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.
Improving Classification of Microembolus and Artifact of HITS Event by Feature Selection
10th International Conference on Robotics, Vision, Signal Processing and Power Applications ; 10 ; 559-565
Lecture Notes in Electrical Engineering ; 547 , 10
2019-01-01
7 pages
Article/Chapter (Book)
Electronic Resource
English
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