This study examined the regulatory effects of autonomic nervous system on aerobic endurance exercise performance in cold exposure, focusing on heart rate recovery (HRR) and heart rate variability (HRV) across genders. Thirty participants (17 males and 13 females) from a university track endurance program, classified as exercise grade II or above, underwent monitoring of HRV in time domain, frequency domain, nonlinear correlation indices and 1 min HRR. Measurements were taken before, during, and after aerobic endurance exercise in cold and normal environments, respectively. The results were as follows. (1) The duration of aerobic endurance exercise completed by all the subjects in cold environment was significantly increased compared with that in normal environment. The 1 min HRR after aerobic endurance exercise in cold environment was significantly lower than that in normal environment, and the decrease in the males was significantly higher than that in the females. (2) The time domain analysis results showed that, prior to the aerobic endurance exercise, there were no significant difference of standard deviation from the mean value of normal to normal intervals (SDNN), root mean square of successive differences (RMSSD), and percentage of adjacent normal-to-normal intervals differing by more than 50 ms (pNN50) between cold and normal environments. During aerobic endurance exercise in cold environment, SDNN, RMSSD and pNN50 were significantly higher than those in normal environment, with the females showing significantly greater increases compared with those of the males. The levels of SDNN, RMSSD and pNN50 in the males at different time points under different environments were significantly lower than those in the quiet state; The levels of SDNN and RMSSD of the females at different time points under different environments were significantly lower than those in the quiet state, while the pNN50 at different time points under cold environments was significantly lower than that in the quiet state. (3) Frequency domain analysis results showed that, prior to the aerobic endurance exercise, there was no significant difference of high frequency normalized units [HF (n.u.)], low frequency normalized units [LF (n.u.)] and LF/HF ratio between cold and normal environments. During aerobic endurance exercise in cold environment, the levels of HF (n.u.) significantly increased compared to normal environment in the females, while LF (n.u.) and LF/HF ratio levels significantly decreased compared to normal environments. The levels of HF (n.u.), LF (n.u.) and LF/HF ratio of different genders at different time points in the different environments showed no significant changes, compared to those in the quiet state. (4) Non-linear analysis results showed a significant increase in SD1 (standard deviation perpendicular to the line-of-identity)/SD2 (standard deviation along the line-of-identity) ratio during aerobic endurance exercise in cold environment in the females, while no significant changes were observed in the males. SD1/SD2 ratios in the males at different time points and in the females at 1 min under cold environments were significantly higher than those in the quiet state. These findings suggest that aerobic endurance performance increases during cold exposure, accompanied by gender-specific differences in the regulation of autonomic nervous system. Females exhibit higher vagal activity and faster autonomic nervous system recovery compared to males.
Humans ; Male ; Female ; Heart Rate/physiology* ; Cold Temperature ; Exercise/physiology* ; Physical Endurance/physiology* ; Autonomic Nervous System/physiology* ; Young Adult ; Adult ; Sex Factors

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological signal measurement. This approach enhances the model's generalization ability by sharing parameters and features between similar tasks, even in data-scarce environments. However, traditional multi-task physiological signal measurement methods face challenges such as feature conflicts between tasks, task imbalance, and excessive model complexity, which limit their application in complex environments. To address these issues, this paper proposes an enhanced multi-scale spatiotemporal network (EMSTN) based on Eulerian video magnification (EVM), super-resolution reconstruction and convolutional multilayer perceptron. First, EVM is introduced in the input stage of the network to amplify subtle color and motion changes in the video, significantly improving the model's ability to capture pulse and respiratory signals. Additionally, a super-resolution reconstruction module is integrated into the network to enhance the image resolution, thereby improving detail capture and increasing the accuracy of facial action unit (AU) tasks. Then, convolutional multilayer perceptron is employed to replace traditional 2D convolutions, improving feature extraction efficiency and flexibility, which significantly boosts the performance of heart rate and respiratory rate measurements. Finally, comprehensive experiments on the Binghamton-Pittsburgh 4D Spontaneous Facial Expression Database (BP4D+) fully validate the effectiveness and superiority of the proposed method in multi-task physiological signal measurement.
Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Face/physiology* ; Video Recording ; Facial Expression ; Heart Rate ; Algorithms

Alzheimer's disease (AD) is the most common degenerative disease of the nervous system. Studies have found that the 40 Hz pulsed magnetic field has the effect of improving cognitive ability in AD, but the mechanism of action is not clear. In this study, APP/PS1 double transgenic AD model mice were used as the research object, the water maze was used to group dementia, and 40 Hz/10 mT pulsed magnetic field stimulation was applied to AD model mice with different degrees of dementia. The behavioral indicators, mitochondrial samples of hippocampal CA1 region and electrocardiogram signals were collected from each group, and the effects of 40 Hz pulsed magnetic field on mouse behavior, mitochondrial kinetic indexes and heart rate variability (HRV) parameters were analyzed. The results showed that compared with the AD group, the loss of mitochondrial crest structure was alleviated and the mitochondrial dynamics related indexes were significantly improved in the AD + stimulated group ( P < 0.001), sympathetic nerve excitation and parasympathetic nerve inhibition were improved, and the spatial cognitive memory ability of mice was significantly improved ( P < 0.05). The preliminary results of this study show that 40 Hz pulsed magnetic field stimulation can improve the mitochondrial structure and mitochondrial kinetic homeostasis imbalance of AD mice, and significantly improve the autonomic neuromodulation ability and spatial cognition ability of AD mice, which lays a foundation for further exploring the mechanism of ultra-low frequency magnetic field in delaying the course of AD disease and realizing personalized neurofeedback therapy for AD.
Animals ; Heart Rate/physiology* ; Mice ; Alzheimer Disease/therapy* ; Mice, Transgenic ; Mitochondrial Dynamics/radiation effects* ; Magnetic Field Therapy/methods* ; Magnetic Fields ; Disease Models, Animal ; Mitochondria ; Male ; Maze Learning ; Cognition ; Dementia/therapy*

Exercise intervention is an important non-pharmacological intervention for various diseases, and establishing precise exercise load assessment techniques can improve the quality of exercise intervention and the efficiency of disease prevention and control. Based on data collection from wearable devices, this study conducts nonlinear optimization and empirical verification of the original "Fitness-Fatigue Model". By constructing a time-varying attenuation function and specific coefficients, this study develops an optimized mathematical model that reflects the nonlinear characteristics of training responses. Thirteen participants underwent 12 weeks of moderate-intensity continuous cycling, three times per week. For each training session, external load (actual work done) and internal load (heart rate variability index) data were collected for each individual to conduct a performance comparison between the optimized model and the original model. The results show that the optimized model demonstrates a significantly improved overall goodness of fit and superior predictive ability. In summary, the findings of this study can support dynamic adjustments to participants' training programs and aid in the prevention and control of chronic diseases.
Humans ; Wearable Electronic Devices ; Exercise/physiology* ; Models, Theoretical ; Heart Rate/physiology* ; Exercise Therapy

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.
Humans ; Autonomic Nervous System/physiology* ; Algorithms ; Aging/physiology* ; Electrocardiography/methods* ; Heart Rate/physiology* ; Wavelet Analysis ; Aged ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

BACKGROUND: Neck cooling is a practical method for preventing heat-related illness, however, its effectiveness in general workers is not well established. This study aimed to assess the effects of neck cooling on core body temperature and other physiological markers during exercise in a hot environment. METHODS: This randomized crossover trial was conducted from November 2023 to April 2024 at the Shared-Use Research Center at UOEH. Fourteen healthy adult males participated in the study under two conditions: with neck cooling (COOL) and without neck cooling (CON). All participants completed both conditions, and the order of condition assignment was determined by a random draw. Participants first rested for 10 minutes in a 28.0 °C, 50% relative humidity environment, followed by a rest in a 35.0 °C, 50% relative humidity environment for another 10 minutes. In the COOL condition, participants wore a neck cooler containing 1,200 g of ice while exercising at 50% Heart Rate Reserve on a bicycle ergometer for 20 minutes. Afterward, they rested for 15 minutes in the hot environment while still wearing the cooler. MAIN OUTCOME MEASURES: Core body temperature (rectal and esophageal), forehead skin temperature, and heart rate were continuously monitored and compared using a mixed model. Estimated sweat volume was calculated based on changes in body weight before and after the experiment. RESULTS: At the end of the rest period, no significant differences were observed between the COOL and CON conditions in rectal temperature (37.76 ± 0.18 °C versus 37.75 ± 0.24 °C, p = 0.9493), esophageal temperature (37.75 ± 0.30 °C versus 37.76 ± 0.23 °C, p = 0.7325), forehead skin temperature (36.87 ± 0.29 °C versus 36.88 ± 0.27 °C, p = 0.2160), or heart rate (104.18 ± 7.56 bpm versus 107.52 ± 7.40 bpm, p = 0.1035). Estimated sweat loss was similar between conditions (578 ± 175 g for CON versus 572 ± 242 g for COOL, p = 0.5066). While more participants felt cooler in the COOL condition, RPE showed no significant difference. CONCLUSION Neck cooling did not significantly affect core temperature or perceived exertion. Maintaining close contact with the skin at sufficiently low temperatures or utilizing cooling methods that prevent excessive negative feedback may be necessary to enhance the effectiveness of neck cooling.
Humans ; Male ; Cross-Over Studies ; Exercise/physiology* ; Adult ; Neck/physiology* ; Hot Temperature/adverse effects* ; Young Adult ; Body Temperature ; Heart Rate ; Skin Temperature ; Body Temperature Regulation ; Cold Temperature

OBJECTIVE: To investigate the association between short-term exposure to indoor total volatile organic compounds (TVOC) and nocturnal heart rate variability (HRV) among young female adults. METHODS: This panel study recruited 50 young females from one university in Beijing, China from December 2021 to April 2022. All the participants underwent two sequential visits. During each visit, real time indoor TVOC concentration was monitored using an indoor air quality detector. The real time levels of indoor temperature, relative humidity, noise, carbon dioxide and fine particulate matter were monitored using a temperature and humidity meter, a noise meter, a carbon dioxide meter and a particulate counter, respectively. HRV parameters were measured using a 12-lead Holter. Mixed-effects models were used to evaluate the association between the TVOC and HRV parameters and establish the exposure-response relationships, and two-pollutant models were applied to examine the robustness of the results. RESULTS: The mean age of the 50 female subjects was (22.5±2.3) years, and the mean body mass index was (20.4±1.9) kg/m². During this study, the median (interquartile range) of indoor TVOC concentrations was 0.069 (0.046) mg/m³, the median (interquartile range) of indoor temperature, relative humidity, carbon dioxide concentration, noise level and fine particulate matter concentration were 24.3 (2.7) ℃, 38.5% (15.0%), 0.1% (0.1%), 52.7 (5.8) dB(A) and 10.3 (21.5) μg/m³, respectively. Short-term exposure to indoor TVOC was associated with significant changes in time-domain and frequency-domain HRV parameters, and the exposure metric for most HRV parameters with the most significant changes was 1 h-moving average. Along with a 0.01 mg/m³ increment in 1 h-moving average concentration of indoor TVOC, this study observed decreases of 1.89% (95%CI: -2.28%, -1.50%) in standard deviation of all normal to normal intervals (SDNN), 1.92% (95%CI: -2.32%, -1.51%) in standard deviation of average normal to normal intervals (SDANN), 0.64% (95%CI: -1.13%, -0.14%) in percentage of adjacent NN intervals differing by more than 50 ms (pNN50), 3.52% (95%CI: -4.30%, -2.74%) in total power (TP), 5.01% (95%CI: -6.21%, -3.79%) in very low frequency (VLF) power, and 4.36% (95%CI: -5.16%, -3.55%) in low frequency (LF) power. The exposure-response curves showed that indoor TVOC was negatively correlated with SDNN, SDANN, TP, and VLF when the concentration exceeded 0.1 mg/m³. The two-pollutant models indicated that the results were generally robust after controlling indoor noise and fine particulate matter. CONCLUSION Short-term exposure to indoor TVOC was associated with significant negative changes in nocturnal HRV of young women. This study provides an important scientific basis for relevant prevention and control measures.
Humans ; Female ; Adult ; Young Adult ; Air Pollutants/analysis* ; Heart Rate/physiology* ; Volatile Organic Compounds/analysis* ; Carbon Dioxide ; Particulate Matter/adverse effects* ; Environmental Pollutants

Respiration protocols have been developed to manipulate mental states, including their use for therapeutic purposes. In this systematic review, we discuss evidence that respiration may play a fundamental role in coordinating neural activity, behavior, and emotion. The main findings are: (1) respiration affects the neural activity of a wide variety of regions in the brain; (2) respiration modulates different frequency ranges in the brain's dynamics; (3) different respiration protocols (spontaneous, hyperventilation, slow or resonance respiration) yield different neural and mental effects; and (4) the effects of respiration on the brain are related to concurrent modulation of biochemical (oxygen delivery, pH) and physiological (cerebral blood flow, heart rate variability) variables. We conclude that respiration may be an integral rhythm of the brain's neural activity. This provides an intimate connection of respiration with neuro-mental features like emotion. A respiratory-neuro-mental connection holds the promise for a brain-based therapeutic usage of respiration in mental disorders.
Humans ; Respiration ; Brain ; Hyperventilation ; Heart Rate/physiology* ; Lung