Taghavi Mahsa, Boostani Reza, Sabeti Malihe, Taghavi Seyed Mohammad Arash
Department of Psychiatry and Behavioral Sciences, Shiraz University of Medical Sciences, Shiraz Iran.
Department of CSE&IT, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Iran J Psychiatry Behav Sci. 2011 Fall;5(2):62-70.
Diagnosis of the psychiatric diseases is a bit challenging at the first interview due to this fact that qualitative criteria are not as accurate as quantitative ones. Here, the objective is to classify schizophrenic patients from the healthy subject using a quantitative index elicited from their electroencephalogram (EEG) signals.
Ten right handed male patients with schizophrenia who had just auditory hallucination and did not have any other psychotic features and ten age-matched right handed normal male control participants participated in this study. The patients used haloperidol to minimize the drug-related affection on their EEG signals. Electrophysiological data were recorded using a Neuroscan 24 Channel Synamps system, with a signal gain equal to 75K (150 xs at the headbox). According to the observable anatomical differences in the brain of schizophrenic patients from controls, several discriminative features including AR coefficients, band power, fractal dimension, and approximation entropy (ApEn) were chosen to extract quantitative values from the EEG signals.
The extracted features were applied to support vector machine (SVM) classifier that produced 88.40% accuracy for distinguishing the two groups. Incidentally, ApEn produces more discriminative information compare to the other features.
This research presents a reliable quantitative approach to distinguish the control subjects from the schizophrenic patients. Moreover, other representative features are implemented but ApEn produces higher performance due to complex and irregular nature of EEG signals.
由于定性标准不如定量标准准确，在初次面谈时对精神疾病进行诊断颇具挑战性。在此，目标是使用从脑电图（EEG）信号中得出的定量指标，将精神分裂症患者与健康受试者区分开来。
十名仅患有幻听且无任何其他精神病特征的右利手男性精神分裂症患者，以及十名年龄匹配的右利手正常男性对照参与者参与了本研究。患者使用氟哌啶醇以尽量减少药物对其EEG信号的影响。使用Neuroscan 24通道Synamps系统记录电生理数据，信号增益等于75K（在头箱处为150倍）。根据精神分裂症患者与对照组大脑中可观察到的解剖差异，选择了包括AR系数、频段功率、分形维数和近似熵（ApEn）在内的几个判别特征，以从EEG信号中提取定量值。
将提取的特征应用于支持向量机（SVM）分类器，该分类器区分两组的准确率为88.40%。顺便说一下，与其他特征相比，ApEn产生的判别信息更多。
本研究提出了一种可靠的定量方法来区分对照组受试者与精神分裂症患者。此外，还实施了其他代表性特征，但由于EEG信号的复杂和不规则性质，ApEn表现出更高的性能。
