Nonlinear Heart Rate Variability Parameters Research Articles

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study

ObjectiveDepression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression.MethodsThe data of 465 outpatients from the Affiliated Hospital of Southwest Medical University were selected for the study. The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. The four models were evaluated by the area under the receiver operating characteristic curve (ROC), calibration curve and the decision curve analysis (DCA). Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model.ResultsThere were 237 people in the depressed group and 228 in the non-depressed group. In the training set (n = 325) and test set (n = 140), the area under of the curve(AUC) values of the XGBoost model are 0.92 [95% confidence interval (CI) 0.888,0.95] and 0.82 (95% CI 0.754,0.892)] respectively, which are higher than the other three models. The XGBoost model has excellent predictive efficacy and clinical utility. The SHAP method was ranked according to the importance of the degree of influence on the model, with age, heart rate, Standard deviation of the NN intervals (SDNN), two nonlinear parameters of HRV and sex considered to be the top 6 predictors.ConclusionWe provided a feasibility study of HRV as a potential biomarker for depression. The proposed model based on HRV provides clinicians with a quantitative auxiliary diagnostic tool, which is assist to improving the accuracy and efficiency of depression diagnosis, and can also be utilized for the monitoring and prevention of depression.

Synthetic assessment of cardiac autonomic modulation and Baevsky stress index in patients with synucleinopathies

Abstract Background The Baevsky Stress Index (BSTRi), calculated from Heart Rate Variability (HRV) data, has been proposed for quantitative assessment of physiological stress, with higher scores indicating higher levels of physical and/or psychological stress [1]. Cardiac autonomic modulation can be synthetically quantified by calculating the parasympathetic (PNSi) and the sympathetic (SNSi) indexes. The values of the BSTRi, SNSi, and PNSi in patients with synucleinopathies of different prognoses have not been reported yet. Purpose To quantify, in patients with Parkinson's disease (PD) and Multisystem Atrophy (MSA), the BSTRi, its correlation with the PNSi and the SNSi indexes, and their potential value for a synthetic assessment of cardiac autonomic modulation and early differential diagnosis between PD and MSA. Methods Holter ECG data of 66 patients with Parkinsonian syndromes were retrospectively analyzed after 10 years of follow-up. Out of them, 34 patients with a certain diagnosis of PD (17) and MSA (17) were selected for this study. HRV was analyzed with the Kubios software (version 4.0), providing automatic calculation of the BSTRi, PNSi, and SNSi [2], as well as of time domain (TD), frequency domain (FD), nonlinear (NL), and time-varying (TV) HRV parameters. HRV was calculated from selected short-term intervals (of 5 minutes), during daily activity (ACT5’), and non-REM sleep (NREM5’), and from 24-hours Holter recordings. Patients’ HRV findings were compared with those of 51 age-matched healthy subjects (HS). Results An example of individual automatic graphic summary is shown in Figure 1. Both short-term and 24-hour BSTRi and SNSi values were significantly (p < 0.05) higher in PD and MSA compared to those of HS, in all investigated conditions. Short-term PNSi was significantly higher in HS than in PD in all conditions, but only during NREM5’ in MSA patients (Table 1). No significant difference was found between the indexes of PD and MSA patients. As in HS (R=92), BSTRi was positively correlated with the SNSi in MSA (average R= 0.83 and 0.89 during ACT5’ and NREM5’, respectively), but less evident in PD (R=0.67). The inverse correlation between PNSi and SNSi (R=0.75 in HS) was lost in both MSA and PD. Conclusions Higher values of BSTRi in PD and MSA compared to HS suggest that patients with synucleinopathies do experience higher levels of psychophysiological stress, which may affect their quality of life negatively. However, since no significant difference was found in this study between BSTRi values of PD and MSA patients, BSTRi seems at present unreliable for early differentiation between the two diseases. Telematic assessment of BSTRi, SNSi, and PNSi could be useful for remote monitoring of PD and MSA patients’ psychophysiological stress, to provide timely intervention and adaptive treatment.Fig.1 Example of graphic summary (ACT5')

Affective Implications of Human-Animal Relationship on Pig Welfare: Integrating Non-Linear Heart Rate Variability Measures.

The human-animal relationship is crucial for animal welfare. Gentle handling enhances pigs' comfort while rough handling causes fear and stress. This study examined how different human-animal relationship qualities affect the behavior and heart rate variability (linear and non-linear parameters) of 36 nursery pigs. Over six weeks, pigs experienced positive (n = 12), minimal (n = 12), or negative (n = 12) human handling. Their responses to handlers were then assessed in an experimental arena with four phases: habituation, exposure to the handler standing and sitting, and forced interaction. Pigs subjected to negative handling exhibited increased fear-related behaviors, spending less time in contact with the handler. They also exhibited heightened stress responses, with greater LF/HF ratio and Lmean values compared with positively handled pigs. Conversely, gently handled pigs displayed affiliative behaviors, accepting more strokes, and higher parasympathetic activation, indicated by greater RMSSD/SDNN and SampEn values, suggesting a more positive affective state. Minimally handled pigs exhibited some behavioral similarities to gently handled pigs, although physiological data indicated that the interaction was likely more rewarding for the gently handled pigs. These results emphasize the impact of human-animal relationships on pig welfare and highlight the value of incorporating non-linear heart rate variability parameters in such evaluations.

Increased stress burden and electrodermal reactivity in bladder cancer patients in comparison to healthy controls

The purpose of our study was to assess specific physiological parameters associated with stress responses in bladder cancer (BCa) patients compared to healthy individuals. By examining the transition from a supine to a sitting position, representing a mild physiological load, we investigated the changes in autonomic nervous system (ANS) activity as reflected by alterations in these parameters, indicating shifts in ANS regulation, using non-linear heart rate variability (HRV) parameters (0V%, 2UV%, parasympathetic and sympathetic nervous system – PNS and SNS indices), modified heart rate acceleration (ACmod) and deceleration capacities (DCmod), heart rate (HR), electrodermal activity (EDA), and also their correlations with perceived stress score. Our findings showed that BCa patients (n = 38) exhibited elevated resting HR, heightened SNS index, and increased EDA compared to their healthy counterparts (n = 47), indicating a notable physiological stress burden. The 0V% parameter showed a positive association with the SNS index, ACmod, HR, and EDA parameters, while displaying a negative correlation with the PNS index, DCmod and 2UV%. These non-linear HRV parameters, such as 0V% and 2UV%, offer nuanced insights into the complexities of heartbeat dynamics and autonomic regulation. After the transition from supine to sitting positions, BCa patients displayed higher EDA responses, indicating heightened stress reactivity and ANS sensitivity. These physiological distinctions persisted even when we did not prove differences in the levels of perceived stress between the studied groups. In conclusion, our study emphasizes the significance of identifying cancer patients at risk of ANS dysregulation, paving the way for tailored stress management strategies.

Heart rate variability for neuro-prognostication after CA: Insight from the Parisian registry

BackgroundHypoxic ischemic brain injury (HIBI) induced by cardiac arrest (CA) seems to predominate in cortical areas and to a lesser extent in the brainstem. These regions play key roles in modulating the activity of the autonomic nervous system (ANS), that can be assessed through analyses of heart rate variability (HRV). The objective was to evaluate the prognostic value of various HRV parameters to predict neurological outcome after CA. MethodsRetrospective monocentric study assessing the prognostic value of HRV markers and their association with HIBI severity. Patients admitted for CA who underwent EEG for persistent coma after CA were included. HRV markers were computed from 5 min signal of the ECG lead of the EEG recording. HRV indices were calculated in the time-, frequency-, and non-linear domains. Frequency-domain analyses differentiated very low frequency (VLF 0.003–0.04 Hz), low frequency (LF 0.04–0.15 Hz), high frequency (HF 0.15–0.4 Hz), and LF/HF ratio. HRV indices were compared to other prognostic markers: pupillary light reflex, EEG, N20 on somatosensory evoked potentials (SSEP) and biomarkers (neuron specific enolase-NSE). Neurological outcome at 3 months was defined as unfavorable in case of best CPC 3–4-5. ResultsBetween 2007 and 2021, 199 patients were included. Patients were predominantly male (64%), with a median age of 60 [48.9–71.7] years. 76% were out-of-hospital CA, and 30% had an initial shockable rhythm. Neurological outcome was unfavorable in 73%. Compared to poor outcome, patients with a good outcome had higher VLF (0.21 vs 0.09 ms2/Hz, p < 0.01), LF (0.07 vs 0.04 ms2/Hz, p = 0.003), and higher LF/HF ratio (2.01 vs 1.01, p = 0.008). Several non-linear domain indices were also higher in the good outcome group, such as SD2 (15.1 vs 10.2, p = 0.016) and DFA α1 (1.03 vs 0.78, p = 0.002). These indices also differed depending on the severity of EEG pattern and abolition of pupillary light reflex. These time–frequency and non-linear domains HRV parameters were predictive of poor neurological outcome, with high specificity despite a low sensitivity. ConclusionIn comatose patients after CA, some HRV markers appear to be associated with unfavorable outcome, EEG severity and PLR abolition, although the sensitivity of these HRV markers remains limited.

Best Parameters of Heart Rate Variability for Assessing Autonomic Responses to Brief Rectal Distention in Patients with Irritable Bowel Syndrome.

Heart rate variability (HRV) has been used to measure autonomic nervous system (ANS) activity noninvasively. The purpose of this study was to identify the most suitable HRV parameters for ANS activity in response to brief rectal distension (RD) in patients with Irritable Bowel Syndrome (IBS). IBS patients participated in a five-session study. During each visit, an ECG was recorded for 15 min for baseline values and during rectal distension. For rectal distension, a balloon was inflated in the rectum and the pressure was increased in steps of 5 mmHg for 30 s; each distension was followed by a 30 s rest period when the balloon was fully deflated (0 mmHg) until either the maximum tolerance of each patient was reached or up to 60 mmHg. The time-domain, frequency-domain and nonlinear HRV parameters were calculated to assess the ANS activity. The values of each HRV parameter were compared between baseline and RD for each of the five visits as well as for all five visits combined. The sensitivity and robustness/reproducibility of each HRV parameter were also assessed. The parameters included the Sympathetic Index (SI); Root Mean Square of Successive Differences (RMSSD); High-Frequency Power (HF); Low-Frequency Power (LF); Normalized HF Power (HFn); Normalized LF Power (LFn); LF/HF; Respiratory Sinus Arrhythmia (RSA); the Poincare Plot's SD1, SD2 and their ratio; and the pNN50, SDSD, SDNN and SDNN Index. Data from 17 patients were analyzed and compared between baseline and FD and among five sessions. The SI was found to be the most sensitive and robust HRV parameter in detecting the ANS response to RD. Out of nine parasympathetic parameters, only the SDNN and SDNN Index were sensitive enough to detect the parasympathetic modulation to RD during the first visit. The frequency-domain parameters did not show any change in response to RD. It was also observed that the repetitive RD in IBS patients resulted in a decreased autonomic response due to habituation because the amount of change in the HRV parameters was the highest during the first visit but diminished during subsequent visits. In conclusion, the SI and SDNN/SDNN Index are most sensitive at assessing the autonomic response to rectal distention. The autonomic response to rectal distention diminishes in repetitive sessions, demonstrating the necessity of randomization for repetitive tests.

Identification and Classification of Physical Fatigue in Construction Workers Using Linear and Nonlinear Heart Rate Variability Measurements

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, 33.2±6.9 years) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to construction tasks. This study demonstrates that HRV values changed significantly in response to physical work, indicating a change in the relative activity of cardiac autonomic functions as a result of fatigue. Using the ways in which HRV parameters vary in response to increased workloads provides a sensitive marker for contrasting construction workers with and without cardiovascular disease. It also allows the site manager to track how quickly workers fatigue, so that they can switch up their workload to reduce the likelihood that any one worker would get severely exhausted, or to suggest that workers who are already severely fatigued take a break to prevent further injury.

HEART RATE VARIABILITY PARAMETERS WERE NOT ASSOCIATED WITH 30-DAY ALL-CAUSE MORTALITY IN INTENSIVE CARE UNIT PATIENTS WITH OR WITHOUT ATRIAL FIBRILLATION: A RETROSPECTIVE STUDY OF THE MIMIC-IV DATABASE.

Objective: Our study aims to evaluate the association between heart rate variability (HRV) and short- and long-term prognosis in patients admitted to intensive care unit (ICU). Methods and Results: Adult patients continuously monitored for over 24 h in ICUs from the the American Medical Information Mart for Intensive Care (MIMIC)-IV Waveform Database were recruited in our study. Twenty HRV-related variables (8 time domain, 6 frequency domain, and 6 nonlinear variables) were calculated based on RR intervals. The association between HRV and all-cause mortality was assessed. Ninety-three patients met the inclusion criteria and were classified into atrial fibrillation (AF) and sinus rhythm (SR) groups, which were further divided into 30-day survivor group and nonsurvivor\groups based on their survival status. The 30-day all-cause mortality rates in AF and SR groups were 36.3% and 14.6%, respectively. All the time domain, frequency domain, and nonlinear HRV parameters did not differ significantly between survivors and nonsurvivors with or without AF (all P > 0.05). Presence of renal failure, malignancy, and elevated blood urea nitrogen level were associated with increased 30-day all-cause mortality in SR patients, while presence of sepsis, infection, higher platelet count, and magnesium level were associated with increased 30-day all-cause mortality in AF patients. Conclusions: Heart rate variability variables were not associated with increased 30-day all-cause mortality in ICU patients with or without AF.

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

Effect of physical exercise on non-linear parameters of heart rate variability in Chronic Obstructive Pulmonary Disease

Background: Chronic Obstructive Pulmonary Disease (COPD) is associated with autonomic nerve dysfunction. Nonlinear methods of heart rate variability (HRV) analysis have been gaining interest as a method of detecting changes in cardiac autonomic nerve function. Regular practice of physical exercise may cause improvement of this impaired autonomic nerve function. Objectives: To assess the effect of physical exercise on nonlinear measures of HRV analysis in male patients with COPD. Methods: This prospective interventional study was carried out in the Department of Physiology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Shahbag, Dhaka in 2018 on 60 diagnosed male COPD patients aged 40-75 years with disease duration of at least 5 years selected from the Department of Respiratory Medicine, BSMMU, Dhaka. Thirty patients underwent physical exercise (walking for 30 minute and pursed lip and diaphragmatic breathing 10 minuts)for 3 months in addition to standard treatment. Thirty (30) patients received only standard treatment for 3 months. For comparison, 30 age matched apparently healthy males were also enrolled. HRV data of all patients were recorded before and after 3 months of follow up. HRV data of healthy controls were recorded once at baseline. For assessing autonomic dysfunction, nonlinear parameters of HRV were recorded by Power Lab 8/35. For statistical analysis, paired sample t-test and independent sample t-test were done as applicable. Results: SD1, SD2 and SD1/ SD2 were found significantly lower in COPD patients compared to healthy controls at baseline. After 3 months of physical exercise, significant increment of these parameters occurred with a trend of improvement in cardiac autonomic nerve function in these patients. Conclusion: This study concluded that physical exercise can improve cardiac autonomic nerve dysfunction function in COPD patients. J Bangladesh Soc Physiol. 2022, June; 17(1): 6-13

Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events.

Heart rate complexity, derived from nonlinear heart rate variability (HRV), has been shown to help predict the outcomes of various diseases. Changes in heart rate complexity before and after paroxysmal atrial fibrillation (PAF) events are unclear. To evaluate changes in heart rate complexity through nonlinear HRV before and after PAF events. We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording, and analyzed nonlinear HRV variables including Poincaré plot analysis, sample entropy (SampEn), and multiscale entropy (MSE). HRV analyses were applied to a 20-minute window before the onset and after the termination of PAF events. HRV parameters were evaluated and compared based on eight different 5-minute time segments, as we divided each 20-minute window into four segments of 5 minutes each. SampEn and MSE1~5 significantly decreased before the onset of PAF events, whereas SampEn, MSE1~5 and MSE6~20 significantly increased after the termination of PAF events. SD1 and SD2, which are nonlinear HRV parameters calculated via Poincaré plot analysis, did not significantly change before the PAF events, however they both decreased significantly after termination. Heart rate complexity significantly decreased before the initiation and increased after the termination of PAF events, which indicates the crucial role of nonlinear heart rate dynamics in the initiation and termination of PAF.

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process.Graphical abstract

The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI).

Background: Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations between HRV and adverse outcomes in a contemporary cohort of patients presenting with STEMI undergoing primary percutaneous coronary intervention (PCI). Methods: We will perform a prospective, observational cohort study in a single healthcare center. Adult patients aged ≥18 years presenting with STEMI in sinus rhythm will be enrolled for primary PCI within 12 h from symptoms onset. Time domain, frequency domain, and nonlinear HRV parameters will be measured using a medically approved wrist-wearable device for 5 min segments during myocardial revascularization by primary PCI. Additional HRV measurements will be performed one and six months from the index event. The primary composite outcome will include all-cause mortality and major adverse cardiovascular events (during the hospital stay, one month, and one year following admission). Several secondary outcomes will be analyzed: individual components of the primary composite outcome, target lesion revascularization, hospitalizations for heart failure, ventricular arrhythmias, left ventricular ejection fraction, and left ventricular diastolic function. Conclusions: Our study will enlighten the reliability and usefulness of HRV evaluation as a prognostic marker in contemporary patients with STEMI. The potential validation of HRV as a risk marker for adverse outcomes following STEMI will ensure a background for including HRV parameters in future risk scores and guidelines.

Altered cardiac autonomic function after recovery from COVID-19.

BackgroundAutonomic dysfunction may occur during the acute phase of COVID‐19. Heart rate variability (HRV) is a useful tool for the assessment of cardiac sympathetic and parasympathetic balance. We aimed to evaluate cardiac autonomic function by using HRV in subjects after recovery from COVID‐19 who had previously symptomatic and were followed outpatiently.MethodsThe study group composed of 50 subjects with a confirmed history of COVID‐19 and the control group composed of 50 healthy subjects without a history of COVID‐19 and vaccination. All the study participants underwent 2‐dimensional, pulsed‐ and tissue‐Doppler echocardiographic examinations and 24‐hour Holter monitoring for HRV analysis.ResultsTime domain parameters of SDNN, SDANN, SDNNi, RMSSD, pNN50, and HRV triangular index were all decreased in the study group when compared with the control group. Frequency domain parameters of TP, VLF, LF, HF, and HFnu were also decreased in the study group in comparison with the control group. LFnu was similar between groups. Nonlinear parameters of HRV including α1 and α2 decreased in the study group. By contrast, Lmax, Lmean, DET, REC, and Shannon entropy increased in the study population. Approximate and sample entropies also enhanced in the study group.ConclusionsThe present study showed that all three domain HRV significantly altered in patients after recovery from COVID‐19 indicating some degree of dysfunction in cardiac autonomic nervous system. HRV may be a useful tool for the detection of preclinical autonomic dysfunction in this group of patients.

Heart rate variability analysis to improve early diagnostic and prognostic differentiation of parkinsonism of uncertain etiology. A 10 years follow-up study

Abstract Introduction Parkinsonian syndromes of different nature feature autonomic nervous system dysfunction (ANSd) which may herald or follow motor symptoms (MS) in Parkinson disease (PD), but anticipate by years and progress more rapidly in multiple system atrophy (MSA). Abnormal Heart Rate Variability (HRV) has been reported in both PD and MSA. Purpose This study aimed to evaluating if HRV analysis (HRVa) could contribute to differentiate PD from MSA and to guide early and more appropriate treatment and prognostic assessment. Methods 66 consecutive parkinsonian patients (pts) (48 males, 18 females) (mean age 63.8±10 y) were enrolled between 2010 and 2020. Initial clinical diagnosis was MSA (19), PD (20), PD with autonomic impairment (PD-AI) (10), Lewy body disease (2), supranuclear palsy (3), undefined Parkinsonism (UP) (12). The severity of MS was quantified with UPDRS and Hoehn/Yahr scales. HRV was analyzed with linear (TD and FD) and non-linear (NL) methods (Kubios Premium 3.4.1), during daily activity and NREM sleep (2-minutes intervals) and during REM sleep (1-minute Interval). 44/66 underwent also ASN functional evaluation with the Ewing protocol. Data of 52 age-matched healthy subjects (HS) were used for comparison. For statistical analysis, SPSS software (version 21) was used. Discriminant Analysis (DA) was applied to identify the more relevant parameters differentiating PD from MSA and classifying “undefined” cases. Prolonged follow-up (f-up) provided clinical diagnostic certainty, as goldstandard for validation of HRVa classification accuracy. Results UPDRS and Hoehn/Yahr scores were higher in MSA than in PD (p&amp;lt;0.0001). Along 10-years of f-up, 16/66 pts died (56,2% MSA), 5/66 had new diagnostic definition, and 15/66 were lost to f-up. Most of HRV parameters of 34 pts with certain diagnosis (17 MSA and 17 PD) whose f-up was completed, were significantly different from those of HS. VLFpower and recurrence plot parameters calculated during active wakefulness were different (p&amp;lt;0.05) between MSA and PD, and correctly classified them with 88,2% predictive accuracy (PA) at DA, according to the formula: F1 = (vlfpw%ar* − 0,087) + (rprec* − 0,319) + (rpshen*10.81) + (−20,53) Comparative classification accuracy of HRVa, Ewing score, UPDRS and Hoehn/Yahr scales is shown in Table 1. Attempting prognostic risk assessment by applying F1 to HRV data of pts with PD-AI and of pts with UP, those classified as MSA according to F1 showed the worst clinical outcome during the f-up. Conclusions This study confirms that a higher degree of prevailing sympathetic ASNd in MSA compared to PD can be identified by a combination of Linear and NL HRV parameters, and correctly differentiate with high (88,2%) predictive accuracy all pts with certain diagnosis verified during prolonged f-up. Moreover, HRVa might be useful for earlier prognostic stratification of pts with parkinsonian symptoms of uncertain nature and to improve clinical and pharmacological management. Funding Acknowledgement Type of funding sources: None.

Autonomic Function in Parkinson's Disease Subjects Across Repeated Short-Term Dry Immersion: Evidence From Linear and Non-linear HRV Parameters.

Several studies have shown that “dry” immersion appears as a promising method of rehabilitation for Parkinson's disease. Still, little is known about the cardiovascular reaction in “dry” immersion (DI), especially in Parkinson's disease (PD). Therefore, this study was aimed to evaluate the effect of repeated 45-min DI sessions on autonomic function in subjects with PD. The study group consisted of 20 subjects with PD [13 men, seven women, aged 51–66 years old, Hoehn & Yahr (H&Y) staged 1–3] were enrolled in the study according to inclusion and non-inclusion criteria. The DI program was comprised of seven 45-min DI sessions, applied within 25–30 days. Blood pressure (BP), heart rate (HR), and electrocardiogram (ECG) in the standard lead II were recorded at 1st, 4th, and 7th DI, before, on the 15, 30, and 40th min of DI session. Autonomic function was assessed with analysis of heart rate variability (HRV) using Kubios Standard version 2 software. Linear (time- and frequency-domain) and non-linear (correlation dimension, entropies, DFA1 and DFA2, percent of determinism, and recurrence) were computed. At baseline condition, time- and frequency-domain HRV parameters showed low variability of HR, which indicates reduced autonomic neurogenic control of HR. Throughout the DI session, systolic and diastolic BP has decreased by 5–7 mm Hg (p < 0.001), and time- and frequency-domain parameters of HRV have significantly increased, what can be regarded as compensatory mechanisms of hemodynamics during DI. The structure of the regulatory input to the heart seen by HRV was characterized by low complexity and reduced autonomic neurogenic control of HR. Across the program of DI sessions, the hypotensive effect was documented, but no notable modification of the HRV-parameters was found. The absence of long-term modification of the studied parameters can be attributed both to deconditioning environmental effect of DI and limited adaptation of the organism due to neurodegeneration in PD. That should be taken into consideration when planning rehabilitation measures in subjects of older age and chronic somatic diseases with modeled microgravity.

Effect of Laboratory Mental Stressors on Cardiovascular Reactivity in Young Women During Different Phases of Menstrual Cycle: An Observational Study.

Introduction: Excessive cardiovascular reactivity to mental stress may be a risk factor for cardiovascular disease. However, there is inconsistent report in the literature regarding change in cardiac autonomic tone with the phase of the menstrual cycle and how it is affected by mental stress. Therefore, the present study was aimed at determining the cardiovascular reactivity to different laboratory mental stressors during follicular and luteal phase of menstrual cycle using heart rate variability (HRV).Methods: Thirty-three regularly cycling young females (19–35 years of age) were exposed to four cognitive tasks (Stroop test, Mental Rotation test, n-back test, and Mental Arithmetic Stress Test [MAST]) employed as laboratory mental stressors. HRV of the study participants were recorded before, during, and after each cognitive task and the recording was done in both phases of menstrual cycle for each individual.Results: A significant difference was observed in time domain parameters and nonlinear parameters of HRV in pretest versus during-test condition and during-test versus post-test conditions, but not in frequency domain parameters. No phase difference was found in time domain or frequency domain analysis of HRV in baseline or during performance of task. MAST performance (score out of 50) was significantly higher in luteal than follicular phase, while other tests showed no such difference.Conclusion: All four mental stress tasks used in the present study were able to elicit significant decrease in parasympathetic tone during performance of task as compared with baseline values of HRV. The present study did not elicit any phase difference in cardiovascular reactivity.

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

Association of Short-Term Heart Rate Variability With Breast Tumor Stage.

Cardiac autonomic modulation, assessed by heart rate variability (HRV), is associated with tumor pathogenesis and development as well as invasion and metastasis. This study aimed to examine this association in breast cancer (BC) patients. A total of 133 patients (average age 49.2years) with BC or benign breast tumors were divided into three groups: benign group, early-stage group, and advanced-stage group. About 5-min resting ECG was collected for the analysis of linear and nonlinear HRV parameters. Multiple logistic regression models were performed to test the independent contribution of HRV to breast tumor stage. The advanced-stage group had significantly reduced HRV compared to the benign and early-stage groups. In particular, for each 1-SD increase in SD2, SD of normal-to-normal intervals, very-low frequency, total power, and low frequency, the odds of having advanced staging decreased by 69.3, 64.3, 58.3, 53.3, and 65.9%, respectively. These associations were independent of age, body mass index, mean heart rate (HR), and respiratory rate (RR). These findings suggest an association between HRV and breast tumor stage, and HRV parameters may help construct an effective early diagnostic and clinical prognostic model.

Effects of Heat Stress on Heart Rate Variability in Free-Moving Sheep and Goats Assessed With Correction for Physical Activity.

Heart rate variability (HRV) is the heart beat-to-beat variation under control of the cardiovascular function of animals. Under stressed conditions, cardiac activity is generally regulated with an upregulated sympathetic tone and withdrawal of vagal tone; thus, HRV monitoring can be a non-invasive technique to assess stress level in animals especially related to animal welfare. Among several stress-induced factors, heat stress is one of the most serious causes of physiological damage to animals. The aim of this study was to assess the effects of heat stress on HRV in small ruminants under free-moving conditions. In three experimental periods (June, August, and October), inter-beat intervals in sheep and goats (three for each) in two consecutive days were measured. HRV parameters were calculated from the inter-beat interval data by three types of analyses: time domain, frequency domain, and non-linear analyses. The temperature–humidity index (THI) was used as an indicator of heat stress, and vectorial dynamic body acceleration (VeDBA) was calculated to quantify the physical activity of the animals tested. First, we investigated correlations of THI and VeDBA with HRV parameters; subsequently, THI was divided into five categories according to the values obtained (≤ 65, 65–70, 70–75, 75–80, and >80), and the effects of the THI categories on HRV parameters were investigated with and without correcting for the effects of physical activity based on the VeDBA. The results indicated that HRV significantly decreased with increasing THI and VeDBA. For non-linear HRV parameters that were corrected for the effects of physical activity, it was suggested that there would be a threshold of THI around 80 that strongly affected HRV; high heat stress can affect the autonomic balance of animals non-linearly by inducing the sympathetic nervous system. In conclusion, to assess psychophysiological conditions of unrestrained animals by HRV analysis, the confounding effect of physical activity on HRV should be minimized for a more precise interpretation of the results.