کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11009513 | 1826478 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
EEG-based multi-feature fusion assessment for autism
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
عصب شناسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication, cognitive and language abilities. In this paper, multi-feature fusion method based on EEG signal is used to extract as many as possible features including power spectrum analysis, bicoherence, entropy and coherence methods, then we use minimum redundancy maximum correlation (mRMR) algorithm to choose the features, which are applied to input to three classifiers to obtain accuracy classification results. We try to find some key biomarkers of ASD by examining the accuracy of classifier, using different models which use the combination of multiplex features. The results show when nine features are selected by SVM-linear classifier, the accuracy is up to 91.38%. This method might provide objective basis for clinical diagnosis of autism.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Clinical Neuroscience - Volume 56, October 2018, Pages 101-107
Journal: Journal of Clinical Neuroscience - Volume 56, October 2018, Pages 101-107
نویسندگان
Jiannan Kang, Tianyi Zhou, Junxia Han, Xiaoli Li,