Premature ventricular contraction detection using artificial neural network developed in android application

We have conducted a study of detection system for premature ventricular contraction (PVC) developed in an android mobile phone. The system utilizes artificial neural network (ANN) with electrocardiographic (ECG) features of RR interval and QRS width. RR Interval and QRS width is Interval in ECG waveform. The algorithms of the detection are implemented using JAVA Eclipse Juno. The system is examined using electrocardiography of patients provided by Physionet MIT-BIH. The feature number is varied and the best result is found when both features RR interval and QRS width are applied with the performances of 94.58%, 96.59% and 96.29% in terms of sensitivity, specificity and accuracy.