MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals
| Autor: | Thanh-Tung Trinh, Chun-Ying Lee, Chien-Te Wu, Yi-Hung Liu, Yu-Tsung Hsiao, Chia-Fen Tsai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
Control and Optimization Fisher’s class separability criterion Feature vector 02 engineering and technology Kernel principal component analysis 03 medical and health sciences 0302 clinical medicine Production of electric energy or power. Powerplants. Central stations Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering support vector machine electroencephalography (EEG) Materials of engineering and construction. Mechanics of materials Mathematics business.industry Pattern recognition kernel principal component analysis Linear discriminant analysis brain–computer interface (BCI) Support vector machine machine learning Control and Systems Engineering Feature (computer vision) Kernel (statistics) mild cognitive impairment (MCI) Principal component analysis TA401-492 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Actuators, Vol 10, Iss 152, p 152 (2021) Actuators Volume 10 Issue 7 |
| ISSN: | 2076-0825 |
| Popis: | Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI. |
| Databáze: | OpenAIRE |
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