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Improving Classification of Microembolus and Artifact of HITS Event by Feature Selection

  • Najah GhazaliEmail author
  • Maz Jamilah MasnanEmail author
  • Dzati Athiar RamliEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)

Abstract

In monitoring system using transcranial Doppler ultrasound for stroke detection, the occurrence of high intensity transient signal can happen at different branch of arteries, i.e. internal cerebral artery (ICA), middle cerebral artery (MCA) and posterior cerebral artery (PCA). The representations of features can sometimes be redundant and not useful, which can degrade the classification performance. Thus, feature selection is studied and presented in this paper. The applied selection criteria are based on the unbounded Mahalanobis distance (referred as A) and single-feature-accuracy measure (referred as B). The result indicates that kinematic descriptor (SMV) is the most significant feature to predict HITS with 85.8% correct. However, the classification accuracy further improved when SMV is combined with other features in different feature subsets.

Keywords

Feature selection TCD ultrasound HITS classification 

Notes

Acknowledgements

The authors would like to thank the financial support provided by Research University Grant (1001.PELECT.8014057) for this research work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of Engineering MathematicsUniversiti Malaysia Perlis (UniMAP) Kampus Tetap Pauh PutraArauMalaysia
  2. 2.School of Electrical and Electronic EngineeringUniversiti Sains Malaysia, Engineering CampusNibong TebalMalaysia

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