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Identification of the Normal/Abnormal Heart Sounds Based on Energy Features and Xgboost

  • Ting Li
  • Xing-rong Chen
  • Hong Tang
  • Xiao-ke Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

A normal/abnormal heart sound identification method was put forward in the paper. The wavelet packet energy features of the heart sounds were extracted in a large database of 1136 recordings and xgboost algorithm was used as the classifier. The feature importance is also evaluated and analyzed. Top 3, 6, 9 and 12 features were used to classify the heart sounds. Experimental results showed that the proposed algorithm can identify the normal and abnormal heart sounds effectively. And the result used top 9 features was as good as that of all features, which can reduce almost half of computation.

Keywords

Heart sounds Identification Wavelet packet energy Xgboost Feature importance 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61601081, 61471081; Fundamental Research Funds for the Central Universities under Grant Nos. DC201501056, DCPY2016008, DUT15QY60, DUT16QY13; Dalian Youth Technology Star Project Supporting Plan under Grant No. 2015R091.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ting Li
    • 1
  • Xing-rong Chen
    • 1
  • Hong Tang
    • 2
  • Xiao-ke Xu
    • 1
  1. 1.School of Information and Communication EngineeringDalian Minzu UniversityDalianChina
  2. 2.School of Biomedical EngineeringDalian University of TechnologyDalianChina

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