Yang Chia-Yen, Chen Yin-Zhen
Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.
Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan.
Asian Biomed (Res Rev News). 2024 Oct 31;18(5):212-223. doi: 10.2478/abm-2024-0029. eCollection 2024 Oct.
Depression is one of the most common mental disorders. Although depression is typically diagnosed by identifying specific symptoms and through history, no recognized standard for depression diagnosis exists. This assures the development of objective diagnostic tools for depression.
We investigated the differences in the resting-state electroencephalograms (EEGs) of patients with depression and healthy controls (HCs) to distinguish patients from HCs by using a support vector machine (SVM) classifier with the following two feature selection approaches: t test and receiver operating characteristic analysis.
We used the EEG data from the Patient Repository of EEG Data + Computational Tools; this study included 21 patients with depressive disorder (MDD) and 21 HCs. The relative frequency power, alpha interhemispheric asymmetry, left-right coherence, strength, clustering coefficient (CC), shortest path length, sample entropy (SampEn), multiscale entropy (MSE), and detrended fluctuation analysis (DFA) data were extracted to determine candidate EEG features associated with depression.
With the t-test selection, the SVM classifier demonstrated the highest performance with the accuracy, sensitivity, and specificity of 96.66%, 95.93%, and 97.550% for the eye-open condition and 91.33%, 90.59%, and 91.81% for the eye-closed condition, respectively. For comparisons of features in the 2 selection approaches, the most influential features were relative frequency power and left-right coherence.
Using this information to distinguish patients with MDD from HC subjects with the SVM classifier resulted in a mean accuracy over 90%. Although this result may not be robust enough for clinical applications, further exploration is necessary given the simplicity, objectivity, and efficiency of the classifier.
抑郁症是最常见的精神障碍之一。尽管抑郁症通常通过识别特定症状和病史来诊断，但目前尚无公认的抑郁症诊断标准。这促使人们开发客观的抑郁症诊断工具。
我们研究了抑郁症患者与健康对照者静息态脑电图（EEG）的差异，通过支持向量机（SVM）分类器及以下两种特征选择方法（t检验和受试者工作特征分析）来区分抑郁症患者与健康对照者。
我们使用了来自EEG数据+计算工具患者库的EEG数据；本研究纳入了21例抑郁症患者（MDD）和21名健康对照者。提取相对频率功率、α半球间不对称性、左右相干性、强度、聚类系数（CC）、最短路径长度、样本熵（SampEn）、多尺度熵（MSE）和去趋势波动分析（DFA）数据，以确定与抑郁症相关的候选EEG特征。
通过t检验选择，SVM分类器在睁眼条件下的准确率、敏感性和特异性分别为96.66%、95.93%和97.550%，在闭眼条件下分别为91.33%、90.59%和91.81%，表现出最高性能。对于两种选择方法中特征的比较，最具影响力的特征是相对频率功率和左右相干性。
利用这些信息通过SVM分类器区分MDD患者与健康对照者，平均准确率超过90%。尽管这一结果可能在临床应用中不够稳健，但鉴于该分类器的简单性、客观性和效率，仍有必要进一步探索。
