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Ventricular Arrhythmia Classification Based on High-Order Statistical Features of ECG Signals

  • Sunghyun Moon
  • Jungjoon Kim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

One class SVM classification model based on high-order statistical features of ECG signals is proposed. This utilizes distinct features of variance, skewness and kurtosis between normal signals and ventricular arrhythmia ECG signals. The model based on a few simple features motivates immediate treatment for sudden cardiac event and wearable technology in practice. The classification algorithm shows significantly improved performance of 98.9% accuracy in correct classification in the experiment using the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). It is expected to be used in real-time electrocardiogram monitoring system in conjunction with ECG measurement part and application part.

Keywords

Classification Support vector machine Ventricular arrhythmia  Kurtosis Skewness Variance 

Notes

Acknowledgments

Research supported by the National Research Foundation of Korea grant funded by the Ministry of Education (NRF-2014R1A1A2057732).

References

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronics EngineeringKyungpook National UniversityDaeguRepublic of Korea

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