Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Abstract

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls' EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.

Keywords: microstate analysis; microstate map correlation; optimized machine learning; resting-state EEG; schizophrenia.

Figure 1

Overview of the process for extracting microstate features; SZ: patients diagnosed with schizophrenia; HC: healthy control subjects.

Figure 2

Four normalized microstates (i.e., A, B, C, and D) of resting-state EEG recordings were obtained for patients diagnosed with schizophrenia and healthy control subjects; SZ: patients diagnosed with schizophrenia; HC: healthy control subjects.

Figure 3

Results of the predictor importance scores for twenty microstate features extracted from resting state EEG recordings of SZ patients and HC subjects.

Figure 4

Shapley additive explanation values for the microstate features to explain the contribution of individual features to the prediction.

Figure 5

Optimized machine learning results for ranked twenty microstate features. Number (1) shows the best-fitted model results acquired for using six ranked features. Number (2) shows the highest output measures when using 19 input features.