The predictive value of light gradient boosting machine model based on three nonlinear features of scalp electroencephalography on the preictal phase of epilepsy.
Objective Constructing a classification model based on 3 nonlinear features of scalp electroencephalography (SEEG) for identifying the preictal phase of epilepsy. Methods Total 83 patients with epilepsy who underwent long - term SEEG monitoring at He'nan Provincial People's Hospital from January 201...
| Published in: | Chinese Journal of Contemporary Neurology & Neurosurgery Vol. 26; no. 3; pp. 227 - 235 |
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| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Chinese Journal of Contemporary Neurology & Neurosurgery
Mar2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193030408&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193030408 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16726731 FDQ6 jtl: Chinese Journal of Contemporary Neurology & Neurosurgery issn: 16726731 maglogo: N pubinfo: dt: Mar2026 vid: 26 iid: 3 pid: 80951 pub: Chinese Journal of Contemporary Neurology & Neurosurgery artinfo: ui: 193030408 193030408 193030408 10.3969/j.issn.1672-6731.2026.03.004 193030408 ppf: 227 ppct: 8 formats: fmt: @attributes: type: P tig: atl: The predictive value of light gradient boosting machine model based on three nonlinear features of scalp electroencephalography on the preictal phase of epilepsy. aug: au: REN, Zhe GAO, Jing YUE, Meng-yan ZHAO, Ting WANG, Na CHEN, Ya-nan HAN, Xiong affil: Department of Neurology, He'nan Provincial People's Hospital, Zhengzhou 450003, He'nan, China sug: subj: Epilepsy Diagnosis Boosting Machine Learning Algorithms Computer Simulation Predictive Value of Tests Scalp Electroencephalography Human China Analysis of Variance Mann-Whitney U Test Chi Square Test Support Vector Machine Machine Learning Algorithms Sensitivity and Specificity ROC Curve Funding Source Retrospective Design Record Review Tertiary Health Care China Data Analysis Software ab: Objective Constructing a classification model based on 3 nonlinear features of scalp electroencephalography (SEEG) for identifying the preictal phase of epilepsy. Methods Total 83 patients with epilepsy who underwent long - term SEEG monitoring at He'nan Provincial People's Hospital from January 2014 to December 2021 were enrolled. Three nonlinear features [phase lag index (PLI), Lempel- Ziv complexity (LZC) and sample entropy (SampEn)] were calculated. Analysis of variance, rank sum test, and χ2 test were used to complete feature selection of disease predictive value. Single and general models were constructed to identify the preictal phase of epilepsy, respectively, using different machine learning (ML) algorithms including support vector machine (SVM), light gradient boosting machine (LightGBM), and k nearest neighbor (KNN) methods based on five -fold cross-validation. Results The LightGBM model constructed with rank sum test feature selection (α + β + δ + θ + whole) performed best. The average performance in the single model was 0.929 for sensitivity, 0.924 for specificity, 0.934 for accuracy, 0.930 for precision, 0.928 for the F1-score, and 0.971 for the area under the curve (AUC). In the general model, the performance was 0.712 for sensitivity, 0.652 for specificity, 0.682 for accuracy, 0.677 for precision, 0.694 for the F1-score, and 0.766 for AUC. Conclusions The LightGBM model constructed using rank sum test was effective in identifying the preictal phase of epilepsy. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Chinese refInfo: holdings: @attributes: islocal: N |
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