Authors:
Gopisetty Ramesh,Donthi Satyanarayana,Maruvada Sailaja,DOI NO:
https://doi.org/10.26782/jmcms.spl.3/2019.09.00019Keywords:
Accuracy,Cardiac Arrhythmia,Detection Rate,DTCWT,ECG,MCSVM,SA,Abstract
Detection of abnormalities in the ECG signal to achieve an automatic diagnosis of several heart related diseases has become an increased research aspect. This paper focused to develop an automatic detection system to detect abnormalities in ECG. These abnormalities results in different cardiac arrhythmias. Towards the detection of different cardiac arrhythmias, this paper analyzed the ECG signal through Dual Tree Complex Wavelet Transform (DTCWT) as a feature extraction technique and further proposed a new selective band coding technique to extract only the informative features from the sub bands obtained from DTCWT. The novelty of this proposed system is to remove the redundant information, thereby achieving a fast and accurate detection results. Multi-Class Support Vector Machine (MC-SVM) is used for classification purpose. Extensive simulations are carried out for the MITBIH database and the performance is measured through the performance metrics such as Accuracy, Precision, Recall, False Positive Rate, F-Measure and overall computational time. The proposed method is also compared with conventional approaches to alleviate the performance enhancement in the detection of Cardiac Arrhythmias (CAs) with less time span.Refference:
I. A.Ebrahimzadeh, B.Shakiba, A.Khazaee, “Detection of electrocardiogram
signals using an efficient method” Applied Soft Computing, Vol.: 22, pp.
108–117 (2014).
II. A.E.Fatin, “Arrhythmia recognition and classification using combined linear
and nonlinear features of ECG signals” Computer Methods and Programs in
Biomedecine, Vol.:127, pp. 52–63 (2016).
III. A.E.Zadeh, A.Khazaee, V.Ranaee, “Classification of the electrocardiogram
signals using supervised classifiers and efficient features” Computer Methods
and Programs in Bio medicine, Vol.:99, pp. 179–194 (2010).
IV. A.Mert, N.Kilic, A.Akan, “Evaluation of bagging ensemble method with
time-domain feature extraction for diagnosing of arrhythmia beats” Neural
Computing and Applications, Vol.:24, pp. 317–326 (2014).
V. C.C.Lin, & C.M.Yang, “Heartbeat classification using normalized RR
intervals and morphological features” Mathematical Problems in
Engineering, Vol.:1, pp. 1–11 (2014). 2014
VI. C.K.Chua, “Cardiac health diagnosis using higher order spectra and support
vector machine” Open Medical Informatics Journal, Vol.:3, pp. 1–8 (2010).
VII. C.P.Shen, “Detection of cardiac arrhythmia in electrocardiograms using
adaptive feature extraction and modified support vector machines” Expert
Systems with Applications, Vol.:39, pp. 7845–7852 (2012).
VIII. C.W.Hsu, C.C.Chang, C.J. Lin “A Practical Guide to Support Vector
Classification” Department of Computer Science National Taiwan
University, Taipei 106, Taiwan, 2016.
IX. E.D.Ubeyli, “ECG beats classification using multiclass support vector
machines with error correcting output codes” Digital Signal Processing,
Vol.:17, pp. 675–684 (2007).
X. F.Alonso-Atienza, “Detection of life-threatening arrhythmias using feature
selection and support vector machines” IEEE Trans. Biomed. Eng., Vol.:61,
pp. 832–840 (2014).
XI. F.K.Aya, I.O.Mohamed, A.Y.Inas, “A novel technique for cardiac arrhythmia
classification using spectral correlation and support vector machines” Expert
Systems with Applications, Vol.:42, pp. 8361–8368 (2015).
XII. G.Ramesh, D.Satyanarayana, M.Sailaja,“ECG Signal Enhancement through
Subband Adaptive Soft Thresholding and EMD for Efficient Cardiac
Arrhythmia Analysis” International Journal of Intelligent Engineering and
Systems, Vol.: 11, Issue:5, 2018.
XIII. [3] H.Khamis, R.Weiss, Y.Xie, C.W.Chen, N.Lovell, S.Redmond, “QRS
detection algorithm for tele-health electrocardiogram recordings” IEEE
Transactions on Biomedical Engineering, Vol.:63, pp. 1377–1388, 2016.
XIV. H.Q.Li, “Arrhythmia classification based on multi-domain feature extraction
for an ECG recognition system” Sensors, Vol.:16, pp. 1–16(2016).
XV. H.Q Li, “Heartbeat classification using different classifiers with non-linear
feature extraction” Transactions of the Institute of Measurement and Control,
Vol.: 38, pp. 1033–1040 (2016).
XVI. J.J.Zhu, L.S.He, Z.Q.Gao, “Feature extraction from a novel ECG model for
arrhythmia diagnosis” Biomedical Materials and Engineering, Vol.:24, pp.
2883–2891 (2014).
XVII. K.Aik, W.Seng, G.Nanyang, S.M.Kuo, “Subband Adaptive Filtering Theory
and Implementation” Wiley and Sons publishers, 2009.
XVIII. L.C.Lin, Y.C.Yeh, T.Y.Chu, “Feature selection algorithm for ECG signals
and its application on heart beat case determining” International Journal of
Fuzzy Systems, Vol.:16, pp. 483–496 (2014).
XIX. Li, P. F. et al. “High-performance personalized heartbeat classification model
for long-term ECG signal” IEEE Trans. Biomed. Eng., Vol.:64, pp. 78–86
(2017).
XX. M.Elgendi, “Fast QRS detection with an optimized knowledge based method:
evaluation on 11 standard ECG databases” PLoS One, Vol.: 8, article e73557,
2013.
XXI. M.K.Song, S.E.Kim, Y.S.Choi, W.J.Song “A Selective Normalized Subband
Adaptive Filter Exploiting an efficient subset of Sub bands” Proc. of
European Signal Processing Conference (EUSIPCO), 2011.
XXII. M.R.Homaeinezhad, “ECG arrhythmia recognition via a neuro-SVM-KNN
hybrid classifier with virtual QRS image-based geometrical features” Expert
Systems with Applications, Vol.: 39, 2047–2058 (2012).
XXIII. M.S.E.Abadi, M.S.Shafiee, “A New Variable Step-Size Normalized Subband
Adaptive Filter Algorithm Employing Dynamic Selection of Subband Filters”
Proc. of International Conf. On Electrical Engineering (ICEE), 2013.
XXIV. M.Thomas, M.K.Das, S.Ari, “Automatic ECG arrhythmia classification using
dual tree complex wavelet based features” International Journal of
Electronics and Communications, Vol.:69, pp. 715–721 (2015).
XXV. N.Acir, “A support vector machine classifier algorithm based on a
perturbation method and its Application to ECG beat recognition systems”
Expert Systems with Applications, Vol.:31, pp. 150–158 (2006).
XXVI. N.Acir, “Classification of ECG beats by using a fast least square support
vector machines with a Dynamic programming feature selection algorithm”
Neural Computing and Applications, Vol.:14, pp. 299–309 (2005).
XXVII. N.Kingsbury, “Complex wavelets for shift invariant analysis and filtering of
signal,” Applied and Computational Harmonic Analysis, Vol.:10, pp. 234-
253, 2010.
XXVIII. O.Sayadi, M.B.Shamsollahi, G.D.Clifford, “Robust detection of premature
ventricular contractions using a wave-based Bayesian framework” IEEE
Trans. Biomed. Eng., Vol.:57, pp. 353–362 (2010).
XXIX. P.Sharma, K.C.Ray, “Efficient methodology for electrocardiogram beat
classification” IET Signal Processing, Vol.:10, pp. 825–832 (2016).
XXX. R.Benali, F. B. Reguig, Z. H. Slimane, “Automatic classification of
heartbeats using wavelet neural network” J. Med. Syst. , Vol.: 36, pp. 883–
892 (2012).
XXXI. R.Ceylan, Y.Ozbay, “Comparison of FCM, PCA and WT techniques for
classification ECG arrhythmias using artificial neural network”. Expert
Systems with Applications, Vol.:33, pp. 286–295 (2007).
XXXII. R.Chen, D.Y.Sun, D.T.Qin, F.B.Hu “A novel engine identification model
based on support vector machine and analysis of precision-influencing
factors” Journal of Central South University (Science and Technology), Vol.:
41, Issue: 4, pp.1391–1397, 2010.
XXXIII. R.J.Martis, “Characterization of ECG beats from cardiac arrhythmia using
discrete cosine transform in PCA framework” Knowledge-based Systems,
Vol.:45, pp. 76–82 (2013).
XXXIV. R.J.Martis, “Computer aided diagnosis of atrial arrhythmia using
dimensionality reduction methods on transform domain representation”.
Biomedical Signal Processing and Control, Vol.:13, pp. 295–305 (2014).
XXXV. R.Mark, G.Moody MIT-BIH Arrhythmia Database. Available:
http://ecg.mit.edu/dbinfo.html, (1997, May)
XXXVI. S.Osowski, T.H.Linh, “ECG beat recognition using fuzzy hybrid neural
network” IEEE transactions on Biomedical Engineering, Vol.: 48, Issue: 11,
pp. 1265–1271, 2001.
XXXVII. U.R.Acharya, P.S.Bhat, S.S.Iyengar, A.Rao, S.Dua, “Classification of heart
rate data using artificial neural network and fuzzy equivalence relation,”
Pattern Recognition, Vol.: 36, Issue:1, pp. 61–68, 2003.
XXXVIII. V.Kalpana, S.T.Hamde, L.M.Waghmare, “ECG feature extraction using
principal component analysis for studying the effect of diabetes” J. Med.
Eng. Technol., Vol.:37, pp. 116–126 (2013).
XXXIX. Y.Can, B.V.K.V.Kumar, M.T.Coimbra, “Heartbeat classification using
morphological and dynamic features of ECG signals” IEEE Trans. Biomed.
Eng, Vol.:59, pp. 2930–2941 (2012).
XL. Y.C.Yeh, W.J.Wang, “QRS complexes detection for ECG signal: the
difference operation method” Computer Methods and Programs in
Biomedicine, Vol.: 91, Issue: 3, pp. 245–254, 2008.
XLI. Y.Kutlu, D.Kuntalp, “A multi-stage automatic arrhythmia recognition and
classification system” Computers in Biology and Medicine. Vol.:41, pp. 37–
45(2011).
XLII. Y.Kutlu, D.Kuntalp, “Feature extraction for ECG heartbeats using higher
order statistics of WPD coefficients” Computer Methods and Programs in
Biomedicine, Vol.:105, pp. 257–267 (2012).