{'fraction_non_alphanumeric': 0.02501786990707648, 'fraction_numerical': 0.010721944245889922, 'mean_word_length': 5.140350877192983, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detection of Vigil And Fatigue States During The Execution of Laparoscopic Tasks Based On EEG Patterns


 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. The EEG signals of 18 physician students were analyzed within theta band (4 - 8 Hz) over front-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. The EEG signal processing pipeline consisted in pre-processing based on individual component analysis, absolute band power estimates, and Support Vector Machine classification. The 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. For this purpose, EEG recording are provided.