Drowsiness detection has a significant importance in aviation industry. Electroencephalogram (EEG) has been extensively studied to characterize drowsiness, nevertheless, its behavior with respect to accurately annotated drowsy periods remains to be investigated. At present, Karolinska Sleepiness Scale (KSS) and Psychomotor Vigilance Task (PVT) are widely used references for subjective drowsiness analysis. However, their practical application in real time monitoring of alertness is limited. To address this limitation, we combined Seeing Machines Driver Monitoring System (DMS) and electrooculogram (EOG) for localization of microsleep (MS) events and studied EEG spectral behavior during MS events. EEG, EOG, and facial behavior data were recorded simultaneously from sixteen commercially-rated pilots during simulated flight. Relative spectral power in delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) in frontal, central, temporal, parietal, and occipital brain regions were analyzed. Compared to baseline, delta power reduced during MS (p<0.05 in all regions), alpha power increased during MS (p0.05). The research findings highlight the capability of EEG delta and alpha spectrum towards characterizing MS events, therefore, with consideration to user acceptability, application towards drowsiness detection is plausible via EEG electrodes embedded in the typical aviation headset.

An intelligent Internet-of-Things (IoT) hardware system in the field tea plantations was built, comprising collection of tea images by HD zoom cameras in a cluster structure and deployment of the detection model by cluster-head edge computing nodes. Data was sent to customer premise equipment through edge nodes and gateways and then to cloud platforms, which provided a hardware platform for identifying remote tea disease online. Field-placed cameras were used as the main acquisition means to study various diseases on Yashixiang, a typical variety of Chaozhou Dancong tea, in different seasons and weather conditions and shooting angles in a natural year period with complex backgrounds. In turn, we constructed a natural environment high-quality dataset covering major diseases e.g., tea anthracnose, tea leaf blight, tea grey blight, Pseudocercospora theae, etc. and explored the feasibility of deep learning algorithms for automatic identification of Chaozhou Dancong tea diseases. Results showed that the recognition rate of Swim Transformer reached 94% in complex natural environments. This paper demonstrated the effectiveness of the dataset and the feasibility of deep learning algorithms applied to the automatic identification of diseases of Chaozhou Dancong tea, laying a foundation for the practical application of the technology in complex natural environments.

The K-complex, a distinctive brain wave pattern observed during sleep, holds significant importance in sleep research. Traditional manual annotation of electroence-phalogram (EEG) signals is time-consuming,labor-intensive and prones to errors. Although some studies have focused on the automatic detection of K-complexes, they still struggle to effectively address the issues caused by data imbalance. Therefore, the study demonstrates a method for detecting sleep K-complexs based on multi-domain feature extraction, KMeansSMOTE technology, and the combination of BOHB-Tuned XGBoost is proposed. There are four steps below. Firstly, the EEG data is preprocessed and 22 features are extracted. Then, the KMeansSMOTE oversampling technique is applied to generate samples for the minority class in the imbalanced data, aiming to achieve a balance between positive and negative samples. Next, XGBoost is employed for ranking feature importance and it is integrated with correlation analysis for feature selection. Finally, the BOHB-Tuned XGBoost model is utilized for K-complex detection using the selected features. Experimental results show that the model has an average accuracy of 96.60%, precision of 97.67%, sensitivity of 89.05%, specificity of 99.1%, and an F1 score of 92.99%. The method proposed in this study not only addresses the problem of data imbalance effectively but also demonstrates a more stable, high classification performance. This can assist sleep experts in analyzing EEG signals.

Impaired autonomic reflex is associated with orthostatic hypotension and falls in ischemic stroke patients. Understanding post-stroke temporal characteristics of autonomic reflex can assist in development of optimal therapy for improving autonomic function and patients’ quality of life. To this end, autonomic reflex 6±2 years post-stroke was characterized during orthostatic challenge induced by sit-to-stand test. RR intervals (RR), heart rate (HR), systolic blood pressure (SBP), mean arterial pressure (MAP), and diastolic blood pressure (DBP) were extracted from simultaneously recorded electrocardiogram (ECG) and blood pressure waveform from stroke patients and age-matched controls. Shock index (SI), modified shock index (MSI), spectral power of RR in low-frequency (LF, 0.04-0.15 Hz) and high-frequency (HF, 0.15-0.4 Hz) bands and LF baroreflex (SBP→RR) gain were calculated. We observed higher HR, SI, and MSI during both phases in stroke patients compared to controls. Standing caused an increase (p0.10) in controls during standing compared to sitting. Stroke patients had lower (p<0.05) SBP→RR gain during both phases of the study compared to controls. Lastly, standing shifted the sympathovagal balance to sympathetic activity (elevated LF/HF ratio, p=0.03) in controls but not in patients. The study findings highlight the long-term effects of ischemic stroke on autonomic reflex and underscore the need for novel rehabilitation schemes to mitigate its adverse effect on autonomic reflex. Furthermore, the method proposed in this manuscript can be utilized to evaluate autonomic reflex continuously during rehabilitation, which could assist clinicians in customizing therapy for optimal recovery of autonomic function.