The reliability of short-term measurement of heart rate variability during spontaneous breathing in people with chronic obstructive pulmonary disease

FABP3, FABP4, and heart rate variability among patients with chronic schizophrenia

Introduction

The prevalence of cardiovascular disease (CVD) and CVD-related deaths in patients with schizophrenia is high. An elevated risk of CVD has been associated with low heart rate variability (HRV). There is increasing evidence that fatty acid-binding protein (FABP)3 and FABP4 play roles in the development and progression of CVD. This study aimed to explore the association of circulating FABP3/FABP4 levels with HRV in patients with chronic schizophrenia.

Methods

We included 265 consecutive patients with chronic schizophrenia who attended a disease management program. We used an enzyme-linked immunosorbent assay for the measurement of plasma concentrations of FABP3 and FABP4. Standard HRV was recorded at baseline following a standard protocol. Mean high- and low-frequency (HF/LF) HRV values were analyzed by tertile of FABP3 and FABP4 using one-way analysis of variance, and linear regression analysis was performed to assess trends.

Results

A positive association between FABP3 and creatinine was found in multiple regression analysis. In addition, negative associations between levels of hematocrit, hemoglobin, HF HRV, and estimated glomerular filtration rate (eGFR) with FABP3 were also found. Moreover, positive associations between FABP4 with body mass index, diabetes mellitus, hypertension, systolic blood pressure, low-density lipoprotein-cholesterol, triglycerides, creatinine, and FABP3 were found. Furthermore, negative associations between levels of high-density lipoprotein-cholesterol, eGFR, and HF HRV with FABP4 were found. We also found a significant inverse association between FABP3 and HF HRV (p for trend = 0.008), and significant inverse associations between FABP4 with HF and LF HRV (p for trend = 0.007 and 0.017, respectively).

Discussion

Together, this suggests that elevated levels of FABP3 and FABP4 may be linked to health problems related to CVD in patients with chronic schizophrenia.

A systematic review and meta-analysis of heart rate variability in COPD

Background

Chronic obstructive pulmonary disease (COPD) is associated with disruption in autonomic nervous control of the heart rhythm. We present here quantitative evidence of the reduction in HRV measures as well as the challenges to clinical application of HRV in COPD clinics.

Method

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we search in June 2022 Medline and Embase databases for studies reporting HRV in COPD patients using relevant medical subject headings (MeSH) terms. The quality of included studies was assessed using the modified version of the Newcastle-Ottawa Scale (NOS). Descriptive data were extracted, while standardized mean difference was computed for changes in HRV due to COPD. Leave-one-out sensitivity test was performed to assess exaggerated effect size and funnel plots to assess publication bias.

Results

The databases search yielded 512 studies, of which we included 27 that met the inclusion criteria. The majority of the studies (73%) had a low risk of bias and included a total of 839 COPD patients. Although there were high between-studies heterogeneity, HRV time and frequency domains were significantly reduced in COPD patients compared with controls. Sensitivity test showed no exaggerated effect sizes and the funnel plot showed general low publication bias.

Conclusion

COPD is associated with autonomic nervous dysfunction as measured by HRV. Both sympathetic and parasympathetic cardiac modulation were decreased, but there is still a predominance of sympathetic activity. There is high variability in the HRV measurement methodology, which affects clinical applicability.

Intra- and Interrater Reliability of Short-Term Measurement of Heart Rate Variability on Rest in Individuals Post-COVID-19

Individuals affected by COVID-19 have an alteration in autonomic balance, associated with impaired cardiac parasympathetic modulation and, consequently, a decrease in heart rate variability (HRV). This study examines the inter- and intrarater reliability of HRV) parameters derived from short-term recordings in individuals post-COVID. Sixty-nine participants of both genders post-COVID were included. The RR interval, the time elapsed between two successive R-waves of the QRS signal on the electrocardiogram (RRi), were recorded during a 10 min period in a supine position using a portable heart rate monitor (Polar^® V800 model). The data were transferred into Kubios^® HRV standard analysis software and analyzed within the stable sessions containing 256 sequential RRi. The intraclass correlation coefficient (ICC) ranged from 0.920 to 1.000 according to the intrarater analysis by Researcher 01 and 0.959 to 0.999 according to the intrarater by Researcher 02. The interrater ICC ranged from 0.912 to 0.998. The coefficient of variation was up to 9.23 for Researcher 01 intrarater analysis, 6.96 for Researcher 02 intrarater analysis and 8.83 for interrater analysis. The measurement of HRV in post-COVID-19 individuals is reliable and presents a small amount of error inherent to the method, supporting its use in the clinical environment and in scientific research.

Immediate Effects of Rib Mobilization and Diaphragm Release Techniques on Cardiac Autonomic Control in Patients With Chronic Obstructive Pulmonary Disease: A Pilot Study

Objective

The purpose of this pilot study was to evaluate the feasibility of testing an intervention protocol and measuring the immediate effects of a rib mobilization technique (RMT) and a diaphragm release technique (DRT) on the autonomic nervous system of patients with chronic obstructive pulmonary disease (COPD).

Methods

This was a pilot study of a randomized controlled trial. Fourteen individuals were evaluated. Eligibility criteria were being a sedentary person with a diagnosis of COPD, age between 50 and 72 years, and being clinically stable. Exclusion criteria were heart disease, other respiratory comorbidities, and body mass index above 30kg/m². Participants first underwent cardiorespiratory evaluation and were then allocated into 2 groups: the RMT + DRT group and the DRT group. Clinical assessments were performed immediately before and after the intervention. Statistical analysis was carried out through a paired-sample Wilcoxon test, and the comparison between groups was performed using the Mann-Whitney test.

Results

All randomized participants completed the assessment and intervention protocol. Sample size was estimated at 24 individuals per group. The DRT group decreased resting heart rate by 5 bpm (P = .03) and increased variance (P = .04) and mean R-R interval (P = .03). The RMT + DRT group decreased mean R-R interval (P = .02).

Conclusion

The design for this study appears to be feasible for evaluating manual-therapy intervention in the nonmusculoskeletal function of patients with COPD. It was possible to determine the sample size for future studies. Preliminary data show that the diaphragm release technique may reduce mean resting heart rate and increase heart-rate variability immediately after the intervention.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.