Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications.

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving.

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual's cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers.

OBJECTIVE: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers. APPROACH: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20). MAIN RESULTS: Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores. SIGNIFICANCE: Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.

Peak and averaged bicoherence for different EEG patterns during general anaesthesia.

BACKGROUND: Changes in nonlinear neuronal mechanisms of EEG generation in the course of general anaesthesia have been extensively investigated in research literature. A number of EEG signal properties capable of tracking these changes have been reported and employed in anaesthetic depth monitors. The degree of phase coupling between different spectral components is a marker of nonlinear EEG generators and is claimed to be an important aspect of BIS. While bicoherence is the most direct measure of phase coupling, according to published research it is not directly used in the calculation of BIS, and only limited studies of its association with anaesthetic depth and level of consciousness have been published. This paper investigates bicoherence parameters across equal band and unequal band bifrequency regions, during different states of anaesthetic depth relating to routine clinical anaesthesia, as determined by visual inspection of EEG. METHODS: 41 subjects scheduled for day surgery under general anaesthesia were recruited into this study. EEG bicoherence was analysed using average and smoothed-peak estimates calculated over different regions on the bifrequency plane. Statistical analysis of associations between anaesthetic depth/state of consciousness and bicoherence estimates included linear regression using generalised linear mixed effects models (GLMs), ROC curves and prediction probability (Pk). RESULTS: Bicoherence estimates for the δ_θ region on the bifrequency plane were more sensitive to anaesthetic depth changes compared to other bifrequency regions. Smoothed-peak bicoherence displayed stronger associations than average bicoherence. Excluding burst suppression and large transients, the δ_θ peak bicoherence was significantly associated with level of anaesthetic depth (z = 25.74, p < 0.001 and R2 = 0.191). Estimates of Pk for this parameter were 0.889(0.867-0.911) and 0.709(0.689-0.729) respectively for conscious states and anaesthetic depth levels (comparable BIS estimates were 0.928(0.905-0.950) and 0.801(0.786-0.816)). Estimates of linear regression and areas under ROC curves supported Pk findings. Bicoherence for eye movement artifacts were the most distinctive with respect to other EEG patterns (average |z| value 13.233). CONCLUSIONS: This study quantified associations between deepening anaesthesia and increase in bicoherence for different frequency components and bicoherence estimates. Increase in bicoherence was also established for eye movement artifacts. While identified associations extend earlier findings of bicoherence changes with increases in anaesthetic drug concentration, results indicate that the unequal band bifrequency region, δ_θ, provides better predictive capabilities than equal band bifrequency regions.

Power analysis of gamma frequencies (30 - 47Hz), adjusting for muscle activity (80 - 97Hz), in anesthesia: a comparison between young adults, middle-aged and the elderly.

This study looks at the role of EEG gamma activity, and the influence of facial EMG (80-97 Hz), in predicting consciousness during anesthesia. It also studies the association between the conventional depth of anesthesia index, BIS (Aspect Medical Systems), and EEG gamma and EMG activity. Data has been collected from 21 adult patients and grouped into young adults (18 - 39 yrs, n=3), middle-aged (40 - 64 yrs, n=10) and the elderly (65+ yrs, n=8). The power of the EEG gamma activity was recorded from Fpz - Mastoid and the power of the EMG was recorded from Fpz - Mastoid and Masseter - Mastoid. It has been found that when considered alone, EEG gamma power is associated with both BIS index and consciousness versus unconsciousness, showing a decrease in power as consciousness is lost. When the effect of EEG gamma power is adjusted for EMG, it is found that generally these associations can be explained by the EMG power alone. There are two exceptions to this. In the young adults group there is a stronger association between BIS index and EEG gamma than there is between BIS index and EMG. In the elderly group, the state of consciousness is equally associated with EEG gamma and EMG recorded from the Masseter, but not with the EMG recorded from Fpz.

Variations in the accuracy of the ECG based detection of obstructive sleep apnoea (OSA) for different numbers of ECG leads and categories of OSA events.

This study investigates the finding that there is a more pronounced change to ECG physiological predictors during apnoea events compared to hypopnoea events and therefore accurate detection of hypopnoea events is likely to be more challenging than detection of apnoea events. The relevant statistical analysis was conducted by generating logistic regression models from the two data sets: the first one containing only the apnoea events and controls and the second data set containing only the hypopnoea events and controls. The discriminating ability of the model from the apnoea data set (AUC = 0.903, CI = 0.888 - 0.920) was significantly superior compared to the model from the hypopnoea data set (AUC = 0.842, CI = 0.817-0.866). The second study objective investigated whether regression models comprising the OSA predictors derived from the two ECG signals performed better than models that involved parameters of a single ECG. The optimised two signal ECG model (AUC = 0.878 and CI = 0.864 - 0.893) outperformed the best single ECG model (AUC = 0.843, CI = 0.826 - 0.860), suggesting that improved results can be achieved using an additional ECG lead.