Abstract

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 in helping physicians make early interventions for patients with depression and avoid malignant events. In this work, a novel framework of predicting prognostic effects of acupuncture for depression based on electroencephalogram (EEG) recordings is presented. Specifically, EEG, as a widely used measurement to evaluate the therapeutic effects of acupuncture, 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 subject Hamilton Depression Rating Scale (HAMD) scores before and after acupuncture for eight 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. Experimental results show that nonlinear 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 most 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. Moreover, lead-rhythm features selected by mRMR-FCD and mRMR-FCQ are analyzed. The 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. The presented framework in this work is effective in predicting the prognostic effects of acupuncture for depression. It can be integrated into an intelligent medical system and provide information on the 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.

Highlights

  • Depression is one of the most common mental disorders, characterized by a persistent low mood, loss of interest, or reduced energy

  • It mainly consists of EEG recording acquisition, feature selection with the Max-relevance and min-redundancy (mRMR) [17, 18], and predicting models built by widely used machine learning methods. e details are described as follows

  • Regarding the three machine learning methods, they run on the machine learning platform of SKlearn V0.23.1, which is deployed in the CentOS 6.5 operation system

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Summary

Introduction

Depression is one of the most common mental disorders, characterized by a persistent low mood, loss of interest, or reduced energy. According to the World Health Organization’s (WHO) report, over 264 million persons at all ages suffer from depression [1, 2]. Depression has a serious impact on the person’s study, work, and social life. Depression can cause the affected person to self-harm or even commit suicide when she/he is under long-lasting moderate or severe intensity [3]. Acupuncture has good clinical efficacy in the treatment of depression, with no side effects or adverse reactions. The mechanism of acupuncture treatment is still controversial and there is still no objective evidence for evaluating its therapeutic effects [4, 5]. E Hamilton Depression Rating Scale (HAMD) [6] is a widely used tool to assess The mechanism of acupuncture treatment is still controversial and there is still no objective evidence for evaluating its therapeutic effects [4, 5]. e Hamilton Depression Rating Scale (HAMD) [6] is a widely used tool to assess

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