Key result
An EEG-based support vector machine differentiates chronic high-altitude exposure from healthy controls with ~93% accuracy.
Why the study?
The study aimed to examine the effect of high altitude on inhibitory control processes underlying sustained attention via EEG, and explore if this EEG data can classify high-altitude chronic hypoxia versus plain controls.
Can machine learning based on event-related EEG during a sustained attention task differentiate adults with chronic high-altitude exposure from healthy controls?
Case-Control (n=67)
Can machine learning based on event-related EEG during a sustained attention task differentiate adults with chronic high-altitude exposure from healthy controls?
Machine learning algorithms using event-related EEG data can accurately identify individuals with chronic high-altitude hypoxia, providing potential neuroelectrophysiological markers.
Supports EEG-ML as a potential marker for chronic high-altitude effects; hypothesis-generating and requires prospective validation before clinical use.
(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.
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Liu et al. (2022) conducted a case-control in Chronic high-altitude hypoxia (n=67). Chronic high-altitude exposure vs. Matched plain controls (low altitude) was evaluated on Classification of high-altitude chronic hypoxia vs plain controls using a support vector machine. A support vector machine model based on event-related EEG data accurately differentiated adults with chronic high-altitude exposure from healthy controls with an accuracy of 92.54% and an AUC of 0.97.
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