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Continuous Detection of Abnormal Heartbeats from ECG Using Online Outlier Detection

  • Yuhang Lin
  • Byung Suk LeeEmail author
  • Daniel Lustgarten
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

Abstract

Detecting abnormal heartbeats from an electrocardiogram (ECG) signal is an important problem studied extensively and yet is a difficult problem that defies a viable working solution, especially on a mobile platform which requires computationally efficient and yet accurate detection mechanism. In this project, a prototype system has been built to test the feasibility and efficacy of detecting abnormal ECG segments from an ECG data stream targeting a mobile device, where data are arriving continuously and indefinitely and are processed online incrementally and efficiently without being stored in memory. The processing comprises three steps: (i) segmentation using R peak detection, (ii) feature extraction using discrete wavelet transform, and (iii) outlier detection using incremental online microclustering. Experiments conducted using real ambulatory ECG datasets showed satisfactory accuracy. In addition, comparing personalized detection (tuned separately for each patient’s ECG datasets) and non-personalized detection (tuned aggregated over all patients’ datasets) confirms a definite advantage of personalized detection for ECG.

Keywords

ECG Anomaly detection Outlier detection Data stream 

References

  1. 1.
    Angiulli, F., Fassetti, F.: Distance-based outlier queries in data streams: the novel task and algorithms. Data Min. Knowl. Discov. 20(2), 290–324 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Assent, I., Kranen, P., Baldauf, C., Seidl, T.: AnyOut: anytime outlier detection on streaming data. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 228–242. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29038-1_18CrossRefGoogle Scholar
  3. 3.
    Bazett, H.C.: An analysis of the time-relations of electrocardiograms. Heart 7, 353–370 (1920)Google Scholar
  4. 4.
    Bensaid, A.M., Bouhouch, N., Bouhouch, R., Fellat, R., Amri, R.: Classification of ECG patterns using fuzzy rules derived from ID3-induced decision trees. In: Proceedings of the Conference of the North American Fuzzy Information Processing Society, pp. 34–38, August 1998Google Scholar
  5. 5.
    Chauhan, S., Vig, L.: Anomaly detection in ECG time signals via deep long short-term memory networks. In: Proceedings of the IEEE International Conference on Data Science and Advanced Analytics, pp. 1–7, October 2015Google Scholar
  6. 6.
    Chen, H.C., Chen, S.W.: A moving average based filtering system with its application to real-time QRS detection. In: Computers in Cardiology, pp. 585–588, September 2003Google Scholar
  7. 7.
    Firebug: Practical hyperparameter optimization: Random vs. grid search (2016). https://stats.stackexchange.com/q/209409. Accessed 18 April 2018
  8. 8.
    Georgiadis, D., Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1061–1064, June 2013Google Scholar
  9. 9.
    Houghton, A.R., Gray, D.: Making Sense of the ECG. CRC Press, Boca Raton (2007)Google Scholar
  10. 10.
    Karczewicz, M., Gabbouj, M.: ECG data compression by spline approximation. Signal Process. 59(1), 43–59 (1997)CrossRefGoogle Scholar
  11. 11.
    Khare, S., Bhandari, A., Singh, S., Arora, A.: ECG arrhythmia classification using spearman rank correlation and support vector machine. In: Deep, K., Nagar, A., Pant, M., Bansal, J.C. (eds.) Proceedings of the International Conference on Soft Computing for Problem Solving December, 2011. AISC, vol. 131, pp. 591–598. Springer, New Delhi (2012).  https://doi.org/10.1007/978-81-322-0491-6_54CrossRefGoogle Scholar
  12. 12.
    Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proceedings of the 24th International Conference on Very Large Data Bases, pp. 392–403 (1998)Google Scholar
  13. 13.
    Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: Algorithms and applications. VLDB J. 8(3–4), 237–253 (2000)CrossRefGoogle Scholar
  14. 14.
    Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 135–146, April 2011Google Scholar
  15. 15.
    Macek, J.: Incremental learning of ensemble classifiers on ECG data. In: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, pp. 315–320, June 2005Google Scholar
  16. 16.
    Monasterio, V., Laguna, P., Martinez, J.P.: Multilead analysis of t-wave alternans in the ECG using principal component analysis. IEEE Trans. Biomed. Eng. 56(7), 1880–1890 (2009)CrossRefGoogle Scholar
  17. 17.
    Patel, A.M., Gakare, P.K., Cheeran, A.N.: Real time ECG feature extraction and arrhythmia detection on a mobile platform. Int. J. Comput. Appl. 44(23), 40–45 (2012)Google Scholar
  18. 18.
    PhysioNet: MIT-BIH Arrhythmia Database Directory (Introduction) (2010). https://physionet.org/physiobank/database/html/mitdbdir/intro.htm. Accessed 29 May 2018
  19. 19.
    PhysioNet: PysioBank Annotation (2016). https://www.physionet.org/physiobank/annotations.shtml. Accessed 25 May 2018
  20. 20.
    PhysioNet: MIT-BIH Arrhythmia Database Directory (Records) (2018). https://www.physionet.org/physiobank/database/html/mitdbdir/records.htm#207. Accessed 14 June 2018
  21. 21.
    PyWavelet: Signal extension modes PyWavelets Documentation (2018). http://pywavelets.readthedocs.io/en/latest/ref/signal-extension-modes.html. Accessed 2 June 2018
  22. 22.
    Tran, L., Fan, L., Shahabi, C.: Distance-based outlier detection in data streams. Proc. VLDB Endow. 9(12), 1089–1100 (2016)CrossRefGoogle Scholar
  23. 23.
    Veeravalli, B., Deepu, C.J., Ngo, D.H.: Real-time, personalized anomaly detection in streaming data for wearable healthcare devices. In: Khan, S.U., Zomaya, A.Y., Abbas, A. (eds.) Handbook of Large-Scale Distributed Computing in Smart Healthcare. SCC, pp. 403–426. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58280-1_15CrossRefGoogle Scholar
  24. 24.
    Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., Kumar, R.: ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access 6, 9767–9773 (2018)CrossRefGoogle Scholar
  25. 25.
    Yang, D., Rundensteiner, E.A., Ward, M.O.: Neighbor-based pattern detection for windows over streaming data. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 529–540 (2009)Google Scholar
  26. 26.
    Yang, T.F., Devine, B., Macfarlane, P.W.: Artificial neural networks for the diagnosis of atrial fibrillation. Med. Biol. Eng. Comput. 32(6), 615–619 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuhang Lin
    • 1
    • 3
  • Byung Suk Lee
    • 1
    Email author
  • Daniel Lustgarten
    • 2
  1. 1.Department of Computer ScienceUniversity of VermontBurlingtonUSA
  2. 2.Department of MedicineUniversity of VermontBurlingtonUSA
  3. 3.Department of Computer ScienceNorth Carolina State UniversityRaleightUSA

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