Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

Japanese encephalitis is a neuropathological disorder caused by Japanese encephalitis virus (JEV), which is characterized by severe pathological neuroinflammation and damage to the blood–brain barrier (BBB). Inflammatory cytokines/chemokines can regulate the expression of tight junction (TJ) proteins and are believed to be a leading cause of BBB disruption, but the specific mechanisms remain unclear. IP-10 is the most abundant chemokine produced in the early stage of JEV infection, but its role in BBB disruption is unknown. The administration of IP-10-neutralizing antibody ameliorated the decrease in TJ proteins and restored BBB integrity in JEV-infected mice. In vitro study showed IP-10 and JEV treatment did not directly alter the permeability of the monolayers of endothelial cells. However, IP-10 treatment promoted tumor necrosis factor alpha (TNF-α) production and IP-10-neutralizing antibody significantly reduced the production of TNF-α. Thus, TNF-α could be a downstream cytokine of IP-10, which decreased TJ proteins and damaged BBB integrity. Further study indicated that JEV infection can stimulate upregulation of the IP-10 receptor CXCR3 on astrocytes, resulting in TNF-α production through the JNK-c-Jun signaling pathway. Consequently, TNF-α affected the expression and cellular distribution of TJs in brain microvascular endothelial cells and led to BBB damage during JEV infection. Regarding regulation of the BBB, the IP-10/TNF-α cytokine axis could be considered a potential target for the development of novel therapeutics in BBB-related neurological diseases.