Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration

The spiking output of the mouse olfactory bulb encodes large-scale temporal features of natural odor environments

Spatiotemporal dynamics of natural odor environment have informative features for animals navigating to an odor source. Population activity in the olfactory bulb (OB) has been shown to follow plume dynamics to a moderate degree (Lewis et al., 2021), but it is unknown whether the ability to follow plume dynamics is driven by individual cells or whether it emerges at the population level. Previous research has explored the responses of individual OB cells to isolated features of plumes, but it is difficult to adequately sample these features as it is still undetermined which features navigating mice employ during olfactory guided search. Here we released odor from an upwind odor source and simultaneously recorded both odor concentration dynamics and cellular response dynamics in awake, head-fixed mice. We found that longer timescale features of odor concentration dynamics were encoded at both the cellular and population level. At the cellular level, plume onset was encoded across all trials and plume offset was encoded for high concentration odors, but not low concentration odors. Although cellular level tracking of plume dynamics was observed to be weak, we found that at the population level, OB activity distinguished whiffs and blanks (accurately detected odor presence versus absence) throughout the duration of a plume. Even ~20 OB cells were enough to accurately encode these features. Our findings indicate that the full range of odor concentration dynamics and high frequency fluctuations are not encoded by OB spiking activity. Instead, relatively lower-frequency dynamics of plumes, such as plume onset, plume offset, whiffs, and blanks, are represented in the OB.

High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators

Arrhythmia is an abnormal rhythm of the heart which leads to sudden death. Among these arrhythmias, some are shockable, and some are non-shockable arrhythmias with external defibrillation. The automated external defibrillator (AED) is used as the automated arrhythmia diagnosis system and requires an accurate and rapid decision to increase the survival rate. Therefore, a precise and quick decision by the AED has become essential in improving the survival rate. This paper presents an arrhythmia diagnosis system for the AED by engineering methods and generalized function theories. In the arrhythmia diagnosis system, the proposed wavelet transform with pseudo-differential like operators-based method effectively generates a distinguishable scalogram for the shockable and non-shockable arrhythmia in the abnormal class signals, which leads to the decision algorithm getting the best distinction. Then, a new quality parameter is introduced to get more details by quantizing the statistical features on the scalogram. Finally, design a simple AED shock and non-shock advice method by following this information to improve the precision and rapid decision. Here, an adequate topology (metric function) is adopted to the space of the scatter plot, where we can give different scales to select the best area of the scatter plot for the test sample. As a consequence, the proposed decision method gives the highest accuracy and rapid decision between shockable and non-shockable arrhythmias. The proposed arrhythmia diagnosis system increases the accuracy to 97.98%, with a gain of 11.75% compared to the conventional approach in the abnormal class signals. Therefore, the proposed method contributes an additional 11.75% possibility for increasing the survival rate. The proposed arrhythmia diagnosis system is general and could be applied to distinguish different arrhythmia-based applications. Also, each contribution could be used independently in various applications.

Rapid adjustments to autonomic control of cardiac rhythm at the onset of isometric exercise in healthy young adults

Sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) influences on cardiac rhythm at the onset of exercise, a time of rapid autonomic adjustments, are clinically important areas of investigation. Continuous wavelet transform (CWT) involves time-frequency-based heart rate variability (HRV) analysis allowing investigation of autonomic influences on cardiac rhythm during short durations of exercise. Therefore, the purpose of this study was to characterize SNS and PNS influences on cardiac rhythm at the onset of isometric exercise in healthy young adults. CWT analysis was retrospectively applied to R-R interval data (electrocardiogram) previously collected from 14 healthy young adults (26 ± 2 years) who performed 30-s, one-legged, isometric, calf exercise at 70% maximal voluntary contraction (MVC; 70% MVC trial) or rested (0% MVC trial). Absolute and normalized low-frequency (aLF, nLF; 0.04-0.15 Hz) and high-frequency (aHF, nHF; 0.15-0.4 Hz) bands and LF/HF were used to analyze one 30-s baseline period and six 5-s time windows during the 30-s exercise (70% MVC) or rest (0% MVC). Statistical analysis involved two-way analysis of variance with post-hoc analysis. aHF, aLF, LF/HF, nHF, and nLF displayed a trial-time interaction (all p ≤ 0.027). In the 70% compared to the 0% MVC trial, aHF and nHF were lower after 5-30 s (all p ≤ 0.040), aLF was lower after 20-30 s (all p ≤ 0.011) and LF/HF and nLF were higher after 5-20 s (all p ≤ 0.045). These results indicate the reduction of the PNS influence on cardiac rhythm begins sooner than the augmentation of the SNS influence at the onset of isometric exercise in healthy young adults.

Increased respiratory modulation of cardiovascular control reflects improved blood pressure regulation in pregnancy

Hypertensive pregnancy disorders put the maternal-fetal dyad at risk and are one of the leading causes of morbidity and mortality during pregnancy. Multiple efforts have been made to understand the physiological mechanisms behind changes in blood pressure. Still, to date, no study has focused on analyzing the dynamics of the interactions between the systems involved in blood pressure control. In this work, we aim to address this question by evaluating the phase coherence between different signals using wavelet phase coherence. Electrocardiogram, continuous blood pressure, electrocardiogram-derived respiration, and muscle sympathetic nerve activity signals were obtained from ten normotensive pregnant women, ten normotensive non-pregnant women, and ten pregnant women with preeclampsia during rest and cold pressor test. At rest, normotensive pregnant women showed higher phase coherence in the high-frequency band (0.15-0.4 Hz) between muscle sympathetic nerve activity and the RR interval, blood pressure, and respiration compared to non-pregnant normotensive women. Although normotensive pregnant women showed no phase coherence differences with respect to hypertensive pregnant women at rest, higher phase coherence between the same pairs of variables was found during the cold pressor test. These results suggest that, in addition to the increased sympathetic tone of normotensive pregnant women widely described in the existing literature, there is an increase in cardiac parasympathetic modulation and respiratory-driven modulation of muscle sympathetic nerve activity and blood pressure that could compensate sympathetic increase and make blood pressure control more efficient to maintain it in normal ranges. Moreover, blunted modulation could prevent its buffer effect and produce an increase in blood pressure levels, as observed in the hypertensive women in this study. This initial exploration of cardiorespiratory coupling in pregnancy opens the opportunity to follow up on more in-depth analyses and determine causal influences.

Correlation between Angiotensin Serum Levels and Very-Low-Frequency Spectral Power of Heart Rate Variability during Hemodialysis

Cardiovascular regulatory mechanisms that fail to compensate for ultrafiltration and cause hypovolemia during hemodialysis (HD) are not completely understood. This includes the interaction between the autonomic nervous system and the biochemistry that regulates blood pressure and modulates cardiac activity and vascular tone in response to hypovolemia in patients treated with HD. The objective was to evaluate the association of spectral indices of heart rate variability (HRV) with serum levels of angiotensin II, angiotensin 1–7, nitric oxide and total antioxidant capacity during HD. Electrocardiographic records were obtained from 20 patients during HD (3 h), from which HRV data and spectral power data in the very-low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands were generated. Three blood samples per patient were collected during HD (0.0, 1.5, 3.0 h) to determine the levels of biomarkers involved in the pressor response during HD. Angiotensin II had a positive correlation with VLF (r = 0.390) and with LF/HF (r = 0.359) and a negative correlation with LF (r = −0.262) and HF (r = −0.383). There were no significant correlations between HRV and the other biomarkers. These results suggest that during HD, VLF could reflect the serum levels of angiotensin II, which may be associated with the autonomic response to HD.

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter

The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. The use of α indices by spectral methods has been the most popular approach to BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) has been proposed to serve the same purpose. GAFD adopts a three-layer tree structure similar to wavelet decomposition but is only constructed by Gaussian windows in different cutoff frequency. Its computation is more efficient than that of conventional spectral methods, and there is no need to specify any parameter. This research presents a novel approach, referred to as modulated Gaussian filter (modGauss) for BRS estimation. It has a more simplified structure than GAFD using only two bandpass filters of dedicated passbands, so that the three-level structure in GAFD is avoided. This strategy makes modGauss more efficient than GAFD in computation, while the advantages of GAFD are preserved. Both GAFD and modGauss are conducted extensively in the time domain, yet can achieve similar results to conventional spectral methods. In computational simulations, the EuroBavar dataset was used to assess the performance of the novel algorithm. The BRS values were calculated by four other methods (three spectral approaches and GAFD) for performance comparison. From a comparison using the Wilcoxon rank sum test, it was found that there was no statistically significant dissimilarity; instead, very good agreement using the intraclass correlation coefficient (ICC) was observed. The modGauss algorithm was also found to be the fastest in computation time and suitable for the long-term estimation of BRS. The novel algorithm, as described in this report, can be applied in medical equipment for real-time estimation of BRS in clinical settings.

Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor

BACKGROUND

The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV).

METHODS

We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods.

RESULTS

No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008).

CONCLUSION

The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.

Stochastic filtering based transmissibility estimation of novel coronavirus

In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.