Ultra-short term heart rate variability as a tool to assess changes in valence

Schippers, Arjen; Aben, Bart; Griep, Yannick; Overwalle, Frank Van

doi:10.1016/j.psychres.2018.10.005

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Our results showed that DFA α 1 as a non-linear index of HRV did not distinguish the different affective image regimes under ultra-short recordings. Although previous work showed that there can be differences in HRV measures when comparing neutral and arousing sessions ( Valenza et al, 2012 ) or watching positive and negative videos ( Barquero-Pérez et al, 2020 ; Ghiasi et al, 2020 ), our findings are consistent with other works that showed the linear HRV metric RMSSD did not distinguish between positive and negative emotions using IAPS ( Schippers et al, 2018 ), and non-linear HRV metric DFA α 1 did not distinguish different emotionally arousing settings ( Marín-Morales et al, 2021 ). We hypothesize that because pure regime participants knew to expect images of the same type, there may not have been a sufficient change in emotional context to create significant variation in their heart rate.…”

Section: Discussionsupporting

confidence: 91%

Heart Rate Variability in Healthy Subjects During Monitored, Short-Term Stress Followed by 24-hour Cardiac Monitoring

Zarubin²,

Steinmetz³

et al. 2022

Front. Physiol.

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Heart Rate Variability (HRV) can be a useful metric to capture meaningful information about heart function. One of the non-linear indices used to analyze HRV, Detrended Fluctuation Analysis (DFA), finds short and long-term correlations in RR intervals to capture quantitative information about variability. This study focuses on the impact of visual and mental stimulation on HRV as expressed via DFA within healthy adults. Visual stimulation can activate the automatic nervous system to directly impact physiological behavior such as heart rate. In this investigation of HRV, 70 participants (21 males) viewed images on a screen followed by a math and recall task. Each viewing segment lasted 2 min and 18 s. The math and memory recall task segment lasted 4 min total. This process was repeated 9 times during which the participants’ electrocardiogram was recorded. 37 participants (12 males) opted in for an additional 24-h Holter recording after the viewing and task segments of the study were complete. Participants were randomly assigned to either a pure (organized image presentation) or mixed (random image presentation) image regime for the viewing portion of the study to investigate the impact of the external environment on HRV. DFA α1 was extracted from the RR intervals. Our findings suggest that DFA α1 can differentiate between the viewing [DFA α1 range from 0.96 (SD = 0.25) to 1.08 (SD = 0.22)] and the task segments [DFA α1 range from 1.17 (SD = 0.21) to 1.26 (SD = 0.25)], p < 0.0006 for all comparisons. However, DFA α1 was not able to distinguish between the two image regimes. During the 24-hour follow up, participants had an average DFA α1 = 1.09 (SD = 0.14). In conclusion, our findings suggest a graded response in DFA during short term stimulation and a responsiveness in participants to adjust physiologically to their external environment expressed through the DFA exponent.

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Section: Discussionsupporting

confidence: 91%

Heart Rate Variability in Healthy Subjects During Monitored, Short-Term Stress Followed by 24-hour Cardiac Monitoring

Zarubin²,

Steinmetz³

et al. 2022

Front. Physiol.

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“…rejected Choi et al. 's claim by stating RMSSD measured with a 30s moving window cannot discern between positive and negative emotions with IAPS images (Schippers et al., 2018). In line with Schippers et al.…”

Section: Discussionmentioning

confidence: 99%

The effectiveness of time domain and nonlinear heart rate variability metrics in ultra‐short time series

Gu,

Zarubin,

Martsberger

2023

Physiological Reports

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Ultra short‐term (UST) heart rate variability (HRV) has been used to establish normative HRV values. This study aims to investigate whether HRV metrics can capture changes in HRV from external stimuli, and whether these metrics remain effective under various recording length. Participants completed varying stimulating activities including viewing images, arithmetic tasks, and memory recall of viewed images. SDNN, RMSSD, pNN50, SD2, SD1/SD2, and DFA were extracted from the data. Comparing arithmetic calculation and the first minute of memory recall, SDNN, pNN50, SD2, and SD1/SD2 had significant HRV differences; this suggests that these metrics can distinguish the inherently different stimuli participants were exposed to. However, comparing first minute of viewing with that of the second, SDNN, pNN50, and SD2, presented some significant HRV differences during two inherently similar stimuli. Comparing the first 60–120 s during viewing, SDNN, pNN50, and SD2 also presented significant differences. Our results suggest that SDNN, pNN50, and SD2 may not be robust in evaluating UST HRVs in replacement of the standard short‐term HRV. It may be beneficial to analyze multiple HRV metrics, particularly SD1/SD2, to achieve a more holistic understanding of the underlying physiology.

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“…Estimating HRV measures on the basis of such relatively short observation windows recently gained interest as it allows to study HRV changes with a higher temporal resolution compared to standard window lengths (Shaffer and Ginsberg, 2017). Several studies showed that such ultra-short-term HRV measures highly correlate with the clinically accepted short-term measures computed over a standard window length of five minutes (Munoz et al, 2015, Schippers et al, 2018, Castaldo et al, 2019.…”

Section: Outcome Measuresmentioning

confidence: 99%

Heart rate variability predicts decline in sensorimotor rhythm control

Nann

Haslacher

Colucci

et al. 2021

Preprint

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Voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g., in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. It would thus be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline during voluntary control of SMR-ERD across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 minutes each. During the second run, task difficulty was gradually increased. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the first run, during the second run, both measures declined over time at high correlation (performance: -0.61%/10s, p = 0; HRV: -0.007ms/10s, p < .001). We found that HRV Granger-caused BCI control performance (p < .001) exhibited predictive characteristics of HRV on an individual participant level. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g., to individualize and optimize assistive BCI systems in stroke.

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Ultra-short term heart rate variability as a tool to assess changes in valence

Cited by 7 publications

References 37 publications

Heart Rate Variability in Healthy Subjects During Monitored, Short-Term Stress Followed by 24-hour Cardiac Monitoring

Heart Rate Variability in Healthy Subjects During Monitored, Short-Term Stress Followed by 24-hour Cardiac Monitoring

The effectiveness of time domain and nonlinear heart rate variability metrics in ultra‐short time series

Heart rate variability predicts decline in sensorimotor rhythm control

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