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Feature Selection for Arrhythmia Classification Using Statistical Tests

  • Conference paper
  • First Online:
MEDICON’23 and CMBEBIH’23 (MEDICON 2023, CMBEBIH 2023)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 93))

  • 148 Accesses

Abstract

In this paper, we evaluate the statistical significance of features enabling us to differentiate between the signals obtained from healthy patients and patients with some type of cardiac arrhythmia. The aim of our research is to obtain a unique feature subset from an original multi-domain feature set according to a filtering-based selection method, which selects the relevant features where the redundant and irrelevant features are removed. Feature selection was implemented us ing a statistical test appropriate for the feature distribution. When the normality assumption is satisfied, an unpaired t-test was performed, and an otherwise non-parametric Wilcoxon–Mann–Whitney test. Statistical based feature selection was performed by comparing ECG signals from the MIT-BIH Normal Sinus Rhythm (NSR) and MIT-BIH Arrhythmia (AR) Database.

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References

  1. Murakoshi, N., Aonuma, K.: Epidemiology of arrhythmias and sudden cardiac death in Asia. Circ J 77(10), 2419–2431 (2013)

    Article  Google Scholar 

  2. Gupta, V., Mittal, M., Mittal, V.: Chaos theory and ARTFA: emerging tools for interpreting ECG signals to diagnose cardiac arrhythmias. Wirel. Pers. Commun. 118, 3615–3646 (2021)

    Article  Google Scholar 

  3. Little, J.W., Falace, D.A., Miller, C.S., Rhodus, N.L.: Little and Falace’s Dental Management of the Medically Compromised Patient, 8th edn., pp. 67–80 (2013)

    Google Scholar 

  4. Lazaros, G., et al.: Incidence and prevalence of cardiac arrhythmias in pericardial syndromes. RCM 23(10), 347 (2022)

    Google Scholar 

  5. Faust, O., Acharya, U.R.: Automated classification of five arrhythmias and nor mal sinus rhythm based on RR interval signals. Expert Syst. Appl. 181, 115031 (2021)

    Google Scholar 

  6. Hammerer-Lercher, A., Namdar, M., Vuilleumier, N.: Emerging biomarkers for cardiac arrhythmias. Clin. Biochem. 75, 1–6 (2020)

    Article  Google Scholar 

  7. Sahoo, S., Dash, M., Behera, S., Sabut, S.: Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. Irbm 41(4), 185–194 (2020)

    Article  Google Scholar 

  8. Kavousi, M.: Differences in epidemiology and risk factors for atrial fibrillation be tween women and men. Front. Cardiovasc. Med. 7, 3. Feature Selection 11 (2020)

    Google Scholar 

  9. Dai, H., et al.: Global, regional, and national prevalence, incidence, mortality, and risk factors for atrial fibrillation, 1990–2017: results from the global burden of disease study 2017. EHJ-QCCO 7(6), 574–582 (2021)

    MathSciNet  Google Scholar 

  10. Albakri, A.: A meta-analysis of ECG abnormalities (Arrhythmias) in different types of heart failure. Integr. Mol. Med. 7, 1–10 (2020)

    Google Scholar 

  11. Patel, K.H., Hwang, T., Se Liebers, C., Ng, F.S.: Epicardial adipose tissue as a mediator of cardiac arrhythmias. Am. J. Physiol. Heart Circ. 322(2), H129-44 (2022)

    Google Scholar 

  12. Sahoo, S., Subudhi, A., Dash, M., Sabut, S.: Automatic classification of cardiac ar rhythmias based on hybrid features and decision tree algorithm. Int. J. Autom. Comput. 17(4), 551–561 (2020)

    Article  Google Scholar 

  13. Salau, A.O., Jain, S.: Feature extraction: a survey of the types, techniques, applications. ICSC 158–164 (2019)

    Google Scholar 

  14. Cai, J., Luo, J., Wang, S., Yang, S.: Feature selection in machine learning: a new perspective. Neurocomputing 300, 70–79 (2018)

    Article  Google Scholar 

  15. Li, J., et al.: Feature selection: a data perspective. CSUR 50(6), 1–45 (2017)

    Article  Google Scholar 

  16. Kim, Y.K., Lee, M., Song, H.S., Lee, S.W.: Automatic cardiac arrhythmia classification using residual network combined with long short-term memory. IEEE Trans. Instrum. Meas. 71, 1–7 (2020)

    Google Scholar 

  17. European Heart Network.: Atrial fibrillation and cardiovascular diseases—a European heart network paper (2015)

    Google Scholar 

  18. Bukata, I.T., Tegene, E., Gobena, T., Woldesenbet, Y.M.: Prevalence and determi nants of cardiac arrhythmias and conduction anomalies in adults aged ≥ 40 years in Jimma Town, Southwest of Ethiopia: a cross-sectional study. Afr. Health Sci. 22(1), 210–219 (2022)

    Article  Google Scholar 

  19. Ramkumar, M., Babu, C.G., Kumar, K.V., Hepsiba, D., Manjunathan, A., Kumar, R.S.: ECG cardiac arrhythmias classification using DWT, ICA and MLP neural networks. J. Phys. Conf. Ser. 1831(1), 012015 (2021)

    Article  Google Scholar 

  20. Ponsiglione, A.M., Amato, F., Romano, M.: Multiparametric investigation of dynamics in fetal heart rate signals. Bioengineering 9(1), 8 (2021)

    Article  Google Scholar 

  21. Task force of the European society of cardiology the North American society of pacing electrophysiology, heart rate variability. Circulation 93(5), 1043–1065 (1996)

    Google Scholar 

  22. Ishaque, S., Khan, N., Krishnan, S.: Trends in heart-rate variability signal analysis. Front. Digit. Health 3, 639444 (2021)

    Google Scholar 

  23. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Public Health Front. 5, 258 (2017)

    Article  Google Scholar 

  24. Smith, A.L., Owen, H., Reynolds, K.J.: Heart rate variability indices for very short term (30 beat) analysis, Part 1: survey and toolbox. J. Clin. Monit. Comput. 27(5), 569–576 (2013)

    Article  Google Scholar 

  25. Smith, A.L., Owen, H., Reynolds, K.J.: Heart rate variability indices for very short term (30 beat) analysis, Part 2: validation. J. Clin. Monit. Comput. 27(5), 577–585 (2013)

    Article  Google Scholar 

  26. Parsi, A., O’Loughlin, D., Glavin, M., Jones, E.: Prediction of sudden cardiac death in implantable cardioverter defibrillators: a review and comparative study of heart rate variability features. IEEE Rev. Biomed. Eng. 13(5), 5–16 (2019)

    Google Scholar 

  27. Parsi, A., Byrne, D., Glavin, M., Jones, E.: Heart rate variability feature selection method for automated prediction of sudden cardiac death. Biomed. Signal Process. Control 65, 102310 (2021)

    Article  Google Scholar 

  28. Goldberger, A.L., et al.: PhysioBank PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2020)

    Google Scholar 

  29. Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)

    Article  Google Scholar 

  30. Kleyko, D., Osipov, E., Wiklund, U.: A comprehensive study of complexity and performance of automatic detection of atrial fibrillation: classification of long ECG recordings based on the PhysioNet computing in cardiology challenge 2017. BPEX 6(2), 025010 (2020)

    Google Scholar 

  31. Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Methods Programs Biomed. 127, 52–63 (2016)

    Article  Google Scholar 

  32. Nascimento, N.M., Marinho, L.B., Peixoto, S.A., do Vale Madeiro, J.P., de Albuquerque, V.H., Filho, P.P.: Heart arrhythmia classification based on statistical moments and structural co-occurrence. CSSP 39(2), 631–50 (2020)

    Google Scholar 

  33. Azami, H., Mohammadi, K., Bozorgtabar, B.: An improved signal segmentation using moving average and Savitzky-Golay filter. J. Signal Process. Syst. 3, 39–44 (2012)

    Google Scholar 

  34. Godoy, M.F.: Nonlinear analysis of heart rate variability: a comprehensive review. J. Cardiol. Therapy 3(3) (2016)

    Google Scholar 

  35. Mishra, P., Pandey, C.M., Singh, U., Gupta, A., Sahu, C., Keshri, A.: Descriptive statistics and normality tests for statistical data. Ann. Card. Anaesth. 22(1), 67 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Amina Tihak, Amna Grahic & Dusanka Boskovic

Authors
  1. Amina Tihak
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  2. Amna Grahic
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  3. Dusanka Boskovic
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Correspondence to Amina Tihak .

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Editors and Affiliations

  1. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Almir Badnjević

  2. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Lejla Gurbeta Pokvić

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Tihak, A., Grahic, A., Boskovic, D. (2024). Feature Selection for Arrhythmia Classification Using Statistical Tests. In: Badnjević, A., Gurbeta Pokvić, L. (eds) MEDICON’23 and CMBEBIH’23. MEDICON CMBEBIH 2023 2023. IFMBE Proceedings, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-031-49062-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-49062-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49061-3

  • Online ISBN: 978-3-031-49062-0

  • eBook Packages: EngineeringEngineering (R0)

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