抑郁症和精神分裂症患者静息态脑电信号的分类研究

Resting-state electroencephalogram classification of patients with schizophrenia or depression

精神分裂症和抑郁症患者的临床表现不仅有一定的相似性,而且会随着患者情绪的变化而变化,因此容易导致临床诊断出现误诊。脑电图(EEG)分析为准确区分和诊断精神分裂症与抑郁症患者提供了重要的参考和客观依据。为了解决精神分裂症与抑郁症患者之间误诊的问题,提高区分和诊断这两类疾病的准确率,本研究提取了100名抑郁症患者和100名精神分裂症患者的静息态EEG信号特征,包括:①信息熵、样本熵、近似熵;②统计学属性;③各节律相对功率谱密度(rPSD)。然后,利用这些特征组成特征向量,结合支持向量机(SVM)和朴素贝叶斯(NB)分类器对精神分裂症和抑郁症患者进行分类研究。实验结果表明:①以各节律的rPSD组成的特征向量P的分类效果最好,平均准确率可达84.2%,最高达86.3%;②SVM的分类效果明显优于NB;③β节律的可分性最好,准确率最高,可达76%;④特征权重较大的电极主要集中在额叶和顶叶。本研究结果表明,SVM结合各节律rPSD组成的特征向量P组成的分类模型,对精神分裂症和抑郁症患者的区分具有较好的效果,或可对相关的临床诊断起到一定的辅助作用。

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity,but also change with the patient’s mood,and thus lead to misdiagnosis in clinical diagnosis.Electroencephalogram(EEG)analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression.In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression,and to improve the accuracy of the classification and diagnosis of these two diseases,in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia,including information entropy,sample entropy and approximate entropy,statistical properties feature and relative power spectral density(rPSD)of each EEG rhythm(δ,θ,α,β).Then feature vectors were formed to classify these two types of patients using the support vector machine(SVM)and the naive Bayes(NB)classifier.Experimental results indicate that:①The rPSD feature vector P performs the best in classification,achieving an average accuracy of 84.2%and a highest accuracy of 86.3%;②The accuracy of SVM is obviously better than that of NB;③For the rPSD of each rhythm,theβrhythm performs the best with the highest accuracy of 76%;④Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe.The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia,and can also play an auxiliary role in the relevant clinical diagnosis.