An open source benchmarked toolbox for cardiovascular waveform and interval analysis

Vest, Adriana Nicholson; Poian, Giulia Da; Li, Qiao; Liu, Chengyu; Nemati, Shamim; Shah, Amit; Clifford, Gari D.

doi:10.1088/1361-6579/aae021

Cited by 221 publications

(185 citation statements)

References 61 publications

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“…Despite the wide use of these HRV indices, there is still ambiguity in the research community emerging from the lack of clear documentation, validation, and standardization of different HRV signal processing methods. Here for HRV analysis, we used a MATLAB-based open source HRV toolbox that was previously validated with a variety of HRV measurement techniques and platforms to calculate LF HRV, HF HRV, LF/HF HRV, SD1, SD2, SD1/ SD2 [91].…”

Section: Signal Processing and Parameter Extractionmentioning

confidence: 99%

Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stress

et al. 2020

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Background: Stress is associated with activation of the sympathetic nervous system, and can lead to lasting alterations in autonomic function and in extreme cases symptoms of posttraumatic stress disorder (PTSD). Vagal nerve stimulation (VNS) is a potentially useful tool as a modulator of autonomic nervous system function, however currently available implantable devices are limited by cost and inconvenience. Objective: The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to stress. Methods: Using a double-blind approach, we investigated the effects of active or sham tcVNS on peripheral cardiovascular and autonomic responses to stress using wearable sensing devices in 24 healthy human participants with a history of exposure to psychological trauma. Participants were exposed to acute stressors over a three-day period, including personalized scripts of traumatic events, public speech, and mental arithmetic tasks. Results: tcVNS relative to sham applied immediately after traumatic stress resulted in a decrease in sympathetic function and modulated parasympathetic/sympathetic autonomic tone as measured by increased pre-ejection period (PEP) of the heart (a marker of cardiac sympathetic function) of 4.2 ms (95% CI 1.6e6.8 ms, p < 0.01), decreased peripheral sympathetic function as measured by increased photoplethysmogram (PPG) amplitude (decreased vasoconstriction) by 47.9% (1.4e94.5%, p < 0.05), a 9% decrease in respiratory rate (À14.3 to À3.7%, p < 0.01). Similar effects were seen when tcVNS was applied after other stressors and in the absence of a stressor. Conclusion: Wearable sensing modalities are feasible to use in experiments in human participants, and tcVNS modulates cardiovascular and peripheral autonomic responses to stress.

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Section: Signal Processing and Parameter Extractionmentioning

confidence: 99%

Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stress

et al. 2020

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“…Cardiac activity data were acquired using bipolar ECG while acquiring the MEG data, and processed using PhysioNet Cardiovascular Signal Toolbox (Goldberger et al, 2000;Vest et al, 2018) in Matlab (MATLAB 2017b, The MathWorks Inc, Natick, MA). To address non-stationarity in ECG recordings, mean heart rate (HR) and hearth rate variability (HRV) summary measures were based on the median across multiple sliding 5-min windows in 30-second steps across the entire eyes-closed, resting-state acquisition, 8.5 minutes.…”

Section: Physiological Recordingsmentioning

confidence: 99%

“…In terms of cardiovascular health, there may be more important measures that were not present in the CamCAN sample. Moreover, the analysis of heart rate variability estimates was based on normal-to-normal beats (Vest et al, 2018). The difference between NN-and RR-beat analysis is that the former considers the detection and exclusion of segments and participants with atrial fibrillation and other abnormal beats.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Tsvetanov

Henson

Jones

et al. 2019

Preprint

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Accurate identification of brain function is necessary to understand neurocognitive ageing, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is therefore essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health and neurovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and neurovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and neurovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and neurovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age-differences in RSFA, after controlling for cardiovascular health. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and neurovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterise neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

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“…Popular free HRV software libraries includes: Kubios [5], PhysioNet Cardiovascular Signal Toolbox [6,7], RHRV [8,9], ARTiiFACT [10], HRVAS [11], SinusCor [12], and hrvanalysis 1 .…”

Section: Introductionmentioning

confidence: 99%

“…HRV indices are computed, results are shown, and the exports made available. The tools differ in providing open access to the made calculations, the usage of HRV indices, the filtering of IBIs for robust measures, accepted data formats, the style of data representation, and user-friendly handling [6].…”

Section: Introductionmentioning

confidence: 99%

HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability

Marcus¹

2019

Computing in Cardiology Conference (CinC)

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Motivation: Many software tools for ECG processing are commercial. New innovative and alternative features for heart rate variability analysis (HRV) and improved methods in ECG preprocessing cannot be incorporated. Moreover, software manuals are lacking of clarity and often conceal the exact calculation methods that makes clinical interpretation difficult, and reproducibility is reduced.Software description:HRVTool provides an opensource and intuitive user-friendly environment for the HRV analysis in Matlab. The software is available at http://marcusvollmer.github.io/HRV and supports the processing of ECG, pulsatile waveforms and RR intervals from various sources (mat and text files containing raw data, Polar, PhysioNet, Hexoskin, BIOPAC, European Data Format, ISHNE Holter Standard Format, and Machine-Independent Beat files). An integrated heart beat detector locates R peaks or pulse waves. Visual inspection, and manual adjustments of beat locations are possible and the corresponding annotation file can be saved in a standard Matlab format or as a delimited text file. HRV statistics are computed in a sliding window to evaluate the alteration over time. HRV metrics can be exported. An animation of intervals supports pattern identification. Moreover the Matlab class (HRV.m) includes functions for windowed HRV computation that can be used for batch processing.

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An open source benchmarked toolbox for cardiovascular waveform and interval analysis

Cited by 221 publications

References 61 publications

Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stress

Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stress

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability

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