Cross-spectral Coherence Research Articles

Unveiling the impact of oil price shocks on global sukuk markets: a focus on quantile coherence and time-frequency connectedness

ABSTRACT This study examines the relationship between oil price shocks and Islamic bonds in 15 global markets. We focus on how they are connected in terms of time and frequency, specifically in quantiles. Through a quantile coherency approach, we discover that oil price shocks affect sukuk indices differently depending on the type of shock. When we increase the frequency of analysis from once a year to once a month, we observe a significant increase in coherence values of oil risk shocks across the majority of sukuk indices of oil exporting nations in both the lower and middle quantiles. Furthermore, based on quantile cross-spectral coherence analysis, we notice that the extreme quantile dependence between oil price shocks and the majority of the oil exporting nations’ sukuk indices remains stable over a short-time horizon. Our research indicates that demand shocks have a significant impact on the correlation between sukuk markets during uncertain periods, such as COVID-19 and the Russian-Ukraine war. Our findings provide a credible understanding of the behaviour of sukuk indices in response to fluctuations in oil prices, which is important for investment portfolio managers and policy decision-makers.

Dependence of green energy markets on big data and other fourth industrial revolution technologies

This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

Tail connectedness of DeFi and CeFi with accessible banking pillars: Unveiling novel insights through wavelet and quantile cross-spectral coherence analyses

This study evaluates the interplay between decentralized finance (DeFi) and centralized finance (CeFi) within the realm of inclusive banking by examining their foundational components, namely, alternative finance industry, distributed ledger platforms, and future payment technologies. The alternative finance industry leverages innovative technologies, such as direct lending, crowdfunding, automated wealth management, on-demand insurance, and digital currencies. This feature aligns well with the DeFi principles, and it is also beneficial to CeFi. Distributed ledger platforms offer transparency and security for both DeFi and CeFi, whereas future payment technologies provide user-friendly solutions. This study applies novel wavelet and quantile cross-spectral coherence analyses to obtain nuanced relationship insights. This methodology categorizes crypto finance into the DeFi and broader cryptocurrency ecosystems to facilitate robust comparison with traditional banking. It further delineates democratized banking into alternative financing mechanisms, distributed ledger technologies, and future payment capabilities. Wavelet coherence analysis reveals time–frequency associations, whereas quantile cross-spectral coherence unveils extreme connections under different market conditions. The findings suggest the existence of dynamic relationships between inclusive banking components and various financial infrastructures. These relationships vary across return distributions and time scales. In regular markets, positive associations are seen in yearly and monthly frequency cycles with alternative finance and centralized services exhibiting the strongest linkage. During periods of extremely high returns, CeFi and centralized-decentralized (hybrid) infrastructures exhibit limited interconnectivity with democratized banking. Moreover, DeFi's association with distributed ledger technology is negligible. In bear markets, the strongest positive associations are observed between inclusive access and conventional, hybrid, and decentralized financial systems. These insights provide new perspectives on the dynamics between financial systems during extreme conditions. This study emphasizes the need for adaptive regulation, international cooperation, and a balance between innovation and stability to protect and empower users. Collaboration between academia and policymakers is essential to navigate this evolving landscape and advance democratized banking.

Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities

This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology (FinTech), the Internet of Things, and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities; (2) Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoin’s good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors.

Which liquidity indicator is more informative to market volatility? Spectrum analysis of China’s base metal futures market

By applying the frequency domain causality test and cross-spectral coherence analysis, this study investigates the informational effects of liquidity indicators on the volatility of China’s base metal futures market from January 2016 to June 2022. We find that trading volume is more informative in economically explaining the price variations of most base metal futures for all frequency horizons, while open interest is more informative to Granger-cause economic innovations in the market volatility of the lead and nickel futures markets both in the short and long run. The coherence levels between the underlying frequency components for open interest are found to be greater than those for trading volume, suggesting that price volatility in China’s base metal futures market would be more responsive to shocks in open interest.

Cross-spectral coherence and co-movement between WTI oil price and exchange rate of Thai Baht

This paper investigates the cross-spectral coherence and co-movement between the monthly return series of WTI oil price and exchange rate of Thai Baht against USD from 1986 to 2019. We use a quantile cross spectral (coherency) approach and time frequency wavelets as estimation techniques. Results from the quantile coherency analysis reveal a negative spillover effects of oil price and Thai exchange rates in the short, medium, and long run which connotes that oil market poses systemic risk to the foreign exchange market in Thailand in short, medium and long term. From the wavelets analysis, we first note that there is no co-movements at high frequency (i.e. short term). However, additional results show positive and negative co-movements between oil price and Thailand-US exchange rate in different periods and different frequencies with the effect been stronger during extreme volatility periods. Policy implications are derived at the end of the article.

Evaluation of Signal Quality from a Wearable Phonocardiogram (PCG) Device and Personalized Calibration

Currently, the only clinically utilized Phonocardiogram (PCG) is an electronic stethoscope used in a hospital or clinical environment. The availability of continuously recorded PCGs can provide a new avenue of research into chronic disease management at home. Researchers have proposed such wearable PCG devices. However, limitations exist in evaluating such devices as PCG recording devices in home-like environments. Here, we evaluate a wearable PCG system in a belt-type form factor with an embedded force sensor, accelerometer, and a single lead ECG to study the feasibility of acquiring diagnostic-grade PCGs while the wearer performs daily activities. We describe qualitative and quantitative exploratory analysis methods for cross-subject comparison of PCG signal quality, wearer comfort, and the impact of activities using Signal-to-Noise (SNR) comparisons and cross-spectral coherence between activity and PCG. The analysis of the data suggests that a common user-chosen method of donning a wearable PCG is not applicable across subjects for obtaining optimal PCG recording quality. We propose a method to calibrate wearable PCG devices using an embedded force sensor and by following a protocol involving feedback from the embedded force sensor to determine the optimal method of wearing the device. Following a similar path to precision medicine using genomic data and the extrapolation of risk, wearable devices with healthcare applications should be developed with the ability to be adapted and calibrated to each individual. In the immediate future this may involve calibration procedures such as those followed in this work, using controlled measurements performed with each patient to tune a device for them.

Tail risk dependence, co-movement and predictability between green bond and green stocks

ABSTRACT This paper examines the coherence of extreme returns between green bonds and a unique set of green stocks. We use the novel quantile cross-spectral coherence methodology of quantile spectral coherency model, cross-quantilogram correlation approach, windowed time-lagged cross-correlation, and windowed scalogram difference models as estimation techniques. The study period spans from 28 November 2008 to 23 September 2020. Our measure of green stocks comprises the constituents of the MSCI Global Environment Price Index: Alternative Energy, Green Building, Pollution Prevention or Clean Technology while our green bond market is proxied by S&P Green Bond Index. We find the dependency between Green Bonds and green stocks to be weak, and this is high during market downturn periods in the short- to medium-term dynamics. This suggests that Green Bonds do act as a hedge, diversifier, or safe-haven instrument for environment portfolio in the short-term, medium-term and long-term dynamics during bearish market conditions. We conclude that green bonds and green stocks are two distinct asset classes with a distinct risk-return profile despite their common climate-friendly nature.

Cross-spectral coherence and dynamic connectedness among contactless digital payments and digital communities, enterprise collaboration, and virtual reality firms

This paper examines the dynamic connectedness and risk transmissions among firms in the Future Payments and Future Communication businesses. In doing so, the authors employ the daily data of key variables of interest from May 15, 2017 to April 16, 2021. The study employs two empirical methodologies namely, the quantiles coherency and the time-varying parameter-vector autoregressive (TVP-VAR) model. While the former helps to determine the interdependence between Future Payments and the components of the Future Communication firms at different market states and investment horizons, the latter suitably addresses the time-varying connectedness among the series. The results from the quantiles coherence approach reveal a heterogeneous pattern of interdependency between Future Payments and each of the Future Communication firms, as it varies across different market states and investment horizons. The net connectedness findings further mention that Future Payments are net transmitters of shocks to other firms in the system, indicating that the firms play a leading role in the general contactless digital technology industry. These findings have theoretical, managerial, and policy implications, especially in terms of the improved adoption of these new technologies to the firms' financial infrastructures and the maintenance of an optimal investment portfolio.

Epidermal Electrodes with Ferrimagnetic/Conductive Properties for Biopotential Recordings.

Interfacing ultrathin functional films for epidermal applications with external recording instruments or readout electronics still represents one of the biggest challenges in the field of tattoo electronics. With the aim of providing a convenient solution to this ever-present limitation, in this work we propose an innovative free-standing electrode made of a composite thin film based on the combination of the conductive polymer PEDOT:PSS and ferrimagnetic powder. The proposed epidermal electrode can be directly transferred onto the skin and is structured in two parts, namely a conformal conductive part with a thickness of 3 μm and a ferrimagnetic-conductive part that can be conveniently connected using magnetic connections. The films were characterized for ECG recordings, revealing a performance comparable to that of commercial pre-gelled electrodes in terms of cross-spectral coherence, signal-to-noise ratio, and baseline wandering. These new, conductive, magnetically interfaceable, and free-standing conformal films introduce a novel concept in the domain of tattoo electronics and can set the basis for the development of a future family of epidermal devices and electrodes.

Carbon market and the conventional and Islamic equity markets: Where lays the environmental cleanliness of their utilities, energy, and ESG sectoral stocks?

This paper compares the dependency of the conventional and the Shariah-compliant stock markets on the carbon market in order to have a pronounced judgment about the cleaner nature of their environmentally-related sectors. As such, we employ the frequency-domain causality test and the time-frequency quantile cross-spectral coherence methods to uncover the dynamic dependency structure in the joint distribution quantiles, which traditional methods are unable to reveal. We establish that carbon allowance returns uni-directionally Granger-cause all the sectoral stock returns (except the conventional utilities stock returns) at the lower quantiles, which imply long-term horizons. Furthermore, all the conventional sectoral stock returns are observed to be more negatively connected to carbon allowance returns than their Shariah-compliant variants across different market conditions. However, the highest and the least negative dependencies are respectively associated with the conventional environment, social and governance (ESG), and energy stock returns. On the other hand, the positive dependency results are heterogeneous across the two broad sectoral classifications, market states, and investment horizons. Overall, we establish that both the conventional and Shariah-compliant sectoral stock markets are gradually getting aligned with the goal of a cleaner environment, although it is stronger for the former. More so, the utilities and ESG sectors are more positively sensitive to the green agenda than the energy sector. The implications of the findings are helpful for clean-energy policymakers, carbon-pricing authorities, and energy market investors.

Does Movement Amplitude of a Co-performer Affect Individual Performance in Musical Synchronization?

Interpersonal coordination in musical ensembles often involves multisensory cues, with visual information about body movements supplementing co-performers’ sounds. Previous research on the influence of movement amplitude of a visual stimulus on basic sensorimotor synchronization has shown mixed results. Uninstructed visuomotor synchronization seems to be influenced by amplitude of a visual stimulus, but instructed visuomotor synchronization is not. While music performance presents a special case of visually mediated coordination, involving both uninstructed (spontaneously coordinating ancillary body movements with co-performers) and instructed (producing sound on a beat) forms of synchronization, the underlying mechanisms might also support rhythmic interpersonal coordination in the general population. We asked whether visual cue amplitude would affect nonmusicians’ synchronization of sound and head movements in a musical drumming task designed to be accessible regardless of musical experience. Given the mixed prior results, we considered two competing hypotheses. H1: higher amplitude visual cues will improve synchronization. H2: different amplitude visual cues will have no effect on synchronization. Participants observed a human-derived motion capture avatar with three levels of movement amplitude, or a still image of the avatar, while drumming along to the beat of tempo-changing music. The moving avatars were always timed to match the music. We measured temporal asynchrony (drumming relative to the music), predictive timing, ancillary movement fluctuation, and cross-spectral coherence of ancillary movements between the participant and avatar. The competing hypotheses were tested using conditional equivalence testing. This method involves using a statistical equivalence test in the event that standard hypothesis tests show no differences. Our results showed no statistical differences across visual cues types. Therefore, we conclude that there is not a strong effect of visual stimulus amplitude on instructed synchronization.

A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device

The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient's home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.

Estimation of Sea Surface Current from X-Band Marine Radar Images by Cross-Spectrum Analysis

The cross-spectral correlation approach has been used to estimate the wave spectrum from optical and radar images. This work aims to improve the cross-spectral approach to derive current velocity from the X-band marine radar image sequence, and evaluate the application conditions of the method. To reduce the dependency of gray levels on range and azimuth, radar images are preprocessed by the contrast-limited adaptive histogram equalization. Two-dimensional cross-spectral coherence and phase are derived from neighboring X-band marine radar images, and the phases with large coherences are used to estimate the phase velocity and angular frequency of waves, which are first fitted with the theoretical dispersion relation by different least square models, and then the current velocity can be determined. Compared with the current velocities measured by a current meter, the root-mean-square error, correlation coefficient, bias, and relative error are 0.15 m/s. 0.88, –0.05 m/s, and 7.79% for the north-south velocity, and 0.14 m/s, 0.86, 0.06 m/s, and 10.75% for the east-west velocity in the experimental area, respectively. The preprocessing, critical coherence, and the number of images for applying the cross-spectral approach, are discussed.

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations.

Cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03Hz (∼30s). This suggests that frequency bands for characterizing cerebral autoregulation should be redefined Low cross-spectral coherence below 0.03Hz highlights the limitations of transfer function approaches Haemodynamic changes induced by lower body pressure could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible. There is currently little empirical basis for time scales that are considered to be most significant in cerebrovascular counter-regulation of changes in arterial pressure. Although it is well established that cerebral autoregulation behaves as a 'high-pass' filter, recommended frequency bands have been largely arbitrarily determined. To test effectiveness of cerebral autoregulation, we refined oscillatory lower body pressure (LBP) to augment resting pressure fluctuations below 0.1Hz by a factor of two in 13 young male volunteers, and thoroughly characterized the time and frequency responses of cerebral autoregulation. We observed that despite a threefold increase in arterial pressure power <0.03Hz with oscillatory LBP, there was no change in cerebral blood flow power, indicating near perfect counter-regulation. By contrast, in the range 0.03-0.10Hz, both cerebral blood flow and arterial pressure power more than doubled. Our data demonstrate that cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03Hz (∼30s). This suggests that frequency bands of interest should be redefined and recording length should be increased considerably to account for this. Furthermore, low cross-spectral coherence below 0.03Hz, even when pressure fluctuations were augmented, highlights the uncertainty in transfer function approaches and the need to either report precision or use non-linear approaches. Finally, haemodynamic changes induced by LBP could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible.

Spectral coherence and hyperbolic solutions applied to time difference of arrival localisation

Time difference of arrival (TDOA) source localisation is based on intersecting hyperboloids from spatially diverse sensor pairs. TDOA is commonly applied to avian source localisation with field studies finding that sources located inside array boundaries are localised with higher accuracy than sources outside the array. We examine the constitutive hyperbolic equations to demonstrate that at locations interior to an array, hyperbolas intersect at large angles translating into higher localisation accuracy with insensitivity to noise and timing errors, and then use the equations to assess expected accuracy in response to noise and timing errors. We also introduce the use of cross-spectral coherence as a requisite for computation of time delays by cross-correlation in order to minimize the likelihood of interference from noise and reduce the need for manual preprocessing of spectrograms or correlation functions. A small-aperture array is evaluated with a field test finding results consistent with analytical solutions suggesting that a lack of sensor spatial diversity coupled with signal timing accuracy is the primary source of localisation error, but that bearing discrimination remains accurate even for a small-aperture and sources external to the array.

A Generalized Coherence Framework for Detecting and Characterizing Nonlinear Interactions in the Nervous System.

This paper introduces a generalized coherence framework for detecting and characterizing nonlinear interactions in the nervous system, namely cross-spectral coherence (CSC). CSC can detect different types of nonlinear interactions including harmonic and intermodulation coupling as present in static nonlinearities and also subharmonic coupling, which only occurs with dynamic nonlinearities. We verified the performance of CSC in model simulations with both static and dynamic nonlinear systems. We applied CSC to investigate nonlinear stimulus-response interactions in the human proprioceptive system. A periodic movement perturbation was imposed to the wrist when the subjects performed an isotonic wrist flexion. CSC analysis was performed between the perturbation and brain responses (electroencephalogram, EEG). Both the simulation and the application demonstrated that CSC successfully detected different types of nonlinear interactions. High-order nonlinearities were revealed in the proprioceptive system, shown in harmonic and intermodulation coupling between the perturbation and EEG for all subjects. Subharmonic coupling was found in some subjects but not all. This paper provides a general tool to detect and characterize nonlinear nature and dynamics of the nervous system. The application of CSC on the experimental dataset indicates a complex nonlinear dynamics in the proprioceptive system. This novel framework 1) unveils the nonlinear neural dynamics in a more complete way than the existing coherence measures, and 2) is more suitable for estimating the input-output relation regarding both phase and amplitude compared to phase synchrony measures (which only consider phase coupling). Subharmonic coupling is reported in human proprioceptive system for the first time.

Coupling between arterial pressure, cerebral blood velocity, and cerebral tissue oxygenation with spontaneous and forced oscillations

We tested the hypothesis that transmission of arterial pressure to brain tissue oxygenation is low under conditions of arterial pressure instability. Two experimental models of hemodynamic instability were used in healthy human volunteers; (1) oscillatory lower body negative pressure (OLBNP) (N = 8; 5 male, 3 female), and; (2) maximal LBNP to presyncope (N = 21; 13 male, 8 female). Mean arterial pressure (MAP), middle cerebral artery velocity (MCAv), and cerebral tissue oxygen saturation (ScO2) were measured non-invasively. For the OLBNP protocol, between 0 and −60 mmHg negative pressure was applied for 20 cycles at 0.05 Hz, then 20 cycles at 0.1 Hz. For the maximal LBNP protocol, progressive 5 min stages of chamber decompression were applied until the onset of presyncope. Spectral power of MAP, mean MCAv, and ScO2 were calculated within the VLF (0.04–0.07 Hz), and LF (0.07–0.2 Hz) ranges, and cross-spectral coherence was calculated for MAP-mean MCAv, MAP-ScO2, and mean MCAv-ScO2 at baseline, during each OLBNP protocol, and at the level prior to pre-syncope during maximal LBNP (sub-max). The key findings are (1) both 0.1 Hz OLBNP and sub-max LBNP elicited increases in LF power for MAP, mean MCAv, and ScO2 (p ≤ 0.08); (2) 0.05 Hz OLBNP increased VLF power in MAP and ScO2 only (p ≤ 0.06); (3) coherence between MAP-mean MCAv was consistently higher (≥0.71) compared with MAP-ScO2, and mean MCAv-ScO2 (≤0.43) during both OLBNP protocols, and sub-max LBNP (p ≤ 0.04). These data indicate high linearity between pressure and cerebral blood flow variations, but reduced linearity between cerebral tissue oxygenation and both arterial pressure and cerebral blood flow. Measuring arterial pressure variability may not always provide adequate information about the downstream effects on cerebral tissue oxygenation, the key end-point of interest for neuronal viability.

The cerebovasculature: a smooth (muscle) operator?

The cerebovasculature: a smooth (muscle) operator?

Chronic fluoxetine reduces autonomic control of cardiac rhythms in rats with congestive heart failure

Up to 40% of patients with heart failure develop depression, and depression is an independent risk factor for cardiovascular mortality in this patient population. Consequently, increasing numbers of patients with heart failure are treated with antidepressants. Selective serotonin reuptake inhibitors are typically the antidepressant of choice since this drug class has limited cardiovascular toxicity. However, little is known about the effects of selective serotonin reuptake inhibitors on autonomic cardiac regulation in congestive heart failure (CHF). Here, indexes of cardiac autonomic control were evaluated before and during chronic fluoxetine (FLX) treatment (20 mg·kg(-1)·day(-1), 5 wk) in rats that developed CHF after coronary artery ligation. FLX reduced the low-frequency (LF) component of heart rate variability (HRV; P < 0.01) as well as the sympathetic contribution to LF HRV (P < 0.01) in both CHF and sham-operated rats. Both FLX and CHF reduced high-frequency HRV (P < 0.01). Spontaneous baroreflex gain was decreased in CHF rats 8 wk after ligation (P < 0.01). Cross-spectral coherence between the interbeat interval and mean arterial pressure was reduced in the LF domain 3 wk after ligation in CHF rats (P < 0.01) and was further reduced after chronic FLX treatment (P < 0.01). Plasma catecholamines and LF blood pressure variability were not affected by FLX. Chronotropic responses to both efferent vagal nerve stimulation and isoproterenol administration were reduced in CHF rats and by FLX (P < 0.01), whereas inotropic responses to isoproterenol were reduced only in CHF rats (P < 0.01). These data indicate that chronic FLX reduces the responsiveness to autonomic output controlling cardiac rhythm and may further compromise autonomic regulation of cardiac function in CHF.