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

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Presentation on theme: "HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm Alan Jovic and Nikola Bogunovic Faculty of Electrical Engineering and Computing,"— Presentation transcript:

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!


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