Random Forest-Based Classification of Heart Rate Variability Signals by Using
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Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features. Alan Jovic, Nikola Bogunovic Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia. Contents. Problem description Methods Feature extraction

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Alan jovic nikola bogunovic faculty of electrical engineering and computing

Random Forest-Based Classification of Heart Rate Variability Signals by UsingCombinations of Linear and Nonlinear Features

Alan Jovic, Nikola Bogunovic

Faculty of Electrical Engineering and Computing,

University of Zagreb, Croatia

Contents Signals by Using

  • Problem description

  • Methods

    • Feature extraction

    • Classification and evaluation

  • HRV records

  • Results

  • Discussion

  • Conclusion

Problem description
Problem description Signals by Using

  • Heart rate variability (HRV) analysis examinesfluctuations in the sequence of cardiac interbeat (RR)intervals

  • Each cardiac rhythm has a pattern (regular or irregular) in these RR interval fluctuations

  • HRV is a strong predictor of arrhythmic mortality

Problem description1
Problem description Signals by Using

  • Some rhythms have very similar patterns of HRV, e.g. normal and (accelerated) junctional rhythm

  • Some patterns cannot be efficiently detected by HRV analysis alone, e.g. bundle branch blocks, differentiating atrial disorders (atrial fibrillation vs. wandering atrial pacemaker)

  • Many rhythms and anomalies can be automatically detected and classified using HRV analysis alone

  • The main question are:

    • How accurately can a rhythm be classified?

    • What should the optimal length of the analyzed segment be?

    • Which features should be used for which type of rhythm?

Motivation Signals by Using

  • Nonlinear phenomena are involved in

    genesis of HRV:

  • Lots of nonlinear features described in

    literature, very few comparisons of

    different features’ combinations on

    the same dataset

  • The aim of this work is to

    evaluate a number of different

    combinations of (sometimes

    interrelated) linear and nonlinear HRV

    features in classification of several

    types of cardiac rhythms

Feature extraction
Feature extraction Signals by Using

  • We consider that a feature is linear if it is unable to take into account the nonlinear dynamics of the HRV signal

  • Examples of linear features include:

    • Time domain statistical and geometric measures

    • Frequency domain spectral features

  • Nonlinear features try to encompass and quantify the observed complexity of the HRV signal changes

  • Most of the employed nonlinear features make no assumptions on whether the changes are deterministic or stochastic in origin

  • Some of the features are specifically designed for HRV analysis, others have more broad areas of application

Feature extraction1
Feature extraction Signals by Using

Feature extraction2
Feature extraction Signals by Using

  • Most of the linear and nonlinear features were implemented in our own framework for HRV called ECG Chaos Extractor

  • The only exceptions were frequency domain features, which were extracted in Matlab using the autoregressive function

  • Some newly proposed nonlinear features

    • ASTA, Carnap 1D (tessellation) entropy – both methods require more elaborate further research

  • Not all of the nonlinear HRV features covered in literature were inspected (e.g. Lyapunov exponents, spectral entropy...)

Classification and evaluation
Classification and evaluation Signals by Using

  • For the best results on a large number of features, a strong classification algorithm is required

  • We opted for Random Forests (RF), an ensemble of random decision trees developed by Breiman in 2001

  • Internal mechanism for feature selection makes it a valuable tool in the case of a large number of potentially insignificant features

  • We have also tried other classifiers: ANN, SVM, and C4.5 decision tree, however none of the algorithms gives better results in terms of accuracy and speed

Classification and evaluation1

DATABASES Signals by Using




ECG Chaos Extractor

HRV annotations records


Classification accuracy


Classification and evaluation

  • RF was constructed with 40 trees for each feature scheme

  • Stratified 10x10-fold cross-validation evaluation procedure was executed

  • Analysis overview:

Hrv records
HRV records Signals by Using

  • Four types of cardiac rhythms (seven databases)

  • 500 RR intervals analyzed, with overlapping

  • A total of 2216 feature vectors

Results Signals by Using

Results Signals by Using

Results Signals by Using

Results Signals by Using

Discussion Signals by Using

  • Good performance was achieved with schemes: 4, 10, 3, and 9

  • The most promising combination is the one in scheme 4 consisting of the following features:

    • SDNN, pNN20, pNN50, RMSSD, HTI, PSD, VLF, LF, HF, LF/HF, SD1/SD2 ratio, Fano factor, Allan factor

  • High increase in the number of nonlinear features did not significantly improve classification accuracy

  • For further research, each segment should be labeled based on the beats or rhythm it contains, and not based on database it originated from

    • Improvement in accuracy is to be expected

    • Drawback is the time needed for careful labeling of the rhythms

  • Additional research is required for useful applicability of certain methods (ASTA, Carnap entropy)

Conclusion Signals by Using

  • Results suggest high efficiency of linear features in the classification problems

  • Some of the nonlinear features contribute to greater accuracy of the models

  • Random forest proved valuable for:

    • Finding the most relevant subset of features

    • Efficient classification of different cardiac rhythms

  • The authors recommend a combination of several time domain, frequency domain and nonlinear features for the best results on medium-sized HRV segments

Thank you
Thank you! Signals by Using

  • Questions?