| dc.description.abstract | Clinical variables are crucial for diagnosing diseases and predicting treatment outcomes. However, they have limitations, particularly for common psychiatric or neurological disorders like Major Depressive Disorder (MDD) and Epilepsy, where clinical variables cannot effectively reflect the brain′s condition.
This study extracts functional connectivity and power from electroencephalography (EEG) and combines these with clinical variables. The data are then trained using Leave-One-Out Cross-Validation (LOOCV) and machine learning classifiers, applied to three sub-topics: ＂Diagnosis of Major Depressive Disorder,＂ ＂Prediction of Treatment Efficacy for Major Depressive Disorder,＂ and ＂Prediction of Treatment Efficacy for Refractory Epilepsy.
The goal of the ＂Diagnosis of Major Depressive Disorder＂ is to ＂assist in diagnosing MDD through EEG and clinical variables.＂ A total of 77 patients and 46 healthy individuals′ EEG and clinical variables were collected. The best diagnostic result was achieved using Absolute Power with Medium KNN, yielding a Precision of 0.881, Recall of 0.961, Specificity of 0.783, Accuracy of 0.894, F1 Score of 0.919, and AUC of 0.876.
The goal of the ＂Prediction of Treatment Efficacy for Major Depressive Disorder＂ is to ＂assist in predicting the efficacy of treatment after 4, 6, and 8 weeks using EEG and clinical variables.＂ EEGs before treatment, EEGs one week after treatment, and clinical variables from 77 patients were collected.
For predicting treatment efficacy at 4 weeks, the best result was achieved using W1 EEG PLV and Absolute Power with Fine Tree or Medium Tree, yielding a Precision of 0.704, Recall of 0.792, Specificity of 0.849, Accuracy of 0.831, F1 Score of 0.745, and AUC of 0.818.
For 6 weeks, the best result was achieved using W1-W0 EEG PLV, Relative Power, and clinical variables with Coarse Gaussian SVM, yielding a Precision of 0.724, Recall of 0.724, Specificity of 0.742, Accuracy of 0.733, F1 Score of 0.724, and AUC of 0.772.
For 8 weeks, the best result was achieved using W1-W0 EEG Absolute Power and clinical variables with Coarse Tree, yielding a Precision of 0.675, Recall of 0.849, Specificity of 0.519, Accuracy of 0.700, F1 Score of 0.757, and AUC of 0.790.
The goal of the ＂Prediction of Treatment Efficacy for Refractory Epilepsy＂ is to ＂assist in predicting the efficacy of ketogenic diet treatment after 3 and 6 months using EEG.＂ EEGs from 90 patients were collected.
For predicting treatment efficacy at 3 months, the best result was achieved using PLV and Relative Power with Fine Tree or Medium Tree, yielding a Precision of 0.831, Recall of 0.817, Specificity of 0.667, Accuracy of 0.767, F1 Score of 0.824, and AUC of 0.760.
For 6 months, the best result was achieved using PLV and Absolute Power with Linear SVM, yielding a Precision of 0.800, Recall of 0.963, Specificity of 0.000, Accuracy of 0.776, F1 Score of 0.874, and AUC of 0.752. | en_US |