An Approach of ECG Analysis for Diagnosis of Heart Diseases

Ekta Gajendra*, Jitendra Kumar**
* PG Scholar, Department of Communication Engineering, SSTC (SSGI), Bhilai, (C.G), India.
** Assistant Professor, Department of Electronics and Instrumentation, SSTC (SSGI), Bhilai, (C.G), India.
Periodicity:April - June'2016
DOI : https://doi.org/10.26634/jdp.4.2.5993

Abstract

In Today's World, heart problems are the major health concern people are facing. In order to prolong life, one must be fit and should keep a check on their health. Cardiac Arrhythmia is one such heart condition, which can be diagnosed from the persons ECG (Electrocardiogram). ECG is the graphical representation of the heart's electrical activity. Any change in the waveform of the ECG depicts change in the functioning of it, which can be used as a diagnostic measure. ECG Classification is done in 3 stages, first is de-noising of the ECG signal, in the second step, the authors perform feature Extraction and finally Classifies it. The authors use FIR Filter for signal de-noising and then, they have tried to do the analysis using DOM (Difference Operation Method) for feature extraction and LDA (Linear Discriminant Analysis) for the classification of ECG signal to predict if he/she is vulnerable to any such heart condition or not. On using the above named methods the authors achieve an accuracy of 98.077% and a sensitivity of 98.009%.

Keywords

Feature Extraction, Classification, DOM (Difference Operation Method), LDA (Linear Discriminant Analysis).

How to Cite this Article?

Gajendra,E., and Kumar,J. (2016). An Approach of ECG Analysis for Diagnosis of Heart Diseases. i-manager’s Journal on Digital Signal Processing, 4(2), 8-16. https://doi.org/10.26634/jdp.4.2.5993

References

[1]. Ouelli, A., Elhadadi, B. and Bouikhalene, B., (2014). “Multivariate Autoregressive Modeling for Cardiac Arrhythmia Classification Using Multilayer Perceptron Neural Networks”. IEEE, pp. 402-406.
[2]. Pathoumvanh, S., Hamamoto, K. and Indahak, P. (2014). “Arrhythmias Detection and Classification Base On Single Beat Ecg Analysis”. 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE). 978-1-4799-3854-4, pp. 1-4.
[3]. Ahmed, R., and Arafat, S., (2014). “Cardiac Arrhythmia Classification using Hierarchical Classification Model”. 6th International Conference on CSIT, pp. 203- 207.
[4]. Klaynin, P., Wongseree, W., Adisorn Leelasantitham, A., and Kiattisin, S., (2013). “An Electrocardiogram Classification Method based on Neural Network”. Biomedical Engineering International Conference, pp. 1- 4.
[5]. Kamaruddin, N.H., Murugappan, M., and Omar, M.I. (2012). “Early Prediction of Cardiovascular Diseases using ECG Signal: Review”. IEEE, Student Conference on Research and Development, pp. 48-53.
[6]. Das, M.K., Ghosh, D.K., and Ari, S., (2013). “Electrocardiogram (ECG) Signal Classification Using S-Transform, Genetic Algorithm And Neural Network”. IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems, pp. 353-357.
[7]. Bushra Mehdi, B., Tahmina Khan, T., and Ali, Z.A., (2013). “Artificial Neural Network Based Electrocardiography Analyzer”. IEEE, pp.1-7.
[8]. Faziludeen, S., and Sabiq P.V., (2013). “ECG Beat Classification Using Wavelets And SVM”. IEEE Conference on Information and Communication Technologies, pp. 815-818.
[9]. Abibullaev, B., Kang, W.S., Lee, S.H., and Jinung An, (2010). “Classification of Cardiac Arrhythmias Using Bi- Orthogonal Wavelet Preprocessing and SVM”. IEEE, pp. 1- 5.
[10]. Pingale, S.L., and Daimiwal, N., (2010). “Detection Of Various Diseases Using Ecg Signal In MATLab”. International Journal of Recent Technology and Engineering, Vol. 3, No. 1, pp. 1-5.
[11]. Mohamad, F.N., M. S. A. Megat Ali, A. H. Jahidin, M. F. Saaid, and M. Z. H. Noor, (2013). “Principal Component Analysis And Arrhythmia Recognition Using Elman Neural Network”. IEEE 4th Control and System Graduate Research Colloquium, pp. 19-20 .
[12]. Ali, M.S.A.M., Jahidin, A.H., and Norali, A.N. (2012). “Hybrid Multilayered Perceptron Network For Classification of Bundle Branch Blocks”. International Conference on Biomedical Engineering (ICOBE), pp. 149-154.
[13]. Rao, S., Jadhav. M., Nalbalwar, S. L., and Ghatol, A.A. “Generalized Feed forward Neural Network Based Cardiac Arrhythmia Classification From ECG Signal Data”. IEEE, pp. 351 – 356.
[14]. Karpagachelvi, S., Arthanari, M., and Sivakumar, M. (2010). “ECG Feature Extraction Techniques - A Survey Approach”. (IJCSIS), International Journal of Computer Science and Information Security, Vol. 8, No. 1.
[15]. Emanet, N. (2009). “ECG Beat Classification By Using Discrete Wavelet Transform And Random Forest Algorithm”. IEEE, ICSCCW 2009, pp. 1-4.
[16]. Khan, T.T., Sultana, N., Reza, R.B., and Mostafa, R. (2015). “ECG Feature Extraction in Temporal Domain and Detection of Various Heart Conditions”. ICEEICT, pp. 1-6.
[17]. Sahoo, S.K., Subudhi, A.K., Kanungo, B., and Sabut, S.K. (2015). “Feature Extraction of ECG Signal based on Wavelet Transform for Arrhythmia Detection”. IEEE, pp. 1-5.
[18]. Sujan, K.S.S., Priya, K.P., Pridhvi, R.S., and Ramana, R.V. (2015). “Performance analysis for the Feature extraction algorithm of an ECG signal”. ICIIECS.
[19]. Saminu, S., Özkurt, N., and Karaye, I.A. (2014). “Wavelet Feature Extraction for ECG Beat Classification”. IEEE 6th International Conference on Adaptive Science and Technology (ICAST), pp. 1-6.
[20]. Muthuvel, K., Suresh, L.P., Veni, S.H.K., Kannan, K.B. (2014). “ECG Signal Feature Extraction and Classification using Harr Wavelet Transform and Neural Network”, ICCPCT, pp. 1396-1399.
[21]. Biswas, U., Das, A., Debnath, S., and Oishee, I., (2014). “ECG Signal Denoising by Using Least-Mean- Square and Normalised-Least-Mean-Square Algorithm Based Adaptive Filter”. 3rd International Conference On Informatics, Electronics & Vision, pp. 1-6.
[22]. Zhang, N., Nie, Z., Luo, Y., Du, L., Wang, X., and Wang, L., (2014). “A Reconfigurable Overlapping FFT/IFFT Filter for ECG Signal De-noising”. International Symposium on Bioelectronics and Bioinformatics IEEE, pp. 1-4.
[23]. Biswas, U., and Maniruzzaman, M., (2014). “Removing Power Line Interference from ECG Signal Using Adaptive Filter and Notch Filter”. ICEEICT.
[24]. Hashim, F.R., Soraghan, J.J., Petropoulakis, L., and Daud, N.G.N., (2014). “EMG Cancellation from ECG Signals using Modified NLMS Adaptive Filters”. IEEE Conf. on Biomedical Engineering and Sciences (IECBES), pp. 735-739.
[25]. Gaikwad, K.M., and Chavan, M.S., (2014). “Removal of High Frequency Noise from ECG Signal Using Digital IIR Butterworth Filter”. GCWCN IEEE, pp. 121-124.
[26]. Niederhauser, T., Marisa, T., Kohler, L., Haeberlin, A., Wildhaber, R.A., Abächerli, R., Goette, J., Jacomet, M., and Vogel, R. (2015). “A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography”, IEEE Trans.Biomed.Circuits syst., pp. 255-265.
[27]. Gordillo, L.A., Medina-Santiago A. Zepeda- Hernandez J. Hernandez-De Leon H. Reyes Barranca M.A , (2014) . “An Adaptive Geometrically-Complemented Approach for ECG Signal Denoising”. CCE, pp. 1-6.
[28]. Bhogeshwar, S.S., Soni, M.K., Bansal, D., (2014). “Design of Simulink Model to denoise ECG signal using various IIR & FIR filters”. ICROIT.
[29]. Mehala, N., and Anand, (2013). “IIR Multiple Notch Filter Design for Power Line Interference Removal”. IJESE, Vol. 1, No. 10, pp. 65-68.
[30]. Chavan, M.S., Agarwala, R., and Uplane, M.D., (2008). “Design and implementation of Digital FIR Equiripple Notch Filter on ECG Signal for removal of Power line Interference”. WSEAS Transactions on Signal Processing, Vol. 4, No. 4, pp. 221-230.
[31]. Smart Cooky, (2014). World Heart Day 2014: Is India staring at a Heart Disease Epidemic.
[32]. Physionet.org. Physio Bank ATM. Retrieved from http://physionet.org/cgi-bin/atm/ATM
[33]. Yeh, Y.C., Wang, W.J., Chiou, C.W., (2009). “Cardiac Arrhythmia Diagnosis Method Using Linear Discriminant analysis on ECG signals”. Measurement, Vol. 42, pp. 778–789.
[34]. Lin, L.C., Yeh, Y.C., and Chu, T.Y., (2014). “Feature Selection Algorithm for ECG Signals and Its Application on Heartbeat Case Determining”. International Journal of Fuzzy Systems, Vol. 16, No. 4.
[35]. Hampton, J.R. The ECG Made Easy, Sixth Edition. pp. 7-12.
[36]. Hampton, J.R. The ECG in Practise, Fourth Edition, pp.1-3.
[37]. Phycionet.org. Physio Bank Annotations. Retrieved from https://www.physionet.org/physiobank/annotations. shtml
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.