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A Study for Excluding Incorrect Detections of Holter ECG Data Using SVM

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 3316)

Abstract

The inspection of arrhythmia using the Holter ECG is done by automatic analysis. However, the accuracy of this analysis is not sufficient, and the results need to be correct by clinical technologists. During the process of picking up one heartbeat in an automatic analysis system, an incorrect detection, whereby a non-heartbeat is picked up as a heartbeat, may occur. In this research, we proposed the method to recognize this incorrect detection by use of a Support Vector Machine (SVM). When the learning results were evaluated on the ECG wave data from the one hundred subject’s heartbeats, this method correctly recognized a maximum of 93% as incorrect detections. These results should dramatically increase the work efficiency of clinical technologists.

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  • DOI: 10.1007/978-3-540-30499-9_190
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References

  1. Byun, H., Lee, S.-W.: Applications of Support Vector Machines for Pattern Recognition: A Survey. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 213–236. Springer, Heidelberg (2002)

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  3. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)

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© 2004 Springer-Verlag Berlin Heidelberg

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Kikawa, Y., Oguri, K. (2004). A Study for Excluding Incorrect Detections of Holter ECG Data Using SVM. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_190

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_190

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive