Analysis of Cardiovascular Disease Classification Through Deep Learning Approach

Padathala Visweswara Rao; Dr. Kamal Srivastava

doi:10.32628/CSEIT2410441

Authors

Padathala Visweswara Rao Research Scholar, Department of Computer Science and Engineering, Mansarovar Global University Madhya Pradesh, India Author
Dr. Kamal Srivastava Department of Computer Science and Engineering, Mansarovar Global University Madhya Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2410441

Keywords:

Deep Convolution Neural Network, Bird Swarm Algorithm

Abstract

Multiple cardiovascular disease classification from Electrocardiogram (ECG) signal is necessary for efficient and fast remedial treatment of the patient. This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network (CNN) where a modified ECG signal is given as an input signal to the network. Each ECG signal is first decomposed through Empirical Mode Decomposition (EMD) and higher order Intrinsic Mode Functions (IMFs) is combined to form a modified ECG signal. It is believed that the use of EMD would provide a broader range of information and can provide denoising performance. This processed signal is fed into the CNN architecture that classifies the record according to cardiovascular diseases using soft max regress or at the end of the network. It is observed that the CNN architecture learns the inherent features of the modified ECG signal better in comparison with the raw ECG signal. The method is applied on three publicly available ECG databases and it is found to be superior to other approaches in terms of classification accuracy. In MIT-BIH, St. Petersburg, PTB databases the proposed method achieves maximum accuracy of 0.9770, 0.9971 and 0.9871 respectively.

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References

S.P. Shashikumar, A.J. Shah, Q. Li, G.D. Clifford, S. Nemati, A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology, in: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 2017, pp. 141–144, http://dx.doi.org/10.1109/ BHI.2017.7897225. DOI: https://doi.org/10.1109/BHI.2017.7897225

L. Zhou, Y. Yan, X. Qin, C. Yuan, D. Que, L. Wang, Deep learning-based classification of massive electrocardiography data, in: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), IEEE, 2016, pp. 780–785, http://dx.doi.org/10.1109/IMCEC.2016.7867316. DOI: https://doi.org/10.1109/IMCEC.2016.7867316

T.J. Jun, H.J. Park, N.H. Minh, D. Kim, Y.-H. Kim, Premature ventricular contraction beat detection with deep neural networks, in: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2016, pp. 859–864, http://dx.doi.org/10.1109/ICMLA.2016.0154. DOI: https://doi.org/10.1109/ICMLA.2016.0154

C. Zhang, G. Wang, J. Zhao, P. Gao, J. Lin, H. Yang, Patient-specific ECG classification based on recurrent neural networks and clustering technique, in: 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), IEEE, 2017, pp. 63–67, http://dx.doi.org/10.2316/P.2017.852-029. DOI: https://doi.org/10.2316/P.2017.852-029

Z. Wu, X. Ding, G. Zhang, X. Xu, X. Wang, Y. Tao, C. Ju, A novel features learning method for ECG arrhythmias using deep belief networks, in: 2016 6thInternational Conference on Digital Home (ICDH), IEEE, 2016, pp. 192–196,http://dx.doi.org/10.1109/ICDH.2016.048. DOI: https://doi.org/10.1109/ICDH.2016.048

Y. Yan, X. Qin, Y. Wu, N. Zhang, J. Fan, L. Wang, A restricted Boltzman machine based two-lead electrocardiography classification, BSN (2015) 1–9,http://dx.doi.org/10.1109/BSN.2015.7299399. DOI: https://doi.org/10.1109/BSN.2015.7299399

C. Yuan, Y. Yan, L. Zhou, J. Bai, L. Wang, Automated atrial fibrillation detection based on deep learning network, in: 2016 IEEE International Conference on Information and Automation (ICIA), IEEE, 2016, pp. 1159–1164, http://dx.doi.org/10.1109/ICInfA.2016.7831994. DOI: https://doi.org/10.1109/ICInfA.2016.7831994

R. Sameni, M.B. Shamsollahi, C. Jutten, G.D. Clifford, A nonlinear Bayesian filtering framework for ECG denoising, IEEE Trans. Biomed. Eng. 54 (12)(2007) 2172–2185, http://dx.doi.org/10.1109/tbme.2007.897817. DOI: https://doi.org/10.1109/TBME.2007.897817

O. Sayadi, M.B. Shamsollahi, ECG denoising and compression using a modified extended Kalman filter structure, IEEE Trans. Biomed. Eng. 55 (9) (2008)2240–2248, http://dx.doi.org/10.1109/tbme.2008.921150. DOI: https://doi.org/10.1109/TBME.2008.921150

M.A. Kabir, C. Shahnaz, Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains, Biomed. Signal Process. Control 7(5) (2012) 481–489, http://dx.doi.org/10.1016/j.bspc.2011.11.003. DOI: https://doi.org/10.1016/j.bspc.2011.11.003

N. Nikolaev, Z. Nikolov, A. Gotchev, K. Egiazarian, Wavelet domain wiener filtering for ECG denoising using improved signal estimate, in: Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, IEEE, 2000, pp. 3578–3581, http://dx.doi.org/10.1109/ICASSP.2000.860175 (Cat. No. 00CH37100). DOI: https://doi.org/10.1109/ICASSP.2000.860175

O. Sayadi, M.B. Shamsollahi, ECG denoising with adaptive bionic wave lettr ans form, in: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2006, pp. 6597–6600, http://dx.doi.org/10.1109/IEMBS.2006.260897. DOI: https://doi.org/10.1109/IEMBS.2006.260897

A. Majumdar, R. Ward, Robust greedy deep dictionary learning for ECG arrhythmia classification, in: 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, 2017, pp. 4400–4407, http://dx.doi.org/10.1109/IJCNN.2017.7966413. DOI: https://doi.org/10.1109/IJCNN.2017.7966413

J. Macek, Incremental learning of ensemble classifiers on ECG data, in: 18thIEEE Symposium on Computer-Based Medical Systems (CBMS’05), IEEE, 2005,pp. 315–320, http://dx.doi.org/10.1109/CBMS.2005.69. DOI: https://doi.org/10.1109/CBMS.2005.69

S. Ha, J.-M. Yun, S. Choi, Multi-modal convolutional neural networks for activity recognition, in: 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2015, pp. 3017–3022, http://dx.doi.org/10.1109/SMC.2015.525. DOI: https://doi.org/10.1109/SMC.2015.525

B. Weng, M. Blanco-Velasco, K.E. Barner, ECG denoising based on the empirical mode decomposition, in: 2006 International Conference of the IEE Engineering in Medicine and Biology Society, IEEE, 2006, pp. 1–4, http://dx.doi.org/10.1109/iembs.2006.259340. DOI: https://doi.org/10.1109/IEMBS.2006.259340

N.E. Huang, An adaptive data analysis method for nonlinear and non stationary time series: the empirical mode decomposition and Hilbert spectral analysis, in: Wavelet Analysis and Applications, Springer, 2006, pp.363–376, http://dx.doi.org/10.1007/978-3-7643-7778-6 25. DOI: https://doi.org/10.1007/978-3-7643-7778-6_25

G. Rilling, P. Flandrin, P. Goncalves, et al., On empirical mode decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and ImageProcessing, vol. 3, NSIP-03, Grado (I) (2003) 8–11.

A. O. Boudraa, J.-C. Cexus, et al., Denoising via empirical mode decomposition, Proc. IEEE ISCCSP 4 (2006) 2006. DOI: https://doi.org/10.1109/ISSPA.2007.4555624

G. Singh, G. Kaur, V. Kumar, Ecg denoising using adaptive selection of IMFS through EMD and EEMD, in: 2014 International Conference on Data Science &Engineering (ICDSE), IEEE, 2014, pp. 228–231, http://dx.doi.org/10.1109/icdse.2014.6974643. DOI: https://doi.org/10.1109/ICDSE.2014.6974643

C. Orphanidou, T. Bonnici, P. Charlton, D. Clifton, D. Vallance, L. Tarassenko, Signal-quality indices for the electrocardiogram and photo plethysmogram: derivation and applications to wireless monitoring, IEEE J. Biomed. HealthInform. 19 (3) (2015) 832–838, http://dx.doi.org/10.1109/jbhi.2014.2338351. DOI: https://doi.org/10.1109/JBHI.2014.2338351

R. Bousseljot, D. Kreiseler, A. Schnabel, Nutzung der ekg-signal date n bank cardio datderptbüber das internet, Biomed. Techn./Bimed. Eng. 40 (s1)(1995) 317–318, http://dx.doi.org/10.1515/bmte.1995.40.s1.317[database].URL ttps://www.physionet.org/physiobank/database/ptbdb/. DOI: https://doi.org/10.1515/bmte.1995.40.s1.317

George B. Moody, R.G. Mark, The impact of the MIT-BIH arrhythmia database,IEEE Eng. Med. Biol. Mag. 20 (3) (2001) 45–50, http://dx.doi.org/10.1109/51.932724 (database), URL https://www.physionet.org/physiobank/database/mitdb/. DOI: https://doi.org/10.1109/51.932724

T. Viktor, A. Khaustov, St.-Petersburg Institute of Cardiological Technics 12 –lead arrhythmia database, Circulation-Electronic 101 (i23) (2000) e215–e220,URL https://www.physionet.org/physiobank/database/incartdb/ (database).

A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E.Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physio toolkit, and physionet: components of a new research resource for complex physiologic140 N.I. Hasan, A. Bhattacharjee / Biomedical Signal Processing and Control 52 (2019) 128–140signals, Circulation 101 (23) (2000) e215–e220, http://dx.doi.org/10.1161/01.CIR.101.23.e215 [database website].

H. Robbins, S. Monro, A stochastic approximation method, Ann. Math. Stat(1951) 400–407, http://dx.doi.org/10.1214/aoms/1177729586. DOI: https://doi.org/10.1214/aoms/1177729586

Dar, Jawad Ahmad, Kamal Kr Srivastava, and Sajaad Ahmed Lone. "Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost." Computers in Biology and Medicine 150 (2022): 106123. DOI: https://doi.org/10.1016/j.compbiomed.2022.106123

S. Banerjee, M. Mitra, Application of cross wavelet transform for ECG pattern analysis and classification, IEEE Trans. Instrum. Measure 63 (2) (2014)326–333, http://dx.doi.org/10.1109/TIM.2013.2279001. DOI: https://doi.org/10.1109/TIM.2013.2279001

S. Kiranyaz, T. Ince, R. Hamila, M. Gabbouj, Convolutional neural networks for patient-specific ECG classification, in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),IEEE, 2015, pp. 2608–2611, http://dx.doi.org/10.1109/EMBC.2015.7318926. DOI: https://doi.org/10.1109/EMBC.2015.7318926

Dar JA, Srivastava KK, Lone SA. Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomedical Signal Processing and Control. 2022 Sep 1;78:103905. DOI: https://doi.org/10.1016/j.bspc.2022.103905

E. Pasolli, F. Melgani, Active learning methods for electrocardiographic signal classification, IEEE Trans. Inf. Technol. Biomed. 14 (6) (2010) 1405–1416,http://dx.doi.org/10.1109/titb.2010.2048922. DOI: https://doi.org/10.1109/TITB.2010.2048922

Dar, J.A., Srivastava, K.K. & Lone, S.A. Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals. SN COMPUT. SCI. 3, 378 (2022). https://doi.org/10.1007/s42979-022-01264-0 DOI: https://doi.org/10.1007/s42979-022-01264-0

N. Hasan, Cardiovascular disease ̇ classification ̇ employing ̇ emd, ̇ URL https://github.com/NahianHasan/Cardiovascular Disease Classification EmployingEMD, (2019).