Extraction of nonlinear features from biomedical time-series using HRVFrame framework

1. Alan Jović1, Nikola Bogunović1, Goran Krstačić2 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 Storing feature vectors in .arff file Feature calculation Knowledge discovery platform Cardiac rhythm records in textual format Extracted feature vectors in .arff file Extraction of nonlinear features from biomedical time-series using HRVFrame framework HRVFrame Supported nonlinear features Framework overview Input data: • Cardiac rhythm (R peaks) in format supported by • PhysioNet (peak times, beat and rhythm annotations) Analysis of cardiac rhythm records using HRVFrame framework and Weka platform 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 D2, largest Lyapunov exponent, spatial filling index, central tendency measure, SD1/SD2, cardiac sympathetic index, cardiac vagal index, recurrence plot features Output data: • Extracted feature vectors stored in .arff format • Modeling supported by several KD platforms (Weka, • RapidMiner) Entropy measures: Approximate entropy, sample entropy, spectral entropy, corrected conditional Shannon entropy, Rényi entropy, multiscale sample entropy, alphabet entropy 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 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 • Applications of nolinear features analysis • Applicable to ECG, EEG, cardiac rhythm, EMG, gait dynamics, pulse oxymetry, galvanic skin resistance, temperature, respiration, BP,… • Classification – cardiac arrhythmias [Acharya 2004, Asl 2008], neurology (e.g. schizophrenia [Hornero 2006], epilepsy [Liang 2010]) • 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] • Prediction – prediction of disorder (re)occurence, e.g. VFIB [Varostos 2007], mortality [Huikuri 2000], risk stratification [Syed 2011], next interval prediction [Bezerianos 1999]