Coherence analysis between respiration and heart rate variability using continuous wavelet transform

Keissar, Kobi; Davrath, Linda R.; Akselrod, Solange

doi:10.1098/rsta.2008.0273

Cited by 53 publications

(36 citation statements)

References 32 publications

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“…Wavelet transform phase shift (WTP), the phase difference between ABP and ICP, in the frequency of 0.0067 Hz to 0.05 Hz was calculated through complex wavelet transform, described in S1 Appendix [19,22–24]. Morlet mother wave with the central frequency at 1 Hz was applied either stretched or compressed to match various components of ABP/ICP signals (See S1 Appendix).…”

Section: Methodsmentioning

confidence: 99%

Cerebrovascular pressure reactivity monitoring using wavelet analysis in traumatic brain injury patients: A retrospective study

et al. 2017

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BackgroundAfter traumatic brain injury (TBI), the ability of cerebral vessels to appropriately react to changes in arterial blood pressure (pressure reactivity) is impaired, leaving patients vulnerable to cerebral hypo- or hyperperfusion. Although, the traditional pressure reactivity index (PRx) has demonstrated that impaired pressure reactivity is associated with poor patient outcome, PRx is sometimes erratic and may not be reliable in various clinical circumstances. Here, we introduce a more robust transform-based wavelet pressure reactivity index (wPRx) and compare its performance with the widely used traditional PRx across 3 areas: its stability and reliability in time, its ability to give an optimal cerebral perfusion pressure (CPPopt) recommendation, and its relationship with patient outcome.Methods and findingsFive hundred and fifteen patients with TBI admitted in Addenbrooke’s Hospital, United Kingdom (March 23rd, 2003 through December 9th, 2014), with continuous monitoring of arterial blood pressure (ABP) and intracranial pressure (ICP), were retrospectively analyzed to calculate the traditional PRx and a novel wavelet transform-based wPRx. wPRx was calculated by taking the cosine of the wavelet transform phase-shift between ABP and ICP. A time trend of CPPopt was calculated using an automated curve-fitting method that determined the cerebral perfusion pressure (CPP) at which the pressure reactivity (PRx or wPRx) was most efficient (CPPopt_PRx and CPPopt_wPRx, respectively).There was a significantly positive relationship between PRx and wPRx (r = 0.73), and wavelet wPRx was more reliable in time (ratio of between-hour variance to total variance, wPRx 0.957 ± 0.0032 versus PRx and 0.949 ± 0.047 for PRx, p = 0.002). The 2-hour interval standard deviation of wPRx (0.19 ± 0.07) was smaller than that of PRx (0.30 ± 0.13, p < 0.001). wPRx performed better in distinguishing between mortality and survival (the area under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx, p = 0.003). The mean difference between the patients’ CPP and their CPPopt was related to outcome for both calculation methods. There was a good relationship between the 2 CPPopts (r = 0.814, p < 0.001). CPPopt_wPRx was more stable than CPPopt_PRx (within patient standard deviation 7.05 ± 3.78 versus 8.45 ± 2.90; p < 0.001).Key limitations include that this study is a retrospective analysis and only compared wPRx with PRx in the cohort of patients with TBI. Prior prospective validation is required to better assess clinical utility of this approach.ConclusionswPRx offers several advantages to the traditional PRx: it is more stable in time, it yields a more consistent CPPopt recommendation, and, importantly, it has a stronger relationship with patient outcome. The clinical utility of wPRx should be explored in prospective studies of critically injured neurological patients.

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Section: Methodsmentioning

confidence: 99%

Cerebrovascular pressure reactivity monitoring using wavelet analysis in traumatic brain injury patients: A retrospective study

et al. 2017

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“…Nevertheless, the listening to music provokes changes also in other signals related to the ANS [6, 7 11, 12, 27, 32], and further studies should be therefore considered to achieve a comprehensive characterization of music-induced effects on the ANS. Additionally, the statistical analysis performed here may also be applied in bivariate TF analysis [19,30] to assess whether different conditions provoke different dynamic interactions in cardiovascular or cardiorespiratory control, or to explore the relationship between physiological rhythm and musical profile as in [7]. The experimental results revealed the transient nature of music-related patterns and suggest the need for further studies on the dynamic relationship between musical and autonomic features to improve the potential use of music in therapeutic applications.…”

Section: Physiological Parameter Changes During Music Stimulimentioning

confidence: 97%

A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis

Orini

Bailón

Enk

et al. 2010

Med Biol Eng Comput

103

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Interest in therapeutic applications of music has recently increased, as well as the effort to understand the relationship between music features and physiological patterns. In this study, we present a methodology for characterizing music-induced effects on the dynamics of the heart rate modulation. It consists of three steps: (i) the smoothed pseudo Wigner-Ville distribution is performed to obtain a time-frequency representation of HRV; (ii) a parametric decomposition is used to robustly estimate the time-course of spectral parameters; and (iii) statistical population analysis is used to continuously assess whether different acoustic stimuli provoke different dynamic responses. Seventy-five healthy subjects were repetitively exposed to pleasant music, sequences of Shepard tones with the same tempo as the pleasant music and unpleasant sounds overlaid with the same sequences of Shepard tones. Results show that the modification of HRV parameters are characterized by an early fast transient phase (15-20 s), followed by an almost stationary period. All kinds of stimuli provoked significant changes compared to the resting condition, while during listening to pleasant music the heart and respiratory rates were higher (for more than 80% of the duration of the stimuli, p < 10(-5)) and the power of the HF modulation was lower (for more than 70% of the duration of the stimuli, p < 0.05) than during listening to unpleasant stimuli.

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“…Wavelet analysis accounts for these non-stationary relationships by detecting patterns in signals (time series) at different time scales (Grinsted et al, 2004;Cazelles et al, 2008). Wavelet analysis has been applied in fields from medicine (e.g., Keissar et al, 2009) to hydrology (e.g., Biswas and Si, 2011;Graf et al, 2014;Fang et al, 2015) and numerous other fields. The method is also considered well suited for the analysis of ecological time series (e.g., Bradshaw and Spies, 1992;Grenfell et al, 2001;Keitt and Urban, 2005;Cazelles et al, 2008;Zhang et al, 2014).…”

Section: Core Ideasmentioning

confidence: 99%

Spatiotemporal Analysis of Dissolved Organic Carbon and Nitrate in Waters of a Forested Catchment Using Wavelet Analysis

Weigand

Bol²,

Reichert

et al. 2017

Vadose Zone Journal

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Understanding natural controls on N and C biogeochemical cycles is important to estimate human impacts on these cycles. This study examined the spatiotemporal relationships between time series of weekly monitored stream and groundwater N and C (assessed by NO 3 − and dissolved organic C [DOC]) in the forested Wüstebach catchment (Germany). In addition to traditional correlation analysis, we applied wavelet transform coherence (WTC) analysis to study variations in the correlation and lag time between the N and C time series for different time scales. Median transit times were used to connect hydrologic and water chemistry data. We defined three stream-water groups: (i) subsurface runoff dominated locations with strong seasonal fluctuations in concentrations, short transit times, and strong negative C/N correlations with short time lags, (ii) groundwater dominated locations, with weaker seasonal fluctuations, longer transit times, and weaker C/N correlations with lags of several months, and (iii) intermediate locations, with moderate seasonal fluctuations, moderate transit times, and strong C/N correlations with short time lags. Water transit times could be identified as key drivers for the C/N relationship and we conclude that C and N transport in stream water can be explained by mixing of groundwater and subsurface runoff. Complemented by transit times and the hydrochemical time series, WTC analysis allowed us to discriminate between different water sources (groundwater vs. subsurface runoff). In conclusion, we found that in time series studies of hydrochemical data, e.g., DOC and NO 3 − , WTC analysis can be a viable tool to identify spatiotemporally dependent relationships in catchments.Abbreviations: COI, cone of influence; CWT, continuous wavelet transform; DOC, dissolved organic carbon; MedTT, median transit time; WTC, wavelet transform coherence; XWT, cross wavelet transform.The cycles of nutrients such as N and C are closely linked. For example, N saturation is linked to C limitation in the soil for microbial processes (Kopáček et al., 2013). In aqueous ecosystems, increasing NO 3 − concentrations (Galloway et al., 2008) and widespread significant increases in the concentrations of dissolved organic C (DOC) in streams during the last two decades have become a major global issue (e.g., Evans et al., 2005;Galloway et al., 2008;Vitousek et al., 1997). Newly created reactive N (biologically or photochemically active) contributes to environmental problems such as air pollution, eutrophication, soil acidification, and climate change (Vitousek et al., 1997;Galloway et al., 2008). The environmental and therefore also social consequences require detailed spatiotemporal models to predict the fate of reactive N across environments (e.g., Mulholland et al., 2008;Taylor and Townsend, 2010;Barnes and Raymond, 2010). For this purpose, it might be important to understand causal links to the spatiotemporal patterns of DOC. Core Ideas• WTC analysis was used to elucidate the non-stationary C/N relationship at different time...

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Coherence analysis between respiration and heart rate variability using continuous wavelet transform

Cited by 53 publications

References 32 publications

Cerebrovascular pressure reactivity monitoring using wavelet analysis in traumatic brain injury patients: A retrospective study

Cerebrovascular pressure reactivity monitoring using wavelet analysis in traumatic brain injury patients: A retrospective study

A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis

Spatiotemporal Analysis of Dissolved Organic Carbon and Nitrate in Waters of a Forested Catchment Using Wavelet Analysis

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