Fan Xiaomao, Huang Xingxian, Zhao Yang, Wang Lin, Yu Haibo, Zhao Gansen
School of Computer Science, South China Normal University, Guangzhou, China.
Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China.
Evid Based Complement Alternat Med. 2022 Mar 2;2022:1381683. doi: 10.1155/2022/1381683. eCollection 2022.
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 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.
抑郁症被认为是一个重大的公共卫生问题，对个人和社会都有重大影响。抑郁症患者可以采用针灸等辅助疗法。预测针灸的预后效果对于帮助医生对抑郁症患者进行早期干预并避免恶性事件具有重要意义。在这项工作中，提出了一种基于脑电图（EEG）记录预测针灸对抑郁症预后效果的新框架。具体而言，EEG作为一种广泛用于评估针灸治疗效果的测量方法，被用于预测针灸的预后效果。最大相关最小冗余（mRMR）方法具有去除所选特征之间冗余信息并保持所选特征与响应变量之间高度相关性的优点，用于从EEG记录中选择重要的导联节律特征。然后，根据针刺八周前后的汉密尔顿抑郁量表（HAMD）评分，计算HAMD评分的降低率作为针灸预后效果的衡量指标。最后，使用五种广泛使用的机器学习方法构建针灸对抑郁症预后效果的预测模型。实验结果表明，在使用EEG记录预测针灸预后效果方面，非线性机器学习方法比线性方法具有更好的性能。特别是，具有高斯核的支持向量机（SVM-RBF）在使用mRMR进行特征选择时，无论是基于FCD还是FCQ评估标准，都能实现最佳且最稳定的性能。mRMR-FCD和mRMR-FCQ都获得了相同的最佳性能，其中准确率和F1分数分别为84.61%和86.67%。此外，还对mRMR-FCD和mRMR-FCQ选择的导联节律特征进行了分析。前七个选定的导联节律特征具有更高的mRMR评估分数，这在一定程度上保证了机器学习方法的良好预测性能。这项工作中提出的框架在预测针灸对抑郁症的预后效果方面是有效的。它可以集成到智能医疗系统中，为医生提供针灸预后效果的信息。提前了解针灸对抑郁症的预后效果并进行干预，可以大大降低精神障碍患者发生恶性事件的风险。
