Santos Febles Elsa, Ontivero Ortega Marlis, Valdés Sosa Michell, Sahli Hichem
Cuban Neuroscience Center, Havana, Cuba.
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
Front Neuroinform. 2022 Jul 8;16:893788. doi: 10.3389/fninf.2022.893788. eCollection 2022.
The event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers. This algorithm finds optimally a combination of kernel functions, integrating them in a meaningful manner, and thus could improve diagnosis.
This study aimed to examine the efficacy of the MKL classifier and the Boruta feature selection method for schizophrenia patients (SZ) and healthy controls (HC) single-subject classification.
A cohort of 54 SZ and 54 HC participants were studied. Three sets of features related to ERP signals were calculated as follows: peak related features, peak to peak related features, and signal related features. The Boruta algorithm was used to evaluate the impact of feature selection on classification performance. An MKL algorithm was applied to address schizophrenia detection.
A classification accuracy of 83% using the whole dataset, and 86% after applying Boruta feature selection was obtained. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300 paradigm.
This study showed that MKL can be useful in distinguishing between schizophrenic patients and controls when using ERP measures. Moreover, the use of the Boruta algorithm provides an improvement in classification accuracy and computational cost.
事件相关电位（ERP）成分P300和失匹配负波（MMN）与精神分裂症患者的认知缺陷有关。通过将机器学习程序应用于这些客观的神经生理学生物标志物，可以改善精神分裂症的诊断。多项研究试图实现这一目标，但尚无研究考察多核学习（MKL）分类器。该算法能以有意义的方式找到核函数的最优组合并将它们整合起来，从而可能改善诊断。
本研究旨在考察MKL分类器和博鲁塔特征选择方法对精神分裂症患者（SZ）和健康对照（HC）进行单受试者分类的效果。
对54名SZ参与者和54名HC参与者组成的队列进行研究。计算了与ERP信号相关的三组特征，如下所示：峰相关特征、峰间相关特征和信号相关特征。使用博鲁塔算法评估特征选择对分类性能的影响。应用MKL算法进行精神分裂症检测。
使用整个数据集的分类准确率为83%，应用博鲁塔特征选择后的准确率为86%。对分类贡献最大的变量主要与听觉P300范式的潜伏期和波幅有关。
本研究表明，在使用ERP测量时，MKL有助于区分精神分裂症患者和对照。此外，使用博鲁塔算法可提高分类准确率并降低计算成本。
