An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

doi:10.1007/s11633-019-1197-4

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	An Integrated MCI Detection Framework Based on Spectral-temporal Analysis
	Jiao Yin 2,3; Jinli Cao 3; Siuly Siuly 1; Hua Wang 1
刊名	International Journal of Automation and Computing
	2019
卷号	16 期号:6 页码:786-799
关键词	Electroencephalogram (EEG) dementia early detection mild cognitive impairment (MCI) stationary wavelet transformation (SWT) support vector machine (SVM).
ISSN号	1476-8186
DOI	10.1007/s11633-019-1197-4
英文摘要	Aiming to differentiate between mild cognitive impairment (MCI) patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state electroencephalogram (EEG) recordings. This framework firstly eliminates noise by employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the support vector machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-nearest neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delaying cognitive decline and dementia onset.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42375]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Institute for Sustainable Industries & Liveable Cities, Victoria University, Victoria 3083, Australia 2.School of Software Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China 3.Department of Computer Science and Information Technology, La Trobe University, Victoria 3083, Australia
推荐引用方式 GB/T 7714	Jiao Yin,Jinli Cao,Siuly Siuly,et al. An Integrated MCI Detection Framework Based on Spectral-temporal Analysis[J]. International Journal of Automation and Computing,2019,16(6):786-799.
APA	Jiao Yin,Jinli Cao,Siuly Siuly,&Hua Wang.(2019).An Integrated MCI Detection Framework Based on Spectral-temporal Analysis.International Journal of Automation and Computing,16(6),786-799.
MLA	Jiao Yin,et al."An Integrated MCI Detection Framework Based on Spectral-temporal Analysis".International Journal of Automation and Computing 16.6(2019):786-799.