Normal heart function depends on complex regulation by the brain, and abnormalities in the brain‒heart axis affect various diseases, such as myocardial infarction and anxiety disorders. However, systematic tracking of the brain regions associated with the input and output of the heart is lacking. In this study, we injected retrograde transsynaptic pseudorabies virus (PRV) and anterograde transsynaptic herpes simplex virus (HSV) into the left ventricular wall of mice to identify the whole-brain regions associated with the input to and output from the heart. We successfully detected PRV and HSV expression in at least 170 brain subregions in both male and female mice. Sex differences were discovered mainly in the hypothalamus and medulla, with male mice exhibiting greater correlation and hierarchical clustering than female mice, indicating reduced similarity and increased modularity of virus expression patterns in male mice. Further graph theory and multiple linear regression analysis of different injection timelines revealed that hub regions of PRV had highly similar clusters, with different brain levels, suggesting a top-down, hierarchically transmitted neural control pattern of the heart. Hub regions of HSV had scattered clusters, with brain regions gathered in the cortex and brainstem, suggesting a bottom-up, leapfrog, multipoint neural sensing pattern of the heart. Both patterns contain many hub brain regions that have been previously overlooked in brain‒heart axis studies. These results provide brain targets for future research and will lead to deeper insight into the brain mechanisms involved in specific heart conditions.
Animals ; Male ; Female ; Heart/physiology* ; Mice ; Herpesvirus 1, Suid ; Brain/physiology* ; Mice, Inbred C57BL ; Brain Mapping ; Efferent Pathways/physiology* ; Afferent Pathways/physiology* ; Simplexvirus ; Sex Characteristics

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

Heart sounds are critical for early detection of cardiovascular diseases, yet existing studies mostly focus on traditional signal segmentation, feature extraction, and shallow classifiers, which often fail to sufficiently capture the dynamic and nonlinear characteristics of heart sounds, limit recognition of complex heart sound patterns, and are sensitive to data imbalance, resulting in poor classification performance. To address these limitations, this study proposes a novel heart sound classification method that integrates improved Mel-frequency cepstral coefficients (MFCC) for feature extraction with a convolutional neural network (CNN) and a deep Transformer model. In the preprocessing stage, a Butterworth filter is applied for denoising, and continuous heart sound signals are directly processed without segmenting the cardiac cycles, allowing the improved MFCC features to better capture dynamic characteristics. These features are then fed into a CNN for feature learning, followed by global average pooling (GAP) to reduce model complexity and mitigate overfitting. Lastly, a deep Transformer module is employed to further extract and fuse features, completing the heart sound classification. To handle data imbalance, the model uses focal loss as the objective function. Experiments on two public datasets demonstrate that the proposed method performs effectively in both binary and multi-class classification tasks. This approach enables efficient classification of continuous heart sound signals, provides a reference methodology for future heart sound research for disease classification, and supports the development of wearable devices and home monitoring systems.
Heart Sounds/physiology* ; Humans ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Cardiovascular Diseases/diagnosis* ; Classification Algorithms

BACKGROUND: Angiotensin receptor neprilysin inhibitors (ARNIs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers (BBs), and mineralocorticoid receptor antagonists (MRAs) are the cornerstones in treating heart failure with reduced ejection fraction (HFrEF). Sodium-glucose cotransporter 2 inhibitors (SGLT-2is) are included in HFrEF treatment guidelines. However, the effect of SGLT-2i and the five drugs on HFrEF have not yet been systematically evaluated. METHODS: PubMed, Embase, and the Cochrane Library were searched for randomized controlled trials (RCTs) from inception dates to September 23, 2022. Additional trials from previous relevant reviews and references were also included. The primary outcomes were changes in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter/dimension (LVEDD), left ventricular end-systolic diameter/dimension (LVESD), left ventricular end-diastolic volume (LVEDV), and left ventricular end-systolic volume (LVESV), left ventricular end-systolic volume index (LVESVI), and left ventricular end-diastolic volume index (LVEDVI). Secondary outcomes were New York Heart Association (NYHA) class, 6-min walking distance (6MWD), B-type natriuretic peptide (BNP) level, and N-terminal pro-BNP (NT-proBNP) level. The effect sizes were presented as the mean difference (MD) with 95% confidence interval (CI). RESULTS: We included 68 RCTs involving 16,425 patients. Compared with placebo, ARNI + BB + MRA + SGLT-2i was the most effective combination to improve LVEF (15.63%, 95% CI: 9.91% to 21.68%). ARNI + BB + MRA + SGLT-2i (5.83%, 95% CI: 0.53% to 11.14%) and ARNI + BB + MRA (3.83%, 95% CI: 0.72% to 6.90%) were superior to the traditional golden triangle ACEI + BB + MRA in improving LVEF. ACEI + BB + MRA + SGLT-2i was better than ACEI + BB + MRA (-8.05 mL/m 2 , 95% CI: -14.88 to -1.23 mL/m 2 ) and ACEI + BB + SGLT-2i (-18.94 mL/m 2 , 95% CI: -36.97 to -0.61 mL/m 2 ) in improving LVEDVI. ACEI + BB + MRA + SGLT-2i (-3254.21 pg/mL, 95% CI: -6242.19 to -560.47 pg/mL) was superior to ARB + BB + MRA in reducing NT-proBNP. CONCLUSIONS: Adding SGLT-2i to ARNI/ACEI + BB + MRA is beneficial for reversing cardiac remodeling. The new quadruple drug "ARNI + BB + MRA + SGLT-2i" is superior to the golden triangle "ACEI + BB + MRA" in improving LVEF. REGISTRATION PROSPERO; No. CRD42022354792.
Humans ; Heart Failure/physiopathology* ; Stroke Volume/physiology* ; Angiotensin Receptor Antagonists/therapeutic use* ; Angiotensin-Converting Enzyme Inhibitors/therapeutic use* ; Sodium-Glucose Transporter 2 Inhibitors/therapeutic use* ; Randomized Controlled Trials as Topic ; Mineralocorticoid Receptor Antagonists/therapeutic use* ; Adrenergic beta-Antagonists/therapeutic use*

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