Multimedia Tools and Applications

, Volume 77, Issue 17, pp 21905–21922 | Cite as

Arrhythmia classification based on wavelet transformation and random forests

  • Guolin Pan
  • Zhuo Xin
  • Si Shi
  • Dawei JinEmail author


Cardiovascular disease accompanied by arrhythmia reduces an individual’s lifespan and health, and long term ECG monitoring would generate large amounts of data. Fortunately, arrhythmia classification assisted by computer science would greatly improve the efficiency of doctors’ diagnoses. However, due to individual differences, noise affecting the signal, the great variety of arrhythmias, and heavy computing workload, it is difficult to implement these advanced techniques for clinical context analysis. Thus, this paper proposes a comprehensive approach based on discrete wavelet and random forest techniques for arrhythmia classification. Specifically, discrete wavelet transformation is used to remove high-frequency noise and baseline drift, while discrete wavelet transformation, autocorrelation, principal component analysis, variances and other mathematical methods are used to extract frequency-domain features, time-domain features and morphology features. Furthermore, an arrhythmia classification system is developed, and its availability is verified that the proposed scheme can significantly be used for guidance and reference in clinical arrhythmia automatic classification.


Arrhythmia classification Wavelet transformation Autocorrelation Random forests 



This work was supported in part by the National Social Science Foundation of China under Grant 13CTJ003 and in part by the China Postdoctoral Science Foundation under Grant 2014M562025.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Economics and ManagementChina University of GeosciencesWuhanChina
  2. 2.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  3. 3.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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