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Objective To establish a machine learning model for driving fatigue recognition based on heart rate variability (HRV) and respiratory rate (RESP), and identify the optimal subsets of features. Methods From June 2021 to December 2021, 20 healthy male volunteers between the ages of 20 and 30 were recruited from the Army Medical University to participate in the fatigue driving experiment. The electrocardiographic (ECG) signals under normal sleep and sleep deprivation during driving tasks were recorded, and 18-dimensional fatigue-related HRV feature values were extracted. The HRV features that differ between awake and fatigue states were selected, which were further combined with RESP as the feature set. Moreover, 5 classic machine learning methods were adopted and compared, including support vector machines (SVM), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT) and logistic regression (LR). The optimal feature subset was screened, and then a fatigue recognition model was established. Results The ratio of low frequency power to high frequency power (LF/HF), RESP, mean RR interval (Mean RR), sample entropy (SampEn), and detrended fluctuation analysis short-term slope (DFAα1) constituted the effective feature subsets of fatigue recognition, which achieved the best classification effect in SVM, with an accuracy of 87.03%, a sensitivity of 87.07% and a specificity of 87.13% in fatigue recognition. Among them, LF/HF and RESP were found as the most important indicators of driving fatigue identification, with an accuracy of each model in the above 2 dimensions reaching more than 80%. In addition, SVM and LR showed better overall performance, with an accuracy, sensitivity and specificity of 84.99%, 85.13%, 82.65% for SVM and 84.43%, 86.49%, 82.02% for LR, respectively. Conclusion LF/HF and RESP are the effective features for driving fatigue recognition. In the driving fatigue recognition model based on HRV features and RESP, the overall performance of SVM and LR models is better than the other models. |
