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Algorithm for drowsiness detection based on hybrid brain network parameter optimization
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AI
DOI：10.1016/j.bspc.2024.106344.png)
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Abstract
En 中文
Drowsiness detection is a test designed to detect a person's reaction ability and speed while in a state of fatigue. This type of drowsiness detection has many practical applications in areas like driver monitoring, transportation industry, and workplace safety. In this paper, a new machine learning drowsiness detection algorithm based on hybrid brain network feature extraction is proposed. First, the raw electroencephalogram signals are segmented into 30 s epochs, which are decomposed with wavelet packet transform into 7 layers to extract 30 s sleep characteristic subbands and the 7-th layer subbands. After that, 30 s subbands are divided into 5 s subbands with 50% overlap. At the same time, 30 s subbands are reconstructed by phase space reconstruction (PSR), whose parameters are optimized through energy valley optimizer. Then, based on mutual information and horizontal visual graph, a hybrid brain network is constructed with the 7-th layer subbands, 5 s subbands and PSR subbands, respectively. Finally, network features and time-frequency features are extracted and input into random forest classifier. The performance of the algorithm is evaluated through two tests: subject-independent test and subject-non-independent test. The results show that in Sleep-EDF dataset, accuracy, kappa coefficient, recall, precision and F1-score obtained by the proposed algorithm can reach 94.19%, 88.39%, 95.37%, 92.53% and 93.93%, respectively. In Sleep-EDF Expanded dataset, accuracy, kappa coefficient, recall, precision and F1-score can reach 93.65%, 87.65%, 94.53%, 92.11%, and 92.89%, respectively.
Keywords:
Drowsiness detection
Machine learning
Brain network
PSR
Energy valley optimizer
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