BACKGROUND

Heart rate variability (HRV) is a common tool for assessing autonomic nervous system activity and monitoring training load in athletes. However, limited research has explored how HRV responds to different forms of resistance training, particularly in high-intensity intermittent sports like basketball and football.

This study aims to compare the acute HRV responses between neuromuscular and metabolic training in collegiate athletes involved in high-intensity intermittent sports.

A comparative cross-sectional study with a quasi-experimental crossover design will be employed.

Collegiate athletes will be randomly assigned to undergo both neuromuscular and metabolic training sessions with a one-week wash-out period in between. HRV data will be recorded using the Polar H10 chest strap during each session.

Descriptive statistics will summarize salient participant characteristics and HRV measurements. Inferential analysis will use paired t-tests or Wilcoxon signed-rank tests based on normality, assessed via the Kolmogorov-Smirnov test. All statistical analyses will be conducted using the IBM SPSS (ver.25) with a confidence interval set. at 95% and a critical α equal to 0.05.

Neuromuscular training is expected to elicit higher low-frequency (LF) power and an increased LF/HF ratio, reflecting greater sympathetic activation, while metabolic training is expected to show lower LF power and a decreased LF/HF ratio, indicating a more balanced autonomic response. These findings will offer insights into the differential autonomic impacts of these training modalities.

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
Humans ; Systems Analysis ; Algorithms ; Software ; Heart Rate ; Microcirculation

Objective Dexmedetomidine is a highly selective alpha-2 adrenergic receptor agonist with sedative and analgesic properties but without respiratory depression effect and has been widely used in perioperative anesthesia. Here we performed a systematic review and meta-analysis to evaluate the effect of dexmedetomidine on maintaining perioperative hemodynamic stability in elderly patients.Methods PubMed, Web of Science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Data were searched for randomized-controlled trials (RCTs) on the application of dexmedetomidine in maintaining perioperative hemodynamic stability in elderly patients from their inception to September, 2021. The standardized mean differences (SMD) with 95% confidence interval (CI) were employed to analyze the data. The random-effect model was used for the potential clinical inconsistency.Results A total of 12 RCTs with 833 elderly patients (dexmedetomidine group, 546 patients; control group, 287 patients) were included. There was no significant increase in perioperative heart rate (HR), mean arterial pressure (MAP), and diastolic blood pressure (DBP) in the dexmedetomidine group before and during the operation. In addition, the variations of hemodynamic indexes including HR, MAP, SBP (systolic blood pressure), and DBP were significantly lower in the dexmedetomidine group compared with the control group (HR: SMD = -0.87, 95% CI: -1.13 to -0.62; MAP: SMD = -1.12, 95% CI: -1.60 to -0.63; SBP: SMD = -1.27, 95% CI: -2.26 to -0.27; DBP: SMD = -0.96, 95% CI: -1.33 to -0.59). Subgroup analysis found that with the prolongation of 1.0 μg/kg dexmedetomidine infusion, the patient's heart rate declined in a time-dependent way.Conclusion Dexmedetomidine provides more stable hemodynamics during perioperative period in elderly patients. However, further well-conducted trials are required to assess the effective and safer doses of dexmedetomidine in elderly patients.
Humans ; Aged ; Dexmedetomidine/adverse effects* ; Hemodynamics ; Hypnotics and Sedatives/pharmacology* ; Blood Pressure ; Heart Rate

As a new energy source for atrial fibrillation ablation, electric pulse ablation has higher tissue selectivity and biosafety, so it has a great application prospect. At present, there is very limited research on multi-electrode simulated ablation of histological electrical pulse. In this study, a circular multi-electrode ablation model of pulmonary vein will be built on COMSOL5.5 platform for simulation research. The results show that when the voltage amplitude reaches about 900 V, it can make some positions achieve transmural ablation, and the depth of continuous ablation area formed can reach 3 mm when the voltage amplitude reaches 1 200 V. When the distance between catheter electrode and myocardial tissue is increased to 2 mm, a voltage of at least 2 000 V is required to make the depth of continuous ablation area reach 3 mm. Through the simulation of electric pulse ablation with ring electrode, the research results of this project can provide reference for the voltage selection in the clinical application of electric pulse ablation.
Humans ; Heart Rate ; Atrial Fibrillation/surgery* ; Electrodes ; Catheter Ablation ; Electricity

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.
Humans ; Atrial Fibrillation/diagnosis* ; Support Vector Machine ; Heart Rate ; Algorithms ; Neural Networks, Computer ; Electrocardiography

To comprehensively evaluate the human body's respiratory, circular metabolism and other functions, and to diagnose lung disease, an accurate and reliable pulmonary function test (PFT) is developed. The system is divided into two parts:hardware and software. It realizes the collection of respiratory, pulse oxygen, carbon dioxide, oxygen and other signals, and draws flow-volume curve (FV curve), volume-time curve (VT curve), respiratory waveform, pulse wave, carbon dioxide and oxygen waveform in real time on the upper computer of the PFT system, and conducts signal processing and parameter calculation for each signal. The experimental results prove that the system is safe and reliable, it can accurately measure the basic functions of human body, and provide reliable parameters, and has good application prospects.
Humans ; Carbon Dioxide ; Respiratory Function Tests ; Oxygen ; Heart Rate

INTRODUCTION: MyDiagnostick is an atrial fibrillation (AF) screening tool that has been validated in the Caucasian population in the primary care setting. METHODS: In our study, we compared MyDiagnostick with manual pulse check for AF screening in the community setting. RESULTS: In our cohort of 671 candidates from a multi-ethnic Asian population, AF prevalence was found to be 1.78%. Of 12 candidates, 6 (50.0%) had a previous history of AF and another 6 (50.0%) were newly diagnosed with AF. Candidates found to have AF during the screening were older (72.0 ± 11.7 years vs. 56.0 ± 13.0 years, P < 0.0001) and had a higher CHADSVASC risk score (2.9 ± 1.5 vs. 1.5 ± 1.1, P = 0.0001). MyDiagnostick had a sensitivity of 100.0% and a specificity of 96.2%. In comparison, manual pulse check had a sensitivity of 83.3% and a specificity of 98.9%. CONCLUSION MyDiagnostick is a simple AF screening device that can be reliably used by non-specialist professionals in the community setting. Its sensitivity and specificity are comparable and validated across various studies performed in different population cohorts.
Humans ; Atrial Fibrillation/diagnosis* ; Heart Rate ; Sensitivity and Specificity ; Risk Factors ; Electrocardiography ; Mass Screening

Humans ; Heart Failure/therapy* ; Kidney/physiology* ; Renal Insufficiency ; Glomerular Filtration Rate

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

OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation. METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening. RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods. CONCLUSIONS The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.
Artificial Intelligence ; Neural Networks, Computer ; Heart Rate ; Electrocardiography ; Photoplethysmography/methods* ; Anesthesia