Laton Jorne, Van Schependom Jeroen, Gielen Jeroen, Decoster Jeroen, Moons Tim, De Keyser Jacques, De Hert Marc, Nagels Guy
Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.
Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium; Faculté de Psychologie et des Sciences de l'Education, Université de Mons, Place du Parc 20, 7000 Mons, Belgium.
J Neurol Sci. 2014 Dec 15;347(1-2):262-7. doi: 10.1016/j.jns.2014.10.015. Epub 2014 Oct 16.
The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying primarily on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements mostly shows moderate accuracy. We wanted to examine whether it is possible to distinguish controls and patients individually with a good accuracy. To this end we used a combination of features extracted from the auditory and visual P300 paradigms and the mismatch negativity paradigm.
We selected 54 patients and 54 controls, matched for age and gender, from the data available at the UPC Kortenberg. The EEG-data were high- and low-pass filtered, epoched and averaged. Features (latencies and amplitudes of component peaks) were extracted from the averaged signals. The resulting dataset was used to train and test classification algorithms. First on separate paradigms and then on all combinations, we applied Naïve Bayes, Support Vector Machine and Decision Tree, with two of its improvements: Adaboost and Random Forest.
For at least two classifiers the performance increased significantly by combining paradigms compared to single paradigms. The classification accuracy increased from at best 79.8% when trained on features from single paradigms, to 84.7% when trained on features from all three paradigms.
A combination of features originating from three evoked potential paradigms allowed us to accurately classify individual subjects as either control or patient. Classification accuracy was mostly above 80% for the machine learners evaluated in this study and close to 85% at best.
精神分裂症的诊断过程主要是临床诊断，必须由经验丰富的精神科医生进行，主要依据临床体征和症状。目前的神经生理学测量可以区分健康对照组和精神分裂症患者组。基于神经生理学测量的个体分类大多显示出中等准确性。我们想研究是否有可能以较高的准确性对对照组和患者进行个体区分。为此，我们使用了从听觉和视觉P300范式以及失配负波范式中提取的特征组合。
我们从UPC科滕贝格可获得的数据中选取了54名患者和54名年龄和性别匹配的对照组。对脑电图数据进行高通和低通滤波、分段和平均。从平均信号中提取特征（成分峰值的潜伏期和振幅）。所得数据集用于训练和测试分类算法。首先在单独的范式上，然后在所有组合上，我们应用了朴素贝叶斯、支持向量机和决策树，并对决策树进行了两种改进：Adaboost和随机森林。
与单一范式相比，至少有两个分类器通过组合范式使性能显著提高。分类准确率从在单一范式特征上训练时的最高79.8%提高到在所有三种范式特征上训练时的84.7%。
源自三种诱发电位范式的特征组合使我们能够准确地将个体受试者分类为对照组或患者。本研究中评估的机器学习算法的分类准确率大多高于80%，最高接近85%。
