Fatigue decreases performance in several professional activities. Fatigue can lead to commit technical mistakes, which consequences might be lethal, such as in health area, where a surgical error due to the absence of rest can provoke the patient death. Therefore, this study aims to detect vigil and fatigue (due to lack of sleep) states in medical students through the classification of electroencephalographic (EEG) patterns.
For this purpose, EEG signals of 18 physician students were analyzed within theta band (4–8 Hz) over fronto-central recording sites, and alpha band (8–13 Hz) rhythms over temporal and parieto-occipital recording sites during the execution of laparoscopic tasks before and after their medical duties at the Medical School of Tecnologico de Monterrey. EEG signal processing pipeline consisted in pre-processing based on independent component analysis, power spectral density estimates, and Support Vector Machine classification.
Resulting f-score to differ between vigil and fatigue states was 90.89%, where the first class was slightly more identifiable, reaching a sensitivity of 90.18%.
Based on this outcome, the detection of fatigue in medical students while their laparoscopic training seems achievable and feasible to diminish technical mistakes that could be lethal in health area. This project moves towards including neuro-ergonomics and human factors in medical trainings to improve the skill acquisition, and thus diminishing technical mistakes during surgeries.
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Preprocessed EEG signals of the 18 junior surgeons undertaking the five laparoscopic tasks can be downloaded from https://doi.org/10.6084/m9.figshare.12559547.v1.
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The authors did not receive support from any organization for the submitted work.
Conflict of interest
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
The experimental procedure was formerly approved by the Ethical Committee of the National School of Medicine of the Tecnologico de Monterrey.
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All participants were informed about the experimental procedure and signed a consent form.
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Authors declare no conflict of interest and agree to publish the present work.
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Pérez, Y., Borboa-Gastelum, R., Alonso-Valerdi, L.M. et al. Detection of Vigil and Fatigue States During Laparoscopic Tasks Based on EEG Patterns: Towards Neuroergonomics in Medical Training. J. Med. Biol. Eng. (2021). https://doi.org/10.1007/s40846-021-00659-3
- EEG Patterns