Stress level evaluation using BP Neural network based on time-frequency analysis of HRV

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver"s drowsiness and waking status in order to develop the classification model for a driver"s drowsiness and waking status using those features. Background: Driver"s drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver"s drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver"s drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver"s drowsiness prevention systems.

Heart rate variability (HRV) analysis has increasingly become a promising marker for the assessment of the autonomic nervous system. The easy derivation of the HRV has determined its popularity, being successfully used in many research and clinical studies. However, the conventional HRV analysis is performed on 5 min ECG recordings which in e-health monitoring might be unsuitable, due to real-time requirements. Thus, the aim of this study is to evaluate the association between the raw ECG heartbeats and the HRV features to further reduce the number of heart beats required for the HRV estimation enabling real time monitoring. We propose a deep learning based system, specifically a recurrent neural network for the inference of two time domain HRV features: AVNN (the average of all the NN intervals) and IHR (instantaneous heart rate). The obtained results suggest that both AVNN and IHR can be accurately inferred from a shorter ECG interval of about 1 min, with a mean error of <5% of the computed HRV features.

In the present study, we studied the impact of cigarette smoking on Heart Rate Variability(HRV). HRV is a non-invasive screening technique that measures the variation in consecutive heartbeats. HRV features were derived from the Electrocardiogram(ECG), and it is an indicator of the autonomous nervous system(ANS) status. The ECG was recorded before and 5 minutes after smoking, and HRV features were extracted. The healthy subjects HRV data was considered as a baseline for the study. Both real and synthetic HRV data were used to perform this study. The synthetic HRV data was generated using Synthetic Minority Oversampling Technique(SMOTE) Machine Learning(ML) technique. The data of healthy and smokers was analyzed using both statistical test and machine learning algorithms. Both approaches suggest an ANS imbalance in smokers that ultimately shows the modulation in HRV. The modulation in HRV indicates the high chances of cardiovascular risk in smokers.

The study of the autonomous nervous system (ANS) has played an important role, over the last years, in prognostic and diagnostic of cardiac diseases, as well as, in the assessment of psychological stress. The most common techniques to evalute the balance of the ANS are invasive and unable to provide a continuous monitoring of the patients. The advances in technology and the development of wearable sensors have provided new alternative methods to study the ANS. The analysis of Heart Rate Variability (HRV) and Electrodermal Activity (EDA) are nonivasive methods to assess the ANS with wearables devices. The wearable device used provides information about HRV with the acquisition of photoplethysmography signals from the wrist and EDA from the fingers. The processing of the biosignals was performed by submitting the participants to a mental arithmetic stress test. The results showed that the participants exhibited two distinct response during stress - “Flight or Fight”. These responses were classified using machine-learning techniques. The constructed models were able to predict how the subjects will respond in a situation of stress, based only on baseline features. The accuracy of the models using only HRV baseline features was of approximately 80% and the accuracy using simultaneously HRV and EDA baseline features was of 77%, when assigning the correct response during stress to the participant.

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

The autonomic nervous system dysfunction has been reported in up to 76% of stroke patients 7 days after an acute stroke. Heart rate variability (HRV) is one of the important indicators reflecting the balance of sympathetic and parasympathetic nerves. Therefore, we performed a systematic literature review of existing literature on the association between heart rate variability and the different types of stroke. We included studies published in the last 32 years (1990 to 2022). The electronic databases MEDLINE and PubMed were searched. We selected the research that met the inclusion or exclusion criteria. A narrative synthesis was performed. This review aimed to summarize evidence regarding the potential mechanism of heart rate variability among patients after stroke. In addition, the association of clinical characteristics of heart rate variability and stroke has been depicted. The review further discussed the relationship between post-stroke infection and heart rate variability, which could assist in curbing clinical infection in patients with stroke. HRVas a noninvasive clinical monitoring tool can quantitatively assess the changes in autonomic nervous system activity and further predict the outcome of stroke. HRV could play an important role in guiding the clinical practice for autonomic nervous system disorder after stroke.

Towards wearable thermal comfort assessment framework by analysis of heart rate variability

Heart Rate Variability in Idiopathic Dilated Cardiomyopathy: Characteristics and Prognostic Value

Objective To investigate the characteristics of heart rate variability (HRV) of ambulatory electrocardiogram in patients with acute stroke, and evaluate the predictive value of HRV in stroke prognosis. Methods Eighty-three patients acute stroke (study group) and 83 cases of healthy subjects (control group) from October 2016 to October 2017 in Dalian Municipal Central Hospital Affiliated of Dalian Medical University were selected. The 24 h ambulatory electrocardiogram was performed to determine HRV in 2 groups, including standard deviation of NN intervals (SDNN), standard deviation of the 5 min mean cycle lengths (SDANN), root-mean-square successive difference (RMSSD) and percentage value of NN50 count (PNN50). Results The SDNN, SDANN, RMSSD and PNN50 in study group were significantly lower than those in control group: (80.83 ± 10.52) ms vs. (148.11 ± 22.59) ms, (79.98 ± 8.89) ms vs. (129.35 ± 5.34) ms, (19.28 ± 4.25) ms vs. (39.57 ± 2.38) ms and (5.91 ± 2.12)% vs. (19.35 ± 12.15)%, and there were statistical differences (P 0.05). The SDNN, SDANN, RMSSD and PNN50 in right stroke (43 cases) were significantly lower than those in left stroke (40 cases):(75.18 ± 2.32) ms vs. (88.12 ± 3.58) ms, (73.36 ± 2.18) ms vs. (85.69 ± 7.29) ms, (17.57 ± 1.67) ms vs. (20.58 ± 4.23) ms and (4.39 ± 1.57)% vs. (8.61 ± 1.12)%, and there were statistical differences (P<0.05). Patients were followed up for 1 year, 24 died and 59 survived. The SDNN, SDANN, RMSSD and PNN50 in dead patients were significantly lower than those in survived patients: (92.35 ± 4.58) ms vs. (154.37 ± 4.65) ms, (76.23 ± 4.03) ms vs. (143.95 ± 4.34) ms, (7.43 ± 2.12) ms vs. (31.65 ± 1.52) ms and (2.35 ± 0.46)% vs. (11.65 ± 0.48)%, and there were statistical differences (P<0.05). Conclusions The autonomic nervous function of patients with acute stroke is seriously unbalanced, with increased sympathetic excitability and decreased vagus excitability. The decrease of HRV can easily induce cardiac events and seriously affect the prognosis. Key words: Stroke; Prognosis; Heart rate viability; Autonomic nervous function

Background: The hallmarks of fibromyalgia (FM), a chronic pain syndrome, include tiredness, cognitive impairment, and extensive musculoskeletal discomfort. An imbalance in the autonomic nerve system may play a major role in its pathogenesis, leading to changes in visceral and cardiovascular control, according to mounting evidence. Heart rate variability (HRV) is a sensitive, non-invasive measure of sympathovagal balance and autonomic regulation. Aims and Objectives: The purpose of this study was to compare HRV indices between FM patients and age- and sex-matched healthy controls in order to evaluate autonomic dysfunction in FM utilizing short-term HRV analysis. Materials and Methods: Thirty patients with FM diagnosed using the 2016 updated American College of Rheumatology’s criteria, and 30 healthy volunteers were included in this cross-sectional research. In accordance with Task Force standards, standardized 5-min rested electrocardiograms were obtained, and HRV characteristics were examined in the time domains and frequency domains. The data were analyzed using Statistical Package for the Social Sciences 26.0, with t-tests for between-group comparisons. P&lt;0.05 was considered statistically significant. Results: When compared to healthy controls, FM patients showed a significant decrease in HRV indicators. The RMSSD was 19.3±7.2 ms compared to 38.7 ± 10.1 ms (P&lt;0.001*), and the mean standard deviation of NN intervals (SDNN) was 22.6±8.1 ms rather than 41.2±9.4 ms (P&lt;0.001*). Frequency-domain measurements revealed a higher low-frequency/high-frequency (LF/HF) ratio (1.72±0.65 vs. 1.15±0.48, P=0.003*), indicating greater sympathetic dominance and decreased parasympathetic modulation, along with significantly lower LF power (315±120 ms2 vs. 520±160 ms2, P&lt;0.001*) and HF power (210±98 ms2 vs. 450±142 ms2, P&lt;0.001*). Conclusion: Significantly decreased HRV in both the temporal and frequency domains is a symptom of substantial autonomic dysfunction linked to FM. The concept of impaired central autonomic control in FM is supported by these data, which highlight a condition of sympathovagal imbalance. For determining the severity of an illness and tracking treatment plans meant to restore autonomic balance, HRV may be a helpful biomarker.

Within the framework of the research into pathological conditions and syndromes associated with undifferentiated connective tissue dysplasia (UCTD), an emphasis needs to be placed on the changes in heart rhythm. In puberty, the severity of neurovegetative disorders caused by the presence of UCTD might increase manifold. The features of heart rate variability (HRV) in pubertal children against the background of UCTD have been studied insufficiently and require more detailed analysis. This study aims to assess heart rate variability and tone of the autonomic nervous system (ANS) in puberty-aged children with UCTD. Two groups of children were examined, with methods including electrocardiography and cardiointervalography. The main group consisted of 36 puberty-aged children with UCTD, the control group consisted of 30 healthy children. ANS tone was assessed based on the HRV. In the control group of children aged 11, the value of variation range (VR) was 0.18±0.4, mode amplitude (MA) 35.2±2.4, stress index (IN) 72.8±4.3, which corresponds to the state of vegetative balance. As the puberty progresses, the state of eutonia is replaced by the activation of the sympathetic division of the ANS, which becomes most pronounced at the age of 13 years. In the control group of children aged 13, the value of VR was 0.08±0.02, MA 61.7±2.1, SI 210.6±8.6, which corresponds to moderate sympathicotonia. By the end of puberty, in healthy children, the severity of sympathetic activation begins to decrease, and at the age of 15 years, vegetative balance is again observed. However, this pattern is violated in children with UCTD. Upon entering puberty, moderate activation of the sympathetic division of the ANS is already observed in children with UCTD. At the age of 11 years in this group, the value of VR is 0.06±0.02, MA 72.4±3.1, SI 211.2±8.6. At the age of 13, pronounced sympathicotonia is observed, which is confirmed by the values of VR - 0.04±0.01, MA 80.2±4.3 and SI 34.3±14.7. By the end of puberty in children with UCTD, in contrast to the control group, the analyzed parameters indicate not an equilibrium of the ANS, but the predominance of parasympathetic activation. The research provides an insight into heart rate variability in puberty-aged children with UCTD. It was established that healthy children enter puberty in a state of equilibrium of the autonomic nervous system, which in the middle of puberty is replaced by moderate sympathicotonia, and then returns to its original values. Children with the presence of UCTD enter puberty with moderately pronounced sympathicotonia, which gradually increases to pronounced values and is replaced by moderate vagotonia. Funding Acknowledgement Type of funding sources: None.

Physiology of brain death is characterized by major disturbances of autonomic nervous system (ANS) activity which can lead to graft dysfunction. These findings exhibit the importance of early diagnosis of brain death to improve transplantation outcome. The aim of this prospective study was to assess whether heart rate variability (HRV) analysis, a noninvasive method to investigate ANS activity in comatose patients, could achieve this goal. A total of 14 brain-injured patients were included in the study as soon as they exhibited the clinical signs of imminent brain death. The electrocardiogram was then recorded from two leads with a Holter digital monitor. The clinical diagnosis of brain death was considered after an autonomic storm had occurred. HRV was assessed from 6 h before to 6 h after brain death in both time domain and spectral analysis, estimating either global ANS activity (index of variability, total power), parasympathetic activity (percentage of delta of R-R interval >50 ms, root mean square for successive interval differences, LnHF) or sympathetic activity (LnLF). Hourly averages of these variables were compared by using one-way analysis of variance. To assess whether HRV could per se diagnose brain death, receiver operating characteristic curves were generated for total power, root mean square for successive interval differences, and LnHF. We observed, for 6 h before brain death, a progressive extinction of the influence of the ANS on cardiovascular regulation. There was no activity in the two components of the ANS as soon as brain death occurred. HRV analysis appeared to be a very sensitive but a less specific method of diagnosing brain death. A total of 14 brain-injured patients with the clinical criteria of imminent brain death were enrolled for electrocardiogram recording and heart rate variability analysis (a noninvasive method to investigate autonomic nervous system activity). For 6 h before brain death, we observed a progressive extinction of autonomic nervous system activity which was not present as soon as brain death was clinically evoked.

Traumatic brain injury (TBI) represents one of the most common social problems worldwide. This neurologic pathology often affects several body systemic organs, including the cardiovascular autonomic system. Changes in cardiac autonomic nervous system (ANS) regulation through the heart rate variability (HRV) analysis have been observed in all degrees of TBI in patients. On the other hand, little research has been addressed in TBI models using rats as a potential target for better explore therapeutic approaches in this pathology. In this sense, we investigate the relationship before and after an induced moderate fluid percussion injury (FPI) model in rats on the HRV features. The findings found in our work showed clearly a decreasing the sympathetic (LF), parasympathetic (HF), and overall HRV (p < 0.01 for mean NN) activities post-TBI when compared to baseline animals. In concomitant to this, the heart rate increased, such as showed by decreasing the mean NN. Therefore, this study indicates that the impacted severity moderate TBI in rats impairs some cardiac autonomic function parameters. However, further research is needed to better understand this pathophysiology.KeywordsTraumatic brain injuryFluid percussion injuryAutonomic nervous systemHeart rate variabilityRat

Objective The characteristics of heart rate variability (HRV) in patients in the functional examination department of a Shanghai hospital from 2023 to 2024 were analyzed. Methods Ninety-five patients who received functional examination in our hospital from 2023 to 2024 were selected as the study objects, including 33 patients with depression, 32 patients with anxiety, and 30 patients with insomnia. Hrv-related indicators of patients with depression, anxiety and insomnia were compared respectively: The standard deviation (SDNN) of the difference of heartbeat cycles, the whole process is divided into consecutive periods by 5 minutes, the mean value of N-N interval every 5 minutes is calculated, then the standard deviation (SDNN5) of all the mean values are calculated, and the number of beats in all N-N interval periods where the difference between adjacent N-N interval periods is greater than 50ms is divided by the total number of NN interval periods. Finally, multiply by 100 (pNN50), the root-mean-square value (RMSSD) of the difference between all adjacent N-N intervals, extremely low frequency power (VLF), low frequency power (LF), high frequency power (HF), total power (TP), LF/HF. The HRV-related indexes of patients of different genders and ages were compared respectively: SDNN, SDNN5, pNN50, RMSSD, VLF, LF, HF, TP, LF/HF. Results There was no significant difference in the levels of SDNN, SDNN5, pNN50, RMSSD, VLF, LF, HF, TP and LF/HF in patients with depression, anxiety and insomnia (P &gt; 0.05). There was no significant difference in SDNN, SDNN5, pNN50, RMSSD, VLF, LF and TP between male and female patients (P &gt; 0.05). There were significant differences in HF and LF/HF levels between male and female patients (P &lt; 0.05). There were significant differences in the levels of SDNN, SDNN5, VLF, LF, TP and LF/HF among different age groups (P &lt; 0.05). There was no significant difference in pNN50, RMSSD and HF levels among different age groups (P &gt; 0.05). Conclusion HRV has a certain predictive power for patients with depression, anxiety and insomnia in the functional examination department of a Shanghai hospital from 2023 to 2024. In addition, the HF and LF/HF levels of HRV related indicators may be correlated with gender. At the same time, with the increase of age, the levels of SDNN, SDNN5, VLF, LF, TP, LF/HF showed a decreasing trend, and the autonomic nervous system function of the body was also declining.