Intermittent hypoxia training effectively protects against cognitive decline caused by acute hypoxia exposure.

Intermittent hypoxia training (IHT) is a promising approach that has been used to induce acclimatization to hypoxia and subsequently lower the risk of developing acute mountain sickness (AMS). However, the effects of IHT on cognitive and cerebrovascular function after acute hypoxia exposure have not been characterized. In the present study, we first confirmed that the simplified IHT paradigm was effective at relieving AMS at 4300 m. Second, we found that IHT improved participants' cognitive and neural alterations when they were exposed to hypoxia. Specifically, impaired working memory performance, decreased conflict control function, impaired cognitive control, and aggravated mental fatigue induced by acute hypoxia exposure were significantly alleviated in the IHT group. Furthermore, a reversal of brain swelling induced by acute hypoxia exposure was visualized in the IHT group using magnetic resonance imaging. An increase in cerebral blood flow (CBF) was observed in multiple brain regions of the IHT group after hypoxia exposure as compared with the control group. Based on these findings, the simplified IHT paradigm might facilitate hypoxia acclimatization, alleviate AMS symptoms, and increase CBF in multiple brain regions, thus ameliorating brain swelling and cognitive dysfunction.

Reflective oxygen saturation monitoring at hypothenar and its validation by human hypoxia experiment.

BACKGROUND: Pulse oxygen saturation (SpO2) is an important parameter for healthcare, and wearable sensors and systems for SpO2 monitoring have become increasingly popular. The aim of this paper is to develop a novel SpO2 monitoring system, which detects photoplethysmographic (PPG) signals at hypothenar with a reflection-mode sensor embedded into a glove. METHODS: A special photo-detector section was designed with two photodiodes arranged symmetrically to the red and infrared light-emitting diodes (LED) to enhance the signal quality. The reflective sensor was placed in a soft silicon substrate sewn in a glove to fit the surface of the hypothenar. To lower the power consumption, the LED driving current was reduced and energy-efficient electronic components were applied. The performance for PPG signal detection and SpO2 monitoring was evaluated by human hypoxia experiments. Accelerometer-based adaptive noise cancellation (ANC) methods applying the least mean squares (LMS) and recursive least squares (RLS) algorithms were studied to suppress motion artifact. RESULTS: A total of 20 subjects participated in the hypoxia experiment. The degree of comfort for wearing this system was accepted by them. The PPG signals were detected effectively at SpO2 levels from about 100-70%. The experiment validated the accuracy of the system was 2.34%, compared to the invasive measurements. Both the LMS and RLS algorithms improved the performance during motion. The total current consumed by the system was only 8 mA. CONCLUSIONS: It is feasible to detect PPG signal and monitor SpO2 at the location of hypothenar. This novel system can achieve reliable SpO2 measurements at different SpO2 levels and on different individuals. The system is light-weighted, easy to wear and power-saving. It has the potential to be a solution for wearable monitoring, although more work should be conducted to improve the motion-resistant performance significantly.

A Siamese Swin-Unet for image change detection.

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

Development of a quantitative measurement on visual clutter in see through display.

Objective: With the wide use of transmission displays to improve operation performance, the display information highlights clutter because of the contradiction between the massive amount of information and limited display area. Our study aimed to develop a quantitative measurement for declutter design and appraisal. Methods: Using the ergonomics research system of characters and symbols in a see-through cockpit display, we set the simulated flight task interface at four pixel scale levels by enlarging all the display elements in a certain ratio. Flight task videos of 12 clutter degrees were recorded using each flight interface matched with three flight scene complexity levels. A total of 60 pilots completed the visual search tasks in the flight task video while the eye tracker was used to record the view path in real time. Visual search performance was analyzed to study the effect of various clutter factors and levels on pilots' performance in visual search tasks, and acquire quantitative clutter measure parameters. Results: GLM univariate test revealed that there were significant differences among the fixation time in areas of interest (AOI), total Fixation point number, total fixation time at four pixel scale levels, and three flight scene complexity levels (P < 0.05). Visual search performance declined after the cutoff point, while the clutter degree increased. According to the visual search performance data, the recommend feature congestion upper pixel number limit in a 600*800 display was 18,576, and the pixel ratio was 3.87%. Conclusion: A quantitative measurement for declutter design and appraisal of cockpit displays was developed, which can be used to support see-through display design.

Preliminary Intermittent Hypoxia Training Alleviates the Damage of Sustained Normobaric Hypoxia on Human Hematological Indexes and Cerebral White Matter.

Zhang, Guangbo, Yanzhao Zhou, Zhengtao Cao, Xiang Cheng, Xiangpei Yue, Tong Zhao, Ming Zhao, Yongqi Zhao, Ming Fan, and Lingling Zhu. Preliminary intermittent hypoxia training alleviates the damage of sustained normobaric hypoxia on human hematological indexes and cerebral white matter. High Alt Med Biol. 23:273-283, 2022. Background: We aimed to examine the effects of preliminary intermittent hypoxia training (IHT) on human hematological indexes and cerebral white matter (WM) after exposure to a simulated altitude of 4,300 m. Methods: We recruited 20 young healthy volunteers. Participants were then randomized to either the IHT group (n = 10) or the control group (n = 10). We measured the physiological function of the control group at sea level and after exposure to a simulated altitude of 4,300 m, respectively. The IHT group performed the above tests at three time points: before and after hypoxia training, and after exposure to a simulated altitude of 4,300 m, respectively. Results: We found that mean SpO2 during day 10 of hypoxia training showed a significant increase compared with mean SpO2 on day 1 (88.3% ± 1.5% vs. 90.0% ± 1.6%, p < 0.05), and erythrocyte P50 of post-training was significantly increased compared with pretraining (37.8 ± 2.9 mmHg vs. 45.9 ± 6.4 mmHg, p < 0.05). Mean SpO2 measures after acute exposure to high altitude exhibited a significant difference, with the IHT group showing significantly greater SpO2 than the control group (73.8% ± 3.7% vs. 77.4% ± 3.2%, p < 0.05), and the Lake Louise Score was also lower than the control group (2.55 ± 2.1 vs. 6.67 ± 2.5, p < 0.05). After daily IHT, brain-derived neurotrophic factor plasma levels of participants in the IHT group did not change but significantly increased in response to high-altitude hypoxia (103.5% ± 70.4% vs. 29.7% ± 73.2%, p < 0.05). Interleukin-10 (IL-10) plasma level did not change before and after IHT in the IHT group, whereas the IL-10 plasma level of the control group after high-altitude exposure was significantly higher. Furthermore, we found that fractional anisotropy values in the left corticospinal tract and splenium of the corpus callosum in the IHT group were significantly higher than those in the control group after high-altitude hypoxia. Conclusions: These results demonstrate that IHT alleviates the damage of sustained normobaric hypoxia on human hematological indexes and cerebral WM.

Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features.

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms.

Variations of Time Irreversibility of Heart Rate Variability Under Normobaric Hypoxic Exposure.

In the field of biomedicine, time irreversibility is used to describe how imbalanced and asymmetric biological signals are. As an important feature of signals, the direction of time is always ignored. To find out the variation regularity of time irreversibility of heart rate variability (HRV) in the initial stage of hypoxic exposure, the present study implemented 2 h acute normobaric hypoxic exposure on six young subjects who have no plateau or hypoxia experiences; oxygen concentration was set as 12.9%. Electrocardiogram (ECG) signals were recorded in the whole process and RR interval sequences were extracted. Mathematical operations were executed to transform the difference of adjacent RR intervals into proportion and distance with delay time to conduct time irreversibility analysis of HRV. The same calculating method was implemented on six items randomly picked out from the MIT-BIH normal sinus rhythm database as a control group. Results show that variation of time irreversibility of HRV in a hypoxic environment is different from that in a normoxic environment, time irreversibility indices of a hypoxic group decreases continually at a delay time of 1 and 2, and indices curves of time irreversibility gradually tend to be steady and gather with each other at a delay time of 3 or 4. The control group shows no consistent tendency no matter what the delay time is in the range of 1-4. Our study indicates that in short-time hypoxic exposure, as hypoxic time goes by, regulation of the cardiovascular autonomic nervous system weakens; regulation times and intensity of sympathetic and parasympathetic nerves tend to be equal.

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients.

Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

A pilot application of Korotkoff sound delay time in evaluating cardiovascular status.

BACKGROUND: Korotkoff sounds have been used to measure systolic and diastolic arterial blood pressures noninvasively for over 100 years. However, most of the research concerning the Korotkoff sound were focused on the origin and frequency component analyzing the Korotkoff sound signal. OBJECTIVE: To show that the occurrence time of the Korotkoff sounds for each cardiac cycle demonstrates a characteristic value during the cuff deflating process of blood pressure measurement. METHODS: The Korotkoff sound delay time (KDT) decreases as the cuff pressure P deflates and KDT is a function of arterial transmural pressure. In the present research, an experiment system was established to explore the relationship between the KDT and the cuff pressure in different subjects. RESULTS: A pilot experiment was conducted to obtain different subjects' KDTs and investigate the relationship between KDT and cuff pressure. CONCLUSION: The relationship between KDT and invasive blood pressure was also studied and its potential application in detection of cardiovascular status was discussed.

Detection of genioglossus myoelectric activity using ICA of multi-channel mandible sEMG.

BACKGROUND: Genioglossus myoelectric activity is of great significance in evaluating clinical respiratory function. However, there is a tradeoff in genioglossus EMG measurement with respect to accuracy versus convenience. OBJECTIVE: This paper presents a way to separate the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG through independent component analysis. METHODS: First, intra-oral genioglossus EMGgenioglossus EMG and three-channel mandible sEMG were recorded simultaneously. The FastICA algorithm was applied to three independent components from the sEMG signals. Then the independent components with the intra-oral genioglossus EMG were compared by calculating the Pearson correlation coefficient between them. RESULTS: An examination of 60 EMG samples showed that the FastICA algorithm was effective in separating the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG. The results of analysis were coincident with clinical diagnosis through intra-oral electrodes. CONCLUSIONS: Genioglossus myoelectric activity can be evaluated accurately by multi-channel mandible sEMG, which is non-invasive and easy to record.

A simplified computer model of cardiovascular system with an arm branch.

Non-invasive pressure simulators that regenerate oscillometric waveforms promise an alternative to expensive clinical trials for validating oscillometric noninvasive blood pressure devices. However, existing simulators only provide oscillometric pressure in cuff and thus have a limited accuracy. It is promising to build a physical simulator that contains a synthetic arm with a built-in brachial artery and an affiliated hydraulic model of cardiovascular system. To guide the construction of this kind of simulator, this paper presents a computer model of cardiovascular system with a relatively simple structure, where the distribution of pressures and flows in aorta root and brachial artery can be simulated, and the produced waves are accordant with the physical data. This model can be used to provide the parameters and structure that will be needed to build the new simulator.