No abstract

Abstract-This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.Index Terms-Driver fatigue, electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), entropy, neural network.

Heteroresistance refers to phenotypic heterogeneity of microbial clonal populations under antibiotic stress, and it has been thought to be an allocation of a subset of “resistant” cells for surviving in higher concentrations of antibiotic. The assumption fits the so-called bet-hedging strategy, where a bacterial population “hedges” its “bet” on different phenotypes to be selected by unpredicted environment stresses. To test this hypothesis, we constructed a heteroresistance model by introducing a blaCTX-M-14 gene (coding for a cephalosporin hydrolase) into a sensitive Escherichia coli strain. We confirmed heteroresistance in this clone and that a subset of the cells expressed more hydrolase and formed more colonies in the presence of ceftriaxone (exhibited stronger “resistance”). However, subsequent single-cell-level investigation by using a microfluidic device showed that a subset of cells with a distinguishable phenotype of slowed growth and intensified hydrolase expression emerged, and they were not positively selected but increased their proportion in the population with ascending antibiotic concentrations. Therefore, heteroresistance—the gradually decreased colony-forming capability in the presence of antibiotic—was a result of a decreased growth rate rather than of selection for resistant cells. Using a mock strain without the resistance gene, we further demonstrated the existence of two nested growth-centric feedback loops that control the expression of the hydrolase and maximize population growth in various antibiotic concentrations. In conclusion, phenotypic heterogeneity is a population-based strategy beneficial for bacterial survival and propagation through task allocation and interphenotypic collaboration, and the growth rate provides a critical control for the expression of stress-related genes and an essential mechanism in responding to environmental stresses.

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.