1. HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm Alan Jovic and Nikola Bogunovic Faculty of Electrical Engineering and Computing, University of Zagreb

2. Motivation Lack of agreement among experts upon the best heart rate variability (HRV) features used to classify cardiac arrhythmias Problem of results comparison:  Different datasets  Different features -> particularly problematic – lots of proposed features!  Different evaluation metrics What are the limits of HRV analysis for classification of cardiac rhythms and cardiac diseases?

3. Research goals Systematize existing HRV features Implement the features in a modular and easily upgradable framework Facilitate comparison of scientific work in biomedical time-series variability modeling Extract HRV features for automatic arrhythmia and heart diseases classification using freely available knowledge discovery platforms

4. Framework overview Input: PhysioNet format (R peak times) Selection: GUI-based selection of extraction parameters and features Calculation: more than 30 linear time, frequency, time-frequency, and nonlinear features Output: feature vectors in.arff file -> Weka, RapidMiner Cardiac rhythm records in textual format Selection of features and features’ parameters Feature calculation Storing feature vectors in.arff file Extracted feature vectors in.arff file Knowledge discovery platform HRVFrame

5. Comparison Other frameworks  ECGLab - Matlab (ECG+RR): linear, time-frequency, few nonlinear features  KARDIA - Matlab (RR); linear, few non-linear features  BioSig - C++/Matlab (EEG+RR); linear features only, aim is standardization of biomed. series processing tools and file formats Advantages of HRVFrame  Implementation of numerous nonlinear features  Preparation for data mining of cardiac disorders and arrhythmias  Java-based, platform-independent  Easily upgradeable to include additional novel HRV features  Modifiable for analysis of other biomedical time-series  Free for non-commercial purposes

6. Thank you!