Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

Objectives The aim of this study was to determine whether ¹¹C-hydroxyephedrine (¹¹C-HED) can predict adverse events including all-cause death in Japanese patients with left ventricular (LV) dysfunction. Background Although ¹¹C-HED PET has been used to assess cardiac sympathetic innervation in various disease conditions, data on their prognostic value are limited. Methods Sixty patients (mean LVEF, 42 ± 14 %) with LV dysfunction (42 ischemic and 18 non-ischemic heart disease) underwent ¹¹C-HED PET. Myocardial retention was calculated for ¹¹C-HED PET as a measure of cardiac sympathetic neuronal integrity. Statistical analysis was performed using Cox proportional hazards regression and log-rank test. Results Thirteen deaths (7 cardiac and 6 non-cardiac deaths) occurred during a mean follow-up period of 33 ± 23 months. The patients with death were associated with significantly lower ¹¹C-HED retention (7.1 ± 2.1 vs 9.0 ± 2.4, p = 0.015) than those without death. The hazard ratio for global ¹¹C-HED retention per unit (/min) was 0.762 (p = 0.039), which remained significant in multivariate analysis. When the patients were divided into the high (≥8.5) and low (<8.5) ¹¹C-HED retention groups, the low ¹¹C-HED retention group was associated with significantly poorer survival than the high ¹¹C-HED retention group (p = 0.004). Conclusion The low global ¹¹C-HED retention is a marker of poor overall survival in patients with LV dysfunction in this study.