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One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.

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Authentication and Identification is primary part of biometric technology. Currently, electrocardiogram (ECG) is not only being used as a diagnostic tool for clinical purposes, but also as a new biometric tool for high level security system because of its liveliness and uniqueness that is hard to imitate and manipulate. There are many fiducial (signal mark) that is classified from ECG morphology (QRS Complex, P, T waves) has already been researched for this purpose. For non fiducial, many researches are focus on dynamic character from heartbeat (ECG Signal). Heart Rate Variability (HRV) analysis is part of non fiducial classifier. This paper reviews Heart Rate Variability analysis (time and frequency domain) as part of multi matches, one of scenario from multimodal biometric. Sample of person’s heartbeat signal is taken from ECG Database MIT-BIH (MIT and Harvard) and the result of every parameter will be analyzed by Biometric Performance Standards Tools (ISO/IEC IS 19795-1) such as: False Non-Match Rate (FNMR), False Match Rate (FMR) and Thresholds EER (Equal Error Rate). Analysis should show accuracy of multi matches Heart Rate Variability (HRV). As integrator tool, LabView is used to collect offline ECG, process the data and generate HRV Analysis.