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A framework for cardiac arrhythmia detection from IoT-based ECGs

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Abstract

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework.

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References

  1. Abawajy, J.H., Kelarev, A.V., Chowdhury, M.: Multistage approach for clustering and classification of ecg data. Comput. Meth. Program. Biomed. 112(3), 720–730 (2013)

    Google Scholar 

  2. Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inform. Sci. 415, 190–198 (2017)

    Google Scholar 

  3. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)

    Google Scholar 

  4. Afkhami, R.G., Azarnia, G., Tinati, M.A.: Cardiac arrhythmia classification using statistical and mixture modeling features of ecg signals. Pattern Recogn. Lett. 70, 45–51 (2016)

    Google Scholar 

  5. Alejo, R., Sotoca, J.M., Valdovinos, R.M., Toribio, P.: Edited nearest neighbor rule for improving neural networks classifications. In: International Symposium on Neural Networks, pp 303–310. Springer (2010)

  6. ANSI/AAMI: Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms. Association for the Advancement of Medical Instrumentation -AAMI ISO EC57 (Unknown Month 1998)

  7. Ballinger, B., Hsieh, J., Singh, A., Sohoni, N., Wang, J., Tison, G.H., Marcus, G.M., Sanchez, J.M., Maguire, C., Olgin, J.E., et al: Deepheart: semi-supervised sequence learning for cardiovascular risk prediction. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

  8. Britto, A.S. Jr, Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiers—a comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)

    Google Scholar 

  9. Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3-4), 673–688 (2013)

    Google Scholar 

  10. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artificial Intell. Res. 16, 321–357 (2002)

    MATH  Google Scholar 

  11. Chen, S., Hua, W., Li, Z., Li, J., Gao, X.: Heartbeat classification using projected and dynamic features of ecg signal. Biomed. Signal Process. Control 31, 165–173 (2017)

    Google Scholar 

  12. Cruz, R.M., Sabourin, R., Cavalcanti, G.D.: Meta-des. oracle: meta-learning and feature selection for dynamic ensemble selection. Information Fusion 38, 84–103 (2017)

    Google Scholar 

  13. Cruz, R.M., Sabourin, R., Cavalcanti, G.D.: Dynamic classifier selection: recent advances and perspectives. Information Fusion 41, 195–216 (2018)

    Google Scholar 

  14. Daamouche, A., Hamami, L., Alajlan, N., Melgani, F.: A wavelet optimization approach for ecg signal classification. Biomed. Signal Process. Control 7(4), 342–349 (2012)

    Google Scholar 

  15. De Albuquerque, V.H.C., Nunes, T.M., Pereira, D.R., Luz E.J.d.S., Menotti, D., Papa, J.P., Tavares, J.M.R.: Robust automated cardiac arrhythmia detection in ecg beat signals. Neural Comput. Appl. 29(3), 679–693 (2018)

    Google Scholar 

  16. De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51 (7), 1196–1206 (2004)

    Google Scholar 

  17. De Lannoy, G., François, D., Delbeke, J., Verleysen, M.: Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans. Biomed. Eng. 59(1), 241–247 (2012)

    Google Scholar 

  18. Doquire, G., De Lannoy, G., François, D., Verleysen, M.: Feature selection for interpatient supervised heart beat classification. Comput. Intell. Neurosci. 2011, 1 (2011)

    Google Scholar 

  19. Dos Santos, E.M., Sabourin, R., Maupin, P.: A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recogn. 41(10), 2993–3009 (2008)

    MATH  Google Scholar 

  20. Güler, İ., ÜBeylı, E.D.: Ecg beat classifier designed by combined neural network model. Pattern Recogn. 38(2), 199–208 (2005)

    Google Scholar 

  21. He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: D-Ecg: a dynamic framework for cardiac arrhythmia detection from Iot-Based Ecgs. In: International Conference on Web Information Systems Engineering, pp 85–99. Springer (2018)

  22. He, J., Sun, L., Rong, J., Wang, H., Zhang, Y.: A pyramid-like model for heartbeat classification from ecg recordings. Plos one 13(11), e0206593 (2018)

    Google Scholar 

  23. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778 (2016)

  24. Huang, H., Liu, J., Zhu, Q., Wang, R., Hu, G.: A new hierarchical method for inter-patient heartbeat classification using random projections and rr intervals. Biomed. Eng. Online 13(1), 90 (2014)

    Google Scholar 

  25. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)

  26. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ecg classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2016)

    Google Scholar 

  27. Ko, A.H., Sabourin, R., Britto, A.S. Jr: From dynamic classifier selection to dynamic ensemble selection. Pattern Recogn. 41(5), 1718–1731 (2008)

    MATH  Google Scholar 

  28. Lin, C., Chen, W., Qiu, C., Wu, Y., Krishnan, S., Zou, Q.: Libd3c: ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing 123, 424–435 (2014)

    Google Scholar 

  29. Llamedo, M., Martínez, J.P.: Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3), 616–625 (2011)

    Google Scholar 

  30. Luz, E.J.D.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: a survey. Comput. Meth. Programs Biomed. 127, 144–164 (2016)

    Google Scholar 

  31. Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. (TOIT) 16(1), 4 (2016)

    Google Scholar 

  32. Martis, R.J., Acharya, U.R., Ray, A.K., Chakraborty, C.: Application of higher order cumulants to ecg signals for the cardiac health diagnosis. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Embc, pp 1697–1700. IEEE (2011)

  33. Masoudnia, S., Ebrahimpour, R.: Mixture of experts: a literature survey. Artif. Intell. Rev. 42(2), 275–293 (2014)

    Google Scholar 

  34. Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)

    Google Scholar 

  35. Özbay, Y., Tezel, G.: A new method for classification of ecg arrhythmias using neural network with adaptive activation function. Digital Signal Processing 20 (4), 1040–1049 (2010)

    Google Scholar 

  36. Pan, J., Tompkins, W.J.: A real-time qrs detection algorithm. IEEE Trans. Biomed. Eng 32(3), 230–236 (1985)

    Google Scholar 

  37. Sahoo, S., Kanungo, B., Behera, S., Sabut, S.: Multiresolution wavelet transform based feature extraction and ecg classification to detect cardiac abnormalities. Measurement 108, 55–66 (2017)

    Google Scholar 

  38. Sellami, A., Hwang, H.: A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Expert Syst. Appl. 122, 75–84 (2019)

    Google Scholar 

  39. Shensa, M.J.: The discrete wavelet transform: wedding the a trous and mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992)

    MATH  Google Scholar 

  40. Sierra, B., Lazkano, E., Irigoien, I., Jauregi, E., Mendialdua, I.: K nearest neighbor equality: giving equal chance to all existing classes. Inform. Sci. 181 (23), 5158–5168 (2011)

    Google Scholar 

  41. Soares, R.G., Santana, A., Canuto, A.M., de Souto, M.C.P.: Using accuracy and diversity to select classifiers to build ensembles. In: International Joint Conference on Neural Networks, 2006. IJCNN’06, pp 1310–1316. IEEE (2006)

  42. Supratak, A., Dong, H., Wu, C., Guo, Y.: Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel eeg. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 1998–2008 (2017)

    Google Scholar 

  43. Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Sys. Man Cyber. SMC-2(3), 408–421 (1972)

    MathSciNet  MATH  Google Scholar 

  44. Woloszynski, T., Kurzynski, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn. 44(10-11), 2656–2668 (2011)

    MATH  Google Scholar 

  45. Woloszynski, T., Kurzynski, M., Podsiadlo, P., Stachowiak, G.W.: A measure of competence based on random classification for dynamic ensemble selection. Information Fusion 13(3), 207–213 (2012)

    Google Scholar 

  46. Xiao, J., Xie, L., He, C., Jiang, X.: Dynamic classifier ensemble model for customer classification with imbalanced class distribution. Expert Syst. Appl. 39 (3), 3668–3675 (2012)

    Google Scholar 

  47. Yang, Z., Zhou, Q., Lei, L., Zheng, K., Xiang, W.: An iot-cloud based wearable ecg monitoring system for smart healthcare. J. Med. Sys. 40(12), 286 (2016)

    Google Scholar 

  48. Ye, C., Kumar, B.V., Coimbra, M.T.: Heartbeat classification using morphological and dynamic features of ecg signals. IEEE Trans. Biomed. Eng. 59(10), 2930–2941 (2012)

    Google Scholar 

  49. Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification. Comput. Biol. Med. 96, 189–202 (2018)

    Google Scholar 

  50. Yu, S.N., Chen, Y.H.: Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recogn. Lett. 28(10), 1142–1150 (2007)

    Google Scholar 

  51. Zhang, C., Wang, G., Zhao, J., Gao, P., Lin, J., Yang, H.: Patient-specific ecg classification based on recurrent neural networks and clustering technique. In: 2017 13th IASTED International Conference on Biomedical Engineering (Biomed), pp 63–67. IEEE (2017)

  52. Zhang, Z., Dong, J., Luo, X., Choi, K.S., Wu, X.: Heartbeat classification using disease-specific feature selection. Comput. Biol. Med. 46, 79–89 (2014)

    Google Scholar 

  53. Zhu, X., Wu, X., Yang, Y.: Dynamic classifier selection for effective mining from noisy data streams. In: Fourth IEEE International Conference On Data Mining, 2004. ICDM’04, pp 305–312. IEEE (2004)

  54. Zubair, M., Kim, J., Yoon, C.: An automated ecg beat classification system using convolutional neural networks. In: 2016 6th International Conference on IT Convergence and Security (ICITCS), pp 1–5. IEEE (2016)

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Acknowledgments

This work is supported by the NSFC (No. 61672161)and the National Natural Science Foundation of China (Grants No. 61702274).

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Melbourne, Australia

    Jinyuan He, Jia Rong, Hua Wang & Yanchun Zhang

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Le Sun

  3. College of Science and Engineering, James Cook University, Queensland, Australia

    Jiangang Ma

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  1. Jinyuan He
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Correspondence to Jia Rong.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2018

Guest Editors: Hakim Hacid, Wojciech Cellary, Hua Wang and Yanchun Zhang

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He, J., Rong, J., Sun, L. et al. A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web 23, 2835–2850 (2020). https://doi.org/10.1007/s11280-019-00776-9

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