Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

Haining Liu; Ruijuan Shi; Runchao Liao; Yanli Liu; Jiajun Che; Ziyu Bai; Nan Cheng; Hailin Ma

doi:10.3390/brainsci12121677

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

Brain Sci. 2022 Dec 7;12(12):1677. doi: 10.3390/brainsci12121677.

Authors

Haining Liu^{1

2

3}, Ruijuan Shi⁴, Runchao Liao⁵, Yanli Liu⁵, Jiajun Che¹, Ziyu Bai¹, Nan Cheng¹, Hailin Ma³

Affiliations

¹ Psychology Department, Chengde Medical University, Chengde 067000, China.
² Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde 067000, China.
³ Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde 067000, China.
⁴ Plateau Brain Science Research Center, Tibet University/South China Normal University, Lhasa 850012, China.
⁵ Department of Biomedical Engineering, Chengde Medical University, Chengde 067000, China.

Abstract

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Keywords: EEG; Go/No-Go; high-altitude chronic hypoxia; inhibitory control; machine learning; sustained attention.

Abstract

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