No Access

Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression

Abdoul-Dalibou Abdou

UFR SET, Université de Thies, Thies, Sénégal

E-mail Address: abdouldalibou@gmail.com

Corresponding author.

Search for more papers by this author

Ndeye Fatou Ngom

Laboratoire Traitement de l’Inforrmation et Systémes Intelligents, Ecole Polytechnique de Thies, Thies, Sénégal

Search for more papers by this author

, and

Oumar Niang

Laboratoire Traitement de l’Inforrmation et Systémes Intelligents, Ecole Polytechnique de Thies, Thies, Sénégal

Search for more papers by this author

https://doi.org/10.1142/S1469026820500248Cited by:4 (Source: Crossref)

Abstract

In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. A. A. Dalibou, N. N. Fatou, S. Samba, N. Oumar, T. Abdoulaye and Cheikh H. T. C. Ndiaye, Neural networks for biomedical signals classification based on empirical mode decomposition and principal component analysis, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), Springer, 2016, pp. 267–278. Google Scholar
2. S. Ahmad, A. Lavin, S. Purdy and Z. Agha, Unsupervised real-time anomaly detection for streaming data, Neurocomputing 262 (2017) 134–147. Crossref, Google Scholar
3. R. Rodriguez, A. Mexicano, J. Bila, S. Cervantes and R. Ponce, Feature extraction of electrocardiogram signals by aplying adaptative threshold and principal component analysis, J. Appl. Res. Technol. 13 (2015) 261–269. Crossref, Google Scholar
4. B. Mohamed, A. Issam, A. Mohamed and B. Abdellatif, ECG image classification in real time based on the haar-like features and artificial neural networks, Int. Conf. Advanced Wireless, Information, and Communication Technologies (AWICT 2015), Vol. 73 (Elsevier, 2015), pp. 32–39. Crossref, Google Scholar
5. L. Mesin, Heartbeat monitoring from adaptively down samples electrocardiogram, Comput. Biol. Med. 84 (2017) 217–225. Crossref, Google Scholar
6. S. Sahoo, B. Kanungo, S. Behera and S. Sabut, Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities, Measurment 108 (2017) 55–66. Google Scholar
7. A. E. Fatin, N. Salim, A. R. Harris, T. T. Swee and T. Ahmed, Arrhythmia recognition and classification using combined linear and nonlinear feature of ECG signals, Comput. Methods Programs Biomed. 127 (2016) 52–63. Crossref, Google Scholar
8. V. Gupta and M. Mittal, KNN and PCA classifier with autoregressive modeling during different ECG signal interpretation, Procedia Comput. Sci. 125 (2018) 18–24. Crossref, Google Scholar
9. V. J. R. Ripoll, A. Wojdel, E. Romero, P. Ramos and J. Brugada, ECG assessment based on neural networks with pretraining, Appl. Soft Comput. 49 (2016) 399–406. Crossref, Google Scholar
10. A. Tjolleng, K. Jung, W. Hong, W. Lee, B. Lee, H. You, J. Son and S. Park, Classification of a driver’s cognitive workload levels using artificial neural network on ECG signals, Appl. Ergon. 59 (2017) 326–332. Crossref, Google Scholar
11. D. Hong, D. Zhao and Y. Zhang, The entropy and PCA based anomaly prediction in data streams, 20th Int. Conf. Knowledge Based and Intelligent Information and Engineering Systems Vol. 96 (Elsevier, 2016), pp. 139–146. Crossref, Google Scholar
12. J. Pagan, J. L. Risco-Martn, J. M. Moya and J. L. Ayala, Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases, J. Biomed. Inf. 62 (2016) 136–147. Crossref, Google Scholar
13. M. Hadjem, O. Salem and F. Nat-Abdesselam, An ECG monitoring system for prediction of cardiac anomalies using WBAN, IEEE 16th Int. Conf. e-Health Networking, Applications and Services (Healthcom) (HAL ARCHIVES-OUVERTES, 2014), pp. 441–446. Crossref, Google Scholar
14. F. Jin, L. Sugavaneswaran, S. Krishnan and V. S. Chauhan, Quantification of fragmented QRS complex using intrinsic time-scale decomposition, Biomed. Signal Process. Control 31 (2017) 513–523. Crossref, Google Scholar
15. S. Pal and M. Mitra, Empirical mode decomposition based on ECG enhencement and QRS detection, Comput. Biol. Med. 42 (2012) 83–92. Crossref, Google Scholar
16. O. Niang, A. Thioune, E. Delechelle and J. Lemoine, Spectral intrinsic decomposition method for adaptative signal representation, ISRN Signal Process. 9 (2012). Google Scholar
17. M. Langkvist, J. Jendeberg, P. Thunberg, A. Loutfi and M. Liden, Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks, Comput. Biol. Med. 97 (2018) 153–160. Crossref, Google Scholar
18. B. Al-Bander, W. Al-Nuaimy, B. M. Williams and Y. Zheng, Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc, Biomed. Signal Process. Control 40 (2018) 91–101. Crossref, Google Scholar
19. Y. Kim, Convolutional neural networks for sentence classification, Conf. Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, October 2014, pp. 1746–1751. Google Scholar
20. B. Pourbabaee, M. Howe-Patterson, E. Reiher and F. Benard, Deep convolutional neural network for ecg-based human identification, Can. Med. Biol. Eng. Soc. 41 (2018). Google Scholar
21. A. Y. Ziat, Apprentissage de reprsentation pour la prdiction et la classification de sries temporelles. Rseau de neurones, Universit Pierre et Marie Curie-Paris VI (2017). Google Scholar
22. U. Rajendra Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan and M. Adam, Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network, Inf. Sci. 405 (2017) 81–90. Crossref, Google Scholar
23. Y. Xiang, Z. Lin and J. Meng, Automatic QRS complex detection using two level convolutional neural network, BioMed Eng OnLine 17 (2018) 13. Crossref, Google Scholar
24. U. Rajendra Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan and M. Adam, Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Inf. Sci. 415–416 (2017) 190–198. Crossref, Google Scholar
25. P. Kora, Ch. Usha Kumari and K. Meenakshi, Heart arhythmia detection using wavelet coherence and firefly algorithm, Int. J. Comput. Appl 179 (2018) 0975–8887. Google Scholar
26. R. Nanjundegowda and V. Meshram, Arrhythmia detection based on hybrid features of T-wave in electrocardiogram, Int. J. Intell. Eng. Syst. 11 (2018) 153–162. Google Scholar
27. A. Turnip, M. Ilham Rizqywan, D. E. Kusumandari, M. Turnip and P. Sihombing, Classification of ECG signal with support vector machine method for arrhythmia detection, J. Phys. Conf. Ser. 970 (2018). Crossref, Google Scholar
28. R. Nanjundegowda and V. Meshram, Arrhythmia recognition and classification using kernel ICA and higher order spectra, Int. J. Eng. Technol. 7 (2018) 256–262. Crossref, Google Scholar
29. S. Sahoo, P. Das, P. Biswal and S. Sabut, Classification of heart rhythm disorders using instructive features and artificial neural networks, Int. J. Med. Eng. Inf. 10 (2018) 359–381. Google Scholar
30. Mr. R. R. Karhe and Ms. Bhagyashri Badhe, Arrhythmia detection using one dimensional convolutional neural network, Int. J. Eng. Technol. 5 (2018). Google Scholar
31. S. Stalin and S. Suresh, Biomedical arrhythmia heart diseases classification based on artificial neural network and machine learning approach, Int. J. Eng. Technol. 7 (2018) 10–14. Crossref, Google Scholar
32. M. Mitra, S. Chakrabarty, Md. S. Mia and A. Rahman, Identification of arrhythmia in electrocardiogram (ECG) using statistical tools and non-linear analysis, Int. J. Bioinf. Biomed. Eng. 4 (2018) 31–44. Google Scholar
33. M. Nazarahari, S. G. Namin, A. H. D. Markazi and A. K. Anaraki, A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification, Biomed. Signal Process. Control 20 (2015) 142–151. Crossref, Google Scholar