Sample, Fuzzy and Distribution Entropies of Heart Rate Variability: What Do They Tell Us on Cardiovascular Complexity?

Heart Rate Variability and Cognition: A Narrative Systematic Review of Longitudinal Studies

BACKGROUND

Heart rate variability (HRV) is a reliable and convenient method to assess autonomic function. Cross-sectional studies have established a link between HRV and cognition. Longitudinal studies are an emerging area of research with important clinical implications in terms of the predictive value of HRV for future cognition and in terms of the potential causal relationship between HRV and cognition. However, they have not yet been the objective of a systematic review. Therefore, the aim of this systematic review was to investigate the association between HRV and cognition in longitudinal studies.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Embase, PsycINFO and PubMed databases were searched from the earliest available date to 26 June 2023. Studies were included if they involved adult human subjects and evaluated the longitudinal association between HRV and cognition. The risk of bias was assessed with the Newcastle-Ottawa Scale for Cohort Studies. The results were presented narratively.

RESULTS

Of 14,359 records screened, 12 studies were included in this systematic review, with a total of 24,390 participants. Two thirds of the studies were published from 2020 onwards. All studies found a longitudinal relationship between HRV and cognition. There was a consistent association between higher parasympathetic nervous system (PNS) activity and better cognition, and some association between higher sympathetic nervous system activity and worse cognition. Also, higher PNS activity persistently predicted better executive functioning, while data on episodic memory and language were more scant and/or controversial.

CONCLUSIONS

Our results support the role of HRV as a biomarker of future cognition and, potentially, as a therapeutic target to improve cognition. They will need confirmation by further, more comprehensive studies also including unequivocal non-HRV sympathetic measures and meta-analyses.

Poincaré plot can help predict the curative effect of metoprolol for pediatric postural orthostatic tachycardia syndrome

Purpose

To study whether a Poincaré plot can help predict the curative effect of metoprolol for postural orthostatic tachycardia syndrome (POTS) in children.

Methods

Pediatric patients with POTS who were administered metoprolol were retrospectively included. The collected data included general data (sex, age, height, weight, and body mass index), the manifestations and treatment (baseline orthostatic intolerance symptom score and course of metoprolol treatment), vital signs (supine heart rate [HR], supine blood pressure, and increased HR during the standing test), HR variability indexes (standard deviation of normal-to-normal intervals [SDNN]; standard deviation of the averages of normal-to-normal intervals [SDANN]; mean standard deviation of the NN intervals for each 5-min segment [SDNNI]; root mean square of the successive differences [rMSSD]; percentage of adjacent NN intervals that differ by >50 ms [pNN50]; triangular index; ultra-low [ULF], very low [VLF], low [LF], and high frequency [HF]; total power [TP]; and LF/HF ratio), and graphical parameters of the Poincaré plot (longitudinal axis [L], transverse axis [T], and L/T). Receiver operator characteristic curves were used to calculate the predictive function of the indexes with significant differences between patients who responded and those who did not. The index combination with the highest predictive value was obtained through series-parallel analysis.

Results

Overall, 40 responders and 23 non-responders were included. The L and T in the Poincaré plots and rMSSD, pNN50, HF, and TP of the HR variability data were significantly lower in participants who responded to metoprolol than in participants who did not (p < 0.001). The L/T of participants who responded to metoprolol was greater than that of non-responders (p < 0.001). Moreover, we noted a strong correlation between every two indexes among L, T, rMSSD, pNN50, HF, TP, and L/T (p < 0.05). T < 573.9 ms combined with L/T > 2.9 had the best performance for predicting the effectiveness of metoprolol, with a sensitivity of 85.0%, specificity of 82.6%, and accuracy of 84.1%.

Conclusion

In the Poincaré plot, a T < 573.9 ms combined with an L/T > 2.9 helps predict good outcomes of using metoprolol to treat pediatric POTS.

Machine learning analysis of primary hyperhidrosis for classification of hyperhidrosis type and prediction of compensatory hyperhidrosis

Background

Although sympathectomy is highly effective for improving symptom, compensatory hyperhidrosis (CH) is a major issue. In this study, characteristics of primary hyperhidrosis were investigated in terms of the heart rate variability (HRV) parameters. Classification of hyperhidrosis type and prediction of CH after sympathicotomy were also determined using machine learning analysis.

Methods

From March 2017 to December 2021, 128 subjects who underwent HRV tests before sympathicotomy were analyzed. T2 and T3 bilateral endoscopic sympathicotomy were routinely performed in patients with craniofacial and palmar hyperhidrosis, respectively. Data collected age, sex, body mass index (BMI), hyperhidrosis type, symptom improvement after sympathicotomy, the degrees of CH after sympathicotomy, and preoperative HRV findings. The independent risk factors associated with the degree of CH after sympathicotomy were investigated. Machine learning analysis was used to determine classification of hyperhidrosis type and prediction of the degree of CH.

Results

Preoperatively, patients with palmar hyperhidrosis presented with significantly larger standard deviation of normal-to-normal (SDNN), root mean square of successive differences (RMSSD), total power (TP), and low frequency (LF) than patients with craniofacial hyperhidrosis after controlling for age and sex (P=0.030, P=0.004, P=0.041, and P=0.022, respectively). More sympathetic nervous predominance was found in craniofacial type (P=0.019). Low degree of CH had significantly greater RMSSD (P=0.047), and high degree of CH showed more sympathetic nervous predominance (P=0.006). Multivariate analysis showed the type and expansion of sympathicotomy were significant factors for CH (P=0.001 and P=0.028, respectively). The neural network (NN) algorithm outperformed and showed a 0.961 accuracy, 0.961 F1 score, 0.961 precision, 0.961 recall, and 0.972 area under the curve (AUC) for classification of hyperhidrosis type. The random forest (RF) model outperformed showed a 0.852 accuracy, 0.853 F1 score, 0.856 precision, 0.852 recall, and 0.914 AUC for prediction of the degree of CH.

Conclusions

The present study showed the machine learning algorithm can classify types and predict CH after sympathicotomy for primary hyperhidrosis with considerable accuracy. Further large-scale studies are needed to validate the findings and provide management guidelines for primary hyperhidrosis.

Entropy-Based Multifractal Testing of Heart Rate Variability during Cognitive-Autonomic Interplay

Entropy-based and fractal-based metrics derived from heart rate variability (HRV) have enriched the way cardiovascular dynamics can be described in terms of complexity. The most commonly used multifractal testing, a method using q moments to explore a range of fractal scaling in small-sized and large-sized fluctuations, is based on detrended fluctuation analysis, which examines the power-law relationship of standard deviation with the timescale in the measured signal. A more direct testing of a multifractal structure exists based on the Shannon entropy of bin (signal subparts) proportion. This work aims to reanalyze HRV during cognitive tasks to obtain new markers of HRV complexity provided by entropy-based multifractal spectra using the method proposed by Chhabra and Jensen in 1989. Inter-beat interval durations (RR) time series were obtained in 28 students comparatively in baseline (viewing a video) and during three cognitive tasks: Stroop color and word task, stop-signal, and go/no-go. The new HRV estimators were extracted from the f/α singularity spectrum of the RR magnitude increment series, established from q-weighted stable (log-log linear) power laws, namely: (i) the whole spectrum width (MF) calculated as αmax - αmin; the specific width representing large-sized fluctuations (MFlarge) calculated as α0 - αq+; and small-sized fluctuations (MFsmall) calculated as αq- - α0. As the main results, cardiovascular dynamics during Stroop had a specific MF signature while MFlarge was rather specific to go/no-go. The way these new HRV markers could represent different aspects of a complete picture of the cognitive-autonomic interplay is discussed, based on previously used entropy- and fractal-based markers, and the introduction of distribution entropy (DistEn), as a marker recently associated specifically with complexity in the cardiovascular control.

On the Analysis of Regularized Fuzzy Systems of Uncertain Differential Equations

This article analyzes a regularized set of fuzzy differential equations with respect to an uncertain parameter. We provide sufficient conditions for the correctness of a new regularization scheme. For the resulting family of regularized fuzzy differential equations, the following properties are analyzed, and efficient criteria are proposed: successive approximations, continuity, global existence of solutions, existence of approximate solutions, existence of solutions in the autonomous case. In addition, we develop stability criteria for the regularized family of fuzzy differential equations on the basis of the comparison technique and the method of nonlinear integral inequalities. We expect that the derived results will inspire future research work in this direction.