Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.
Objective To preliminarily analyse the phenomenon of the first seizure in patients with epilepsy while driving a motor vehicle, and discuss its harms and possible coping strategies. Methods The first seizure while driving a motor vehicle was investigated among epilepsy patients who attended the First Affiliated Hospital of Soochow University from June 2020 to March 2023. Results A total of five patients had their first seizure while driving a motor vehicle, all causing traffic accidents. One patient had a generalized tonic-clonic seizure with a first epileptic seizure, two had focal seizures with impaired consciousness that progressed to generalized seizures, and two had focal seizures with impaired awareness. One of the patients caused a fatal traffic accident, leading to the death of another person who riding the electric bicycle. In this case, the patient's driving license was revoked. The other four patients continued to drive after the first seizure. One patient terminated driving 5 months after the diagnosis of epilepsy. Two patients drove less since then, and one patient continued driving as before. Two patients experienced seizures again while driving, and one of them coincidentally had his second seizure while driving. Conclusions The first seizure while driving may not be uncommon, reflecting the severity of epilepsy and driving, in which traffic accidents can be fatal. People with epilepsy are currently prohibited from driving in China. After the first seizure, patients should immediately stop driving and go to see an epileptologist, avoiding further endangering themselves and the public.