Predicting Prognostic Effects of Acupuncture for Depression Using Electroencephalogram
Background: Depression is considered to be a major public health problem with significant implications for individuals and society. Patients with depression can be with complementary therapies such as acupuncture. Predicting the prognostic effects of acupuncture has a big significance of helping physicians to take early interventions for patients with depression and avoid malignant events.Methods: In this work, a novel framework of predicting prognostic effects of acupuncture for depression
... d on electroencephalogram (EEG) recordings is presented. Specifically, EEG is utilized for predicting prognostic effects of acupuncture. Max-relevance and min-redundancy (mRMR), with merits of removing redundant information among selected features and remaining high relevance between selected features and response variable, is employed to select important lead-rhythm features extracted from EEG recordings. Then, according to the subjects' HAMD scores before and after acupuncture for 8 weeks, the reduction rate of HAMD score is calculated as a measure of the prognostic effects of acupuncture. Finally, five widely used machine learning methods are utilized for building the predicting models of prognostic effects of acupuncture for depression.Results: Experimental results show that non-linear machine learning methods have better performance than linear ones on predicting prognostic effects of acupuncture using EEG recordings. Especially, the support vector machine with Gaussian kernel (SVM-RBF) can achieve the best and stable performance using the mRMR with both evaluating criteria of FCD and FCQ for feature selection. Both mRMR-FCD and mRMR-FCQ obtain the same best performance, where the accuracy and F1 score are 84.61 % and 86.67 %, respectively. What's more, lead-rhythm features selected by mRMR-FCD and mRMR-FCQ are analyzed. Top seven selected lead-rhythm features have much higher mRMR evaluating scores, which guarantee the good predicting performance for machine learning methods to some degree.Conclusion: The presented framework in this work is effective in predicting prognostic effects of acupuncture for depression. It can be integrated into an intelligent medical system and provide the information of prognostic effects of acupuncture for physicians. Informed prognostic effects of acupuncture for depression in advance and taking interventions can greatly reduce the risk of malignant events for patients with mental disorders.