Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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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

안정 상태에서의 정량 뇌파를 이용한 기계학습 기반의 경도인지장애 환자의 감별 진단 모델 개발 및 검증

  • Received : 2022.03.28
  • Accepted : 2022.07.11
  • Published : 2022.08.31
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Abstract

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 normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified 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 impairment, 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 meaningful biomarker to discriminate cognitive decline.

Keywords

Acknowledgement

본 연구는 보건복지부 및 과학기술정보통신부의 재원으로 치매극복연구개발 사업단선정, 한국보건산업진흥원의 보건의료기술연구개발사업 지원에 의하여 이루어진 것임(과제번호: HU20C0487).

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