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An Efficient Classifier-Based Approach for Early Arrhythmia Detection with Feature Reduction Using Ranker Search Algorithm

  • Monalisa Mohanty
  • Asit Kumar SubudhiEmail author
  • Pradyut Kumar Biswal
  • Sukanta Sabut
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)

Abstract

The increasing rate of sudden cardiac arrest (SCA) is a measure cause of death across the globe. The arrest may too take place with no forewarning; therefore, the primary estimation of “ventricular tachycardia” (VT) and “ventricular fibrillation” (VF) arrhythmia conditions is very much crucial for the precise detection of ventricular arrhythmia conditions. The present work aims at the detection and classification of VT and VF arrhythmias by extracting the temporal, statistical, and spectral features of the ECG signal. Two different databases from PhysioNet repository, i.e., CUDB and VFDB, were considered for evaluation of the proposed algorithms. For a 5 s window length, a total of 19 features were extracted. The ranking of the attributes has been by using correlation attribute evaluation method together with a ranker search algorithm. The normal sinus rhythm (NSR), VF, and VT arrhythmia rhythms are classified by J48 decision tree algorithm, SVM, MLP, and CVR classifiers. A total of 57 records of ECG signals have been evaluated to attain an accuracy of 97.73%, sensitivity of 92.78%, specificity of 98.11%, and precision of 96% using J48 decision tree algorithm, which gives better result compared to other three classifiers. This method may be served as an imperative decision support tool in precise recognition of ventricular cardiac arrhythmia.

Keywords

ECG signals Sudden cardiac arrest (SCA) Ventricular tachycardia (VT) Ventricular fibrillation (VF) Features Decision tree SVM 

References

  1. 1.
    Jekova I (2007) Shock advisory tool: detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomed Sig Process Control 2:25–33CrossRefGoogle Scholar
  2. 2.
    Li Q, Rajagopalan C, Clifford G (2014) Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng 61:1607–1613CrossRefGoogle Scholar
  3. 3.
    Clayton RH, Murray A, Campbell RWF (1994) Recognition of ventricular fibrillation using neural networks. Med Biol Eng Comput 32:217–220CrossRefGoogle Scholar
  4. 4.
    Khadra L, Al-Fahoum AS, Al-Nashash H (1997) Detection of life-threatening cardiac arrhythmias using the wavelet transformation. Med Biol Eng Comput 35:626–632CrossRefGoogle Scholar
  5. 5.
    Alonso-Atienza F, Morgado E, Fernandez-Martınez L, Alberola AG, Rojo-Alvarez JL (2012) Combination of ECG parameters with support vector machines for the detection of life-threatening arrhythmias. Comput Cardiol 39:385–388Google Scholar
  6. 6.
    Krasteva V, Jekova I (2005) Assessment of ECG frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias. Physiol Meas 26:707–723CrossRefGoogle Scholar
  7. 7.
    Zhang XS, Zhu YS, Thakor NV, Wang ZZ (1999) Detecting ventricular tachycardia and fibrillation by complexity measure IEEE Trans Biomed Eng 46:548–555Google Scholar
  8. 8.
    Karegowda AG, Manjunath AS, Jayaram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Tech Know Man 2:271–277Google Scholar
  9. 9.
    Panigrahi R, Borah S (2018) Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia Comput. Sci. 132:323–332CrossRefGoogle Scholar
  10. 10.
    Raji G, Chandra SCSV (2017) Long-term forecasting the survival in liver transplantation using multilayer perceptron networks. IEEE Trans Syst Man. Cybern 47:2318–2329CrossRefGoogle Scholar
  11. 11.
    Hou C, Nie F, Yi D, Wu Y (2013) Efficient image classification via multiple rank regression. IEEE Trans Image Proc 22:340–352MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Monalisa Mohanty
    • 1
  • Asit Kumar Subudhi
    • 1
    Email author
  • Pradyut Kumar Biswal
    • 2
  • Sukanta Sabut
    • 3
  1. 1.Department of Electronics and Communication, Institute of Technical Education & Research, SOA Deemed to be UniversityBhubaneswarIndia
  2. 2.Department of Electronics EngineeringIIIT BhubaneswarBhubaneswarIndia
  3. 3.School of Electronics EngineeringKIIT Deemed to be UniversityBhubaneswarIndia

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