1. Extraction of nonlinear features from biomedical time-series using HRVFrame framework Analysis of cardiac rhythm records using HRVFrame framework and Weka platform HRVFrame Alan Jović 1, Nikola Bogunović 1, Goran Krstačić 2 1 Department of Electronics, Microelectronics, Computer and Intelligent Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia, {alan.jovic, nikola.bogunovic}@fer.hr 2 Institute for Cardiovascular Diseases and Rehabilitation, Draškovićeva 13, 10000 Zagreb, goran.krstacic@zg.t-com.hr Selection of features and feature parameters Feature calculation Storing feature vectors in.arff file Cardiac rhythm records in textual format Extracted feature vectors in.arff file Knowledge discovery platform Input data:  Cardiac rhythm (R peaks) in format supported by PhysioNet (peak times, beat and rhythm annotations) Output data:  Extracted feature vectors stored in.arff format  Modeling supported by several KD platforms (Weka, RapidMiner) Implementation:  Open-source, standalone framework, implemented in Java  More than 30 feature extraction methods, including traditional linear time domain and frequency domain features  A large variety of time-series variability measures with special focus on nonlinear features for scientific explorations  Framework is easily upgradable to accomodate new features Phase space features: Correlation dimension D 2, largest Lyapunov exponent, spatial filling index, central tendency measure, SD1/SD2, cardiac sympathetic index, cardiac vagal index, recurrence plot features Entropy measures: Approximate entropy, sample entropy, spectral entropy, corrected conditional Shannon entropy, Rényi entropy, multiscale sample entropy, alphabet entropy Fractal properties: Detrended fluctuation analysis, Hurst exponent, Higuchi fractal dimension Other nonlinear features: Sequential trend analysis, advanced sequential trend analysis, Allan factor, multiscale asymmetry indeks, Lempel-Ziv complexity Supported nonlinear features Applications of nolinear features analysis Applicable to ECG, EEG, cardiac rhythm, EMG, gait dynamics, pulse oxymetry, galvanic skin resistance, temperature, respiration, BP,… 1.Classification – cardiac arrhythmias [Acharya 2004, Asl 2008], neurology (e.g. schizophrenia [Hornero 2006], epilepsy [Liang 2010]) 2.Time-series modeling – discovering novel nonlinear properties of biomedical time-series, e.g. multifractality [Ivanov 1999], transition to chaos [Weiss 1999], decreased complexity [Goldberger 1996], examining applicability conditions, e.g. [Seker 2000, Protopopescu 2005] 3.Prediction – prediction of disorder (re)occurence, e.g. VFIB [Varostos 2007], mortality [Huikuri 2000], risk stratification [Syed 2011], next interval prediction [Bezerianos 1999] Purpose and applications:  Currently supports analysis of cardiac disorders using heart rate variability (HRV) signal (cardiac rhythm analysis)  Facilitates comparison of models proposed by researchers  Possible application in on-line arrhythmia detection system  Future applications in other biomedical time-series domains Framework overview