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KMID : 1137820220430040185
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의공학회지
2022 Volume.43 No. 4 p.185 ~ p.192
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Development and Validation of a Machine Learning-based Differential Diagnosis Model for Patients with Mild Cognitive Impairment using Resting-State Quantitative EEG
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Lim Seung-Eui
Kim Jin-Uk
Moon Ki-Wook
Ha Sang-Won
Lee Ki-Won
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Abstract
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Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with nor- mal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rec- tified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impair- ment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a mean- ingful biomarker to discriminate cognitive decline.
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KEYWORD
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Quantitative electroencephalogram, EEG analysis, Mild cognitive impairment, Machine learning
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