Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Elsa Santos Febles; Marlis Ontivero Ortega; Michell Valdés Sosa; Hichem Sahli

doi:10.3389/fninf.2022.893788

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Front Neuroinform. 2022 Jul 8:16:893788. doi: 10.3389/fninf.2022.893788. eCollection 2022.

Authors

Elsa Santos Febles^{1

2}, Marlis Ontivero Ortega^{1

3}, Michell Valdés Sosa¹, Hichem Sahli^{2

4}

Affiliations

¹ Cuban Neuroscience Center, Havana, Cuba.
² Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
³ Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium.
⁴ Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium.

Abstract

Antecedent: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Boruta; event related potential; feature selection; machine learning; multiple kernel learning; schizophrenia.