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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Review Article

Detection of Ventricular Arrhythmias using HRV Analysis and Quadratic Features

Author(s): Desh D. Gautam*, Vinod K. Giri and Krishn G. Upadhyay

Volume 13, Issue 6, 2020

Page: [847 - 855] Pages: 9

DOI: 10.2174/2352096512666191021112835

Price: $65

Abstract

Background: Ventricular Arrhythmias, one of the fatal heart diseases, requires timely recognition. The nonlinear and random nature of heart rate makes the diagnosis challenging.

Introduction: The research work in this paper is divided into three phases. In the first phase, classification of some of the ventricular arrhythmias is done in four classes as Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB) with some Normal (N) samples and the analysis of classifying algorithms to improve the classifiers accuracy. A Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), and K Nearest Neighbor (KNN) algorithms were used to train and test the classifier, with the help of online available MIT-BIH Arrhythmia Database. Then, in the second phase, the variance analysis of the data is carried out using Principle Component Analysis (PCA) to improve the classifier performance. In the last phase, the whole process is repeated after including Quadratic features with the best performing classifier only.

Methods: Signal processing, generation of Heart Rate Variability (HRV) signals from the available Electrocardiogram (ECG) signals and training, testing of ANN classifier was done in MATLAB environment, and the training and testing of SVM, and Random Forest classifier was done in R project software.

Results: Random Forest shows the best result among all classifiers with 86.11% accuracy, 87.1% after applying PCA with top 16 features, and 91.4% after including quadratic features with top 28 features.

Conclusion: The present study envisages helping ECG and HRV data analyses while selecting the AI techniques for classification purposes according to data.

Keywords: Heart Rate Variability (HRV), Electrocardiogram (ECG), ventricular arrhythmias, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), K Nearest Neighbor (KNN), Principle Component Analysis (PCA).

Graphical Abstract
[1]
K.H. Haugaa, T. Edvardsen, and J.P. Amlie, "Prediction of life-threatening arrhythmias-still an unresolved problem", Cardiology, vol. 118, no. 2, pp. 129-137, 2011.
[http://dx.doi.org/10.1159/000327093] [PMID: 21555886]
[2]
N. Alajlan, "Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods", Signal Image Video Process., vol. 8, no. 5, pp. 931-942, 2014.
[http://dx.doi.org/10.1007/s11760-012-0339-8]
[3]
H. Lee, "Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks", In: Nature Publishing Group. pp. 1-7, 2016
[http://dx.doi.org/10.1038/srep32390]
[4]
M.M. Rahhal, "Deep Learning Approach for Active Classification of Electrocardiogram Signals", In: , Information Sciences, vol. 345, . 2016, pp. 340-354
[5]
A. Jovic, and N. Bogunovic, "Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features", Artif. Intell. Med., vol. 51, no. 3, pp. 175-186, 2011.
[http://dx.doi.org/10.1016/j.artmed.2010.09.005] [PMID: 20980134]
[6]
B.M. Asl, S.K. Setarehdan, and M. Mohebbi, "Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal", Artif. Intell. Med., vol. 44, no. 1, pp. 51-64, 2008.
[http://dx.doi.org/10.1016/j.artmed.2008.04.007] [PMID: 18585905]
[7]
P. Taylor, N. Kannathal, U. R. Acharya, C. Lim, P. K. Sadasivan, and S. S. Iyengar, Intelligent automation & soft computing cardiac health diagnosis using heart rate variability signals a comparative study. pp. 3741. January 2015.
[8]
M. Ahmad, "Basheri, M. J. Iqbal and A. Rahim, “Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection", IEEE Access, vol. 6, pp. 33789-33795, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2841987]
[9]
A. Paul, D.P. Mukherjee, P. Das, A. Gangopadhyay, A.R. Chintha, and S. Kundu, "Improved Random Forest for Classification", IEEE Trans. Image Process., vol. 27, no. 8, pp. 4012-4024, 2018.
[http://dx.doi.org/10.1109/TIP.2018.2834830] [PMID: 29993742]
[10]
Y. Zhang, G. Cao, X. Li, and B. Wang, "Cascaded random forest for hyperspectral image classification", IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 11, no. 4, pp. 1082-1094, 2018.
[http://dx.doi.org/10.1109/JSTARS.2018.2809781]
[11]
M.V. Reddy, and R. Sodhi, "A modified S-transform and random forests-based power quality assessment framework", IEEE Trans. Instrum. Meas., vol. 67, no. 1, pp. 78-89, 2018.
[http://dx.doi.org/10.1109/TIM.2017.2761239]
[12]
Z. Xu, J. Chen, J. Xia, P. Du, H. Zheng, and L. Gan, "Multisource earth observation data for land-cover classification using random forest", IEEE Geosci. Remote Sens. Lett., vol. 15, no. 5, pp. 789-793, 2018.
[http://dx.doi.org/10.1109/LGRS.2018.2806223]
[13]
W. Lin, Z. Wu, L. Lin, A. Wen, and J. Li, "An ensemble random forest algorithm for insurance big data analysis", IEEE Access, vol. 5, pp. 16568-16575, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2738069]
[14]
V. Vijayakumar, M. Case, S. Shirinpour, and B. He, "Quantifying and characterizing tonic thermal pain across subjects from EEG data using random forest models", IEEE Trans. Biomed. Eng., vol. 64, no. 12, pp. 2988-2996, 2017.
[http://dx.doi.org/10.1109/TBME.2017.2756870] [PMID: 28952933]
[15]
C. Brueser, J. Diesel, M.D.H. Zink, S. Winter, P. Schauerte, and S. Leonhardt, "Automatic detection of atrial fibrillation in cardiac vibration signals", IEEE J. Biomed. Health Inform., vol. 17, no. 1, pp. 162-171, 2013.
[http://dx.doi.org/10.1109/TITB.2012.2225067] [PMID: 23086532]
[16]
J. Song, Z. Xie, J. Zhou, X. Yang, and A. Pan, Power quality indexes prediction based on cluster analysis and support vector machine CIRED - Open Access Proceed. J.. vol. 2017. 2017, no. 1, pp. 814-817.
[http://dx.doi.org/10.1049/oap-cired.2017.0120]
[17]
W. Jin, Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine CIRED - Open Access Proceed. J.. vol. 2017. 2017, no. 1, pp. 170-174.
[http://dx.doi.org/10.1049/oap-cired.2017.0541]
[18]
M. Uccellari, On the application of support vector machines to the prediction of propagation losses at 169 MHz for smart metering applications IET Microwaves, Antennas Propag.. vol. 12. 2018, no. 3, pp. 302-312.
[19]
H. El-Saadawy, M. Tantawi, H.A. Shedeed, and M.F. Tolba, "Hybrid hierarchical method for electrocardiogram heartbeat classification", IET Signal Process., vol. 12, no. 4, pp. 506-513, 2018.
[http://dx.doi.org/10.1049/iet-spr.2017.0108]
[20]
C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran, and R. Kumar, "ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications", IEEE Access, vol. 6, pp. 9767-9773, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2794346]
[21]
P. Cheng, and X. Dong, "Life-threatening ventricular arrhythmia detection with personalized features", IEEE Access, vol. 5, pp. 14195-14203, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2723258]
[22]
"F. A.-Atienza E. Morgado, L. F.-Martínez, A. G.-Alberola, and J.L.R.-Álvarez, “Detection of life-threatening arrhythmias using feature selection and support vector machines", IEEE Trans. Biomed. Eng., vol. 61, no. 3, pp. 832-840, 2014.
[http://dx.doi.org/10.1109/TBME.2013.2290800] [PMID: 24239968]
[23]
R. Mark, and G. Moody, MIT-BIH Arrhythmia Database 1997 [Online]. Available at, ecg.mit.edu/dbinfo.html
[24]
M.G. Poddar, V. Kumar, and Y.P. Sharma, "Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods", J. Med. Eng. Technol., vol. 39, no. 6, pp. 331-341, 2015.
[http://dx.doi.org/10.3109/03091902.2015.1063721] [PMID: 26171965]
[25]
U. Rajendra Acharya, P.S. Bhat, S.S. Iyengar, A. Rao, and S. Dua, "Classification of heart rate data using artificial neural network and fuzzy equivalence relation", Pattern Recognit., vol. 36, no. 1, pp. 61-68, 2003.
[http://dx.doi.org/10.1016/S0031-3203(02)00063-8]
[26]
G.B. Moody, and R.G. Mark, "The impact of the MIT-BIH arrhythmia database", IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45-50, 2001.
[http://dx.doi.org/10.1109/51.932724] [PMID: 11446209]
[27]
V. Vapnik, Statistical Learning Theory., Wiley: New York, 1998.
[28]
C. Cortes, and V. Vapnik, Support-Vector Networks, vol. 297, pp. 273-297, 1995.
[29]
I. Silva, and G. Moody, "An open-source toolbox for analysing and processing physionet databases in MATLAB and octave", J. Open Res. Softw.. vol. 2, no. 1, p. e27 , 2014
[http://dx.doi.org/10.5334/jors.bi]
[30]
A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.Ch. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, and H.E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals", Circulation, vol. 101, no. 23, pp. E215-E220, 2000.http://circ.ahajournals.org/content/101/23/e215.full
[http://dx.doi.org/10.1161/01.CIR.101.23.e215] [PMID: 10851218]
[31]
J. Yang, H. Singh, E.L. Hines, F. Schlaghecken, D.D. Iliescu, M.S. Leeson, and N.G. Stocks, "Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach", Artif. Intell. Med., vol. 55, no. 2, pp. 117-126, 2012.
[http://dx.doi.org/10.1016/j.artmed.2012.02.001] [PMID: 22503644]

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