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A Coarse-to-Fine Feature Selection Method for Accurate Detection of Cerebral Small Vessel Disease


Authors: Y. Chen, M. Huang, C. Hu, Y. Zhu, F. Han, and C. Miao
Title: A Coarse-to-Fine Feature Selection Method for Accurate Detection of Cerebral Small Vessel Disease
Abstract: Cerebral small vessel disease (SVD) is common in the elderly and is associated with loss of functional independence, institutionalization, and death. In this paper, we propose a coarse-to-fine feature selection method for accurate SVD detection and timely implementation of interventions. The proposed method first uses an Iterative Random Forest based Feature Selection (IRFFS) method to obtain the most representative features from a feature set that includes gait, balance, and agility performance features extracted from 17 predefined clinical actions. The method then uses the Feature Incremental Extreme Learning Machine (FIELM) model to further verify the discriminant ability of each kind of selected features. Our results demonstrate that the proposed method can effectively select the most significant features for SVD detection, which include gait and agility performance features. Our method achieves up to 91.44% classification accuracy, outperforming other state-of-the-art feature selection methods. Our findings also verify clinical observations indicating that the fine motor pattern features of upper and lower limbs are helpful for high-accuracy SVD detection.
Keywords: Coarse-to-fine feature selection; Detection of cerebral small vessel disease; Iterative random forest-based feature selection; Feature incremental extreme learning machine
Conference Name: International Joint Conference on Neural Networks (IJCNN'16)
Location: Vancouver, Canada
Publisher: IEEE
Year: 2016
Accepted PDF File: A_Coarse-to-Fine_Feature_Selection_Method_for_Accurate_Detection_of_Cerebral_Small_Vessel_Disease_accepted.pdf
Permanent Link: https://dx.doi.org/10.1109/IJCNN.2016.7727526
Reference: Y. Chen, M. Huang, C. Hu, Y. Zhu, F. Han, and C. Miao, “A coarse-to-fine feature selection method for accurate detection of cerebral small vessel disease,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN’16). IEEE, July 2016, pp. 2609–2616.
bibtex: 
@inproceedings{LILY-c90, 
    author	= {Chen, Yiqiang and Huang, Meiyu and Hu, Chunyu and Zhu, Yicheng and Han, Fei and Miao, Chunyan},
    title	= {A Coarse-to-Fine Feature Selection Method for Accurate Detection of Cerebral Small Vessel Disease},  
    booktitle	= {Proceedings of the International Joint Conference on Neural Networks (IJCNN'16)}, 
    year		= {2016}, 
    month	= {July}, 
    pages	= {2609-2616}, 
    location	= {Vancouver, Canada},
    publisher	= {IEEE},
 }