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Very deep feature extraction and fusion for arrhythmias detection

  • S.I. : Deep Learning for Biomedical and Healthcare Applications
  • Published:
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Abstract

The electrocardiogram (ECG) is a picture of heart electrical conduction, which is widely used to diagnose many types of diseases such as abnormal heartbeat rhythm (arrhythmia). However, it is very difficult to detect the abnormal ECG characteristics because of the nonlinearity and the complexity of ECG signals from one side, and the noise effect of these signals from the other side, which make it very difficult to perform direct information extraction. Therefore, in this study we propose a very deep convolutional neural network (VDCNN) by using small filters throughout the whole net to reduce the noise affect and improve the performance. Our approach introduces multi-canonical correlation analysis (MCCA), a method to learn selective adaptive layer’s features such that the resulting representations are highly linearly correlated and speed up the training task. Moreover, the Q-Gaussian multi-class support vector machine (QG-MSVM) is introduced for classification, an algorithm which has a better learning performance and generalization ability on ECG signals processing. As a result, we come up with expressively more accurate architecture which is able to differentiate between the normal (NSR) heartbeats and three common types of arrhythmia atrial fibrillation (A-Fib), atrial flutter (AFL), and paroxysmal supraventricular tachycardia (PSVT) without performing any noise filtering or pre-processing techniques. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods.

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References

  1. Arrhythmia Irregular Heartbeat Center (2017) Heart disease and abnormal heart rhythm (arrhythmia). https://www.medicinenet.com/arrhythmia_irregular_heartbeat/article.htm. Accessed 12 Nov 2017

  2. National Heart, Lung and Blood Institute (2017) How is heart failure diagnosed? https://www.nhlbi.nih.gov/health/health-topics/topics/hf/diagnosis. Accessed 12 Nov 2017

  3. Atrial Fibrillation ECG Review (2017) https://www.healio.com/cardiology/learn-the-heart/ecg-review/ecg-topic-reviews-and-criteria/atrial-fibrillation-review. Accessed 12 Nov 2017

  4. Healthline (2017) Atrial flutter. https://www.healthline.com/health/heart-disease/atrial-flutter#overview1. Accessed 12 Nov 2017

  5. Medications for Paroxysmal Supraventricular Tachycardia (2017) https://www.drugs.com/condition/paroxysmal-supraventricular-tachycardia.html. Accessed 12 Nov 2017

  6. Sahoo S et al (2016) De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding. Procedia Technol 25:68–75

    Article  Google Scholar 

  7. De Albuquerque VHC et al (2016) Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput Appl 1:1–15

    Google Scholar 

  8. Acharya UR et al (2016) Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2612-1

    Article  Google Scholar 

  9. Ebrahimzadeh A et al (2016) Classification of ECG signals using hermite functions and MLP neural networks. J AI Data Min 4(1):55–65

    Google Scholar 

  10. Alfaro-Ponce M et al (2017) Automatic detection of electrocardiographic arrhythmias by parallel continuous neural networks implemented in FPGA. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3051-3

    Article  Google Scholar 

  11. Andersen RS et al (2017) A novel approach for automatic detection of atrial fibrillation based on inter beat intervals and support vector machine. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2039–2042

  12. Garcia G et al (2017) Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Sci Rep 7(1):10543

    Article  Google Scholar 

  13. Desai U, Martis RJ, Acharya UR, Nayak CG, Seshikala G, Ranjan SK (2016) Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. J Mech Med Biol 16(1):1640005

    Article  Google Scholar 

  14. Acharya UR, Fujita H, Adam H, Oh SL, Tan JH, Sudarshan VK, Koh JEW (2016) Automated characterization of Arrhythmias using nonlinear features from tachycardia ECG beats. In: IEEE international conference on systems, man, and cybernetics

  15. Acharya D et al (2017) Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 405:81–90

    Article  Google Scholar 

  16. Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process A Rev J 16(3):275–287

    Article  Google Scholar 

  17. Zubair M, Kim J, Yoon CW (2016) An automated ECG beat classification system using convolutional neural networks. In: IEEE 6th international conference on IT convergence and security

  18. Acharya D et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396

    Article  Google Scholar 

  19. Goldberger AL et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  Google Scholar 

  20. Acharya D et al (2017) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2017.09.017

    Article  Google Scholar 

  21. An X et al (2014) A deep learning method for classification of EEG data based on motor imagery. ICIC 8590:203–210

    Google Scholar 

  22. Sun Y et al (2014) Deep learning face representation from predicting 10,000 classes. In: 2014 IEEE conference on computer vision and pattern recognition, pp 1891–1898

  23. Hinton G et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(2012):82–97

    Article  Google Scholar 

  24. Wang X et al (2017) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66:763–776

    Google Scholar 

  25. Burlina P et al (2017) Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 82:80–86

    Article  Google Scholar 

  26. Greenspan H et al (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35:1153–1159

    Article  Google Scholar 

  27. Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969

    Article  Google Scholar 

  28. Ravì D et al (2017) Deep learning for health informatics. IEEE J Biomed Health Inf 21:4–21

    Article  Google Scholar 

  29. Havaei M et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Article  Google Scholar 

  30. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of international conference learning represent. (ICLR), San Diego, CA, pp 1–14

  31. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS, pp 1106–1114

  32. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) OverFeat: integrated recognition, localization and detection using convolutional networks. In: Proceedings of ICLR

  33. Nogueira RF, de Alencar Lotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213

    Article  Google Scholar 

  34. Rodriguez R et al (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 13(2):261–269

    Article  Google Scholar 

  35. Amrani M (2017) Deep feature extraction and combination for synthetic aperture radar target classification. J Appl Remote Sens 11(4):1

    Article  Google Scholar 

  36. Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst Appl 47:23–34

    Article  Google Scholar 

  37. Hammad M, Wang K (2017) Fingerprint classification based on a Q-Gaussian multiclass support vector machine. In: Proceedings of the 2017 international conference on biometrics engineering and application. ACM

  38. Silva L et al (2010) Reconstruction of multivariate signals using q-Gaussian radial basis function network. IEEE Comput Cardiol 2010:465–468

    Google Scholar 

  39. Tsallis C (1994) What are the numbers that experiments provide?. Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro

    Google Scholar 

  40. Isin Ali, Ozdalili Selen (2017) Cardiac arrhythmia detection using deep learning. Procedia Comput Sci 120:268–275

    Article  Google Scholar 

  41. Luo K et al (2017) Patient-specific deep architectural model for ECG classification. J Healthc Eng 2017:1–13

    Google Scholar 

  42. Acharya UR et al (2018) Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gener Comp Syst 79:952–959

    Article  Google Scholar 

  43. Acharya UR et al (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415:190–198

    Article  Google Scholar 

  44. Jia F, Lei Y, Guo L, Lin J, Xing S (2017) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 248:98–109

    Google Scholar 

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Acknowledgements

This work was supported by the MOE–Microsoft Key Laboratory of Natural Language, Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804) and the National Natural Science Foundation of China under Grant Nos. 61572155, 61672188, and 61272386.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Moussa Amrani, Mohamed Hammad, Feng Jiang & Kuanquan Wang

  2. Faculty of Computers and Information, Menoufia University, Menoufia, Egypt

    Mohamed Hammad

  3. Department of Computer Science, University of Frères Mentouri, Constantine, Algeria

    Moussa Amrani & Amel Amrani

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  1. Moussa Amrani
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  2. Mohamed Hammad
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  3. Feng Jiang
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  5. Amel Amrani
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Correspondence to Moussa Amrani.

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Amrani, M., Hammad, M., Jiang, F. et al. Very deep feature extraction and fusion for arrhythmias detection. Neural Comput & Applic 30, 2047–2057 (2018). https://doi.org/10.1007/s00521-018-3616-9

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