<![CDATA[Premature Ventricular Contraction (PVC) Arrhythmia is a condition in which the heart beats irregularly. The PVC condition causes the heart to beat too fast or be too slow than it should be. PVC Arrhythmias appear in the lower part of the heart or commonly called the ventrical (chambers) of the heart. Patients with PVC Arrhythmia have a risk of experiencing heart failure, coronary heart disease, and other heart diseases if the PVC Arrhythmia condition occurs continuously. Currently, PVC Arrhythmia can only be detected in the hospital at a cost that is quite expensive. Therefore, research in detecting PVC Arrhythmias is needed in order to solve the cost problem required. There are several parameters that can be used in detecting PVC Arrhythmias, in this study the QRS Interval, QT Interval, and ST segment were selected as parameters. These parameters are in each cycle of the ECG signal which will be read using the AD8232 sensor. Reading the ECG signal requires 3 electrodes attached to the user's body. The placement of the electrodes is on the chest by attaching 2 electrodes and on the stomach by attaching 1 electrode. The ECG signal obtained by the AD8232 sensor will then be processed on the Arduino Uno to reduce the noise obtained and classified by the Support Vector Machine (SVM) method. The use of training data as much as 46 heart data with 23 types of PVC data and 23 types of normal data will be embedded in the SVM method. SVM classification testing was carried out with 20 cardiac data. Classification test results are in the form of class "Normal" or "PVC". Every 3 SVM classification results will be used to determine the type of heart condition. Types of heart conditions include "Normal", "Bigeminy", and "Trigeminy" which will later be shown on the LCD so that users know the condition of their heart. In testing the SVM accuracy level, it was obtained a value of 85% with 1.79 seconds of average training time and 2.49 seconds of average testing time.]]>
