testing data. The results of the traning data have 100 % accuracy, so that it will procedure a hyperplane that is able to separate the two classes sitting and standing. From the results of testing with the hyperplane the system can classify properly.