[논문]Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography

Kim, Jieun; Kim, Min-Young; Kwon, Hyukchan; Kim, Ji-Woong; Im, Woo-Young; Lee, Sang Min; Kim, Kiwoong; Kim, Seung Jun

doi:10.1016/j.jneumeth.2020.108688

[해외논문] Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography 원문보기

Journal of neuroscience methods, v.338, 2020년, pp.108688 -

Kim, Jieun (Advanced Instrumentation Institute, Korea Research Institute of Standards and Science) , Kim, Min-Young (Advanced Instrumentation Institute, Korea Research Institute of Standards and Science) , Kwon, Hyukchan (Advanced Instrumentation Institute, Korea Research Institute of Standards and Science) , Kim, Ji-Woong (Department of Psychiatry, Konyang University College of Medicine, Konyang University Hospital) , Im, Woo-Young (Department of Psychiatry, Konyang University College of Medicine, Konyang University Hospital) , Lee, Sang Min (Department of Psychiatry, Kyung Hee University School of Medicine, Kyung Hee University Hospital) , Kim, Kiwoong (Advanced Instrumentation Institute, Korea Research Institute of Standards and Science) , Kim, Seung Jun (Department of Psychiatry, Konyang University College of Medicine, Konyang University Hospital

Abstract ▼ AI-Helper

Abstract Background When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected features. New method: To reduce the redundant features, we applied a technique employing FDR in conjunction with feature correlation. We performed an attention network test on schizophrenic patients and normal subjects with a 152-channel magnetoencephalograph. P300m amplitudes of event-related fields (ERFs) were used as features at the sensor level and P300m amplitudes of ERFs for 500 nodes on the cortex surface were used as features at the source level. Features were ranked using FDR criterion and cross-correlation measure, and then the highest ranked 10 features were selected and an exhaustive search was used to find combination having the maximum accuracy. Results At the sensor level, we found a single channel of the occipital region that distinguished the two groups with an accuracy of 89.7 %. At source level, we obtained an accuracy of 96.2 % using two features, the left superior frontal region and the left inferior temporal region. Comparison with existing method At source level, we obtained a higher accuracy than traditional method using only FDR criterion (accuracy = 88.5 %). We used only the P300 m amplitude (not latency) on a single channel and two brain regions at a fairly high rate. Highlights Feature reduction method is proposed when there are redundant or correlated features. For feature reduction, we use FDR value in conjunction with feature correlation. An ANT task is used to distinguish patient with schizophrenia. The 149 and 500 visual P300 m are used as features in sensor and source level. Our method can be applied to EEG, MEG and fMRI data.

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