Skip to main content

Real-Time Anomaly Detection over ECG Data Stream Based on Component Spectrum

  • Conference paper
  • First Online:
Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9932))

Included in the following conference series:

Abstract

Anomaly detection is a popular research in the age of Big Data. As a typical application scenario, anomaly detection over ECG data stream is confronted with particular difficulties including high real-time requirement and poor data quality. In this article, a novel method based on component spectrum is presented to provide a practicable solution for the problem. Experiments on real data show that the proposed method achieves high sensitivity, high specificity and low false alarm rate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., Rundensteiner, E.A.: Scalable distance-based outlier detection over high-volume data streams. In: International Conference on Data Engineering, pp. 76–87. IEEE (2014)

    Google Scholar 

  2. Jiang, X., Xie, C.: Home health telemonitoring system and data analyzing of physical parameters. In: Computer Engineering and Applications, pp. 213–215 (2011). (in Chinese)

    Google Scholar 

  3. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., et al.: Physiobank physiotoolkit. physionet: components of a new research resource for complex physiologic signals. Circulation 215–220 (2000)

    Google Scholar 

  4. Tsien, C.L.: Reducing false alarms in the intensive care unit: a systematic comparison of four algorithms. In: Proceedings: A Conference of the American Medical Informatics Association. AMIA Annual Fall Symposium, vol. 4, p. 894 (1997)

    Google Scholar 

  5. Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16(1), 1–20 (2016)

    Article  Google Scholar 

  6. Liu, S., Qu, Q., Chen, L., Ni, L.M.: SMC: a practical schema for privacy-preserved data sharing over distributed data streams. IEEE Trans. Big Data 1(2), 68–81 (2015)

    Article  Google Scholar 

  7. Stephenson Jr., H.E., Reid, L.C., Hinton, J.W.: Some common denominators in 1200 cases of cardiac arrest. Ann. Surg. 137(5), 731–744 (1953)

    Google Scholar 

  8. Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.C.: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)

    Article  Google Scholar 

  9. Doǧan, B., Korürek, M.: A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Appl. Soft Comput. 12(11), 3442–3451 (2012)

    Article  Google Scholar 

  10. Qi, J., Zhang, R., Ramamohanarao, K., Wang, H., Wen, Z., Wu, D.: Indexable online time series segmentation with error bound guarantee. World Wide Web-internet Web Inf. Syst., 1–43 (2013)

    Google Scholar 

  11. Ngo, D.H., Veeravalli, B.: Design of a real-time morphology-based anomaly detection method from ECG streams. In: International Conference on Bioinformatics and Biomedicine, pp. 829–836. IEEE (2015)

    Google Scholar 

  12. Aggarwal, C.C.: Outlier Analysis. Springer Publishing Company, New York (2015)

    Book  MATH  Google Scholar 

  13. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 75–79 (2009)

    Article  Google Scholar 

  14. Wang, J., Sun, X., She, M.F.H., Kouzani, A., Nahavandi, S.: Unsupervised mining of long time series based on latent topic model. Neurocomputing 103, 93–103 (2013)

    Article  Google Scholar 

  15. Qu, Q., Qiu, J., Sun, C., Wang, Y.: Graph-based knowledge representation model and pattern retrieval. In: International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp. 541–545 (2008)

    Google Scholar 

  16. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: ACM-SIAM Symposium on Discrete Algorithms, vol. 11, pp. 1027–1035 (2007)

    Google Scholar 

  17. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)

    Google Scholar 

  18. Shabib, A., Narang, A., Niddodi, C.P., Das, M.: Parallelization of searching and mining time series data using dynamic time warping. In: International Conference on Advances in Computing, Communications and Informatics. IEEE (2015)

    Google Scholar 

  19. Smith, L.N., Elad, M.: Improving dictionary learning: multiple dictionary updates and coefficient reuse. IEEE Sig. Process. Lett. 20(1), 79–82 (2013)

    Article  Google Scholar 

  20. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by Natural Science Foundation of China (No. 61170003).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongyan Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wu, M., Qiu, Z., Hong, S., Li, H. (2016). Real-Time Anomaly Detection over ECG Data Stream Based on Component Spectrum. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45817-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45816-8

  • Online ISBN: 978-3-319-45817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics