Lower Resting State Heart Rate Variability Relates to High Pain Catastrophizing in Patients with Chronic Whiplash-Associated Disorders and Healthy Controls

Pain Practice ◽  
2015 ◽  
Vol 16 (8) ◽  
pp. 1048-1053 ◽  
Author(s):  
Julian Koenig ◽  
Margot De Kooning ◽  
Anthony Bernardi ◽  
DeWayne P. Williams ◽  
Jo Nijs ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Resting State ◽  
Pain Catastrophizing ◽  
Healthy Controls ◽  
Whiplash Associated Disorders ◽  
Chronic Whiplash

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

scholarly journals Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Rosa María Escorihuela ◽  
Lluís Capdevila ◽  
Juan Ramos Castro ◽  
María Cleofé Zaragozà ◽  
Sara Maurel ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Fatigue Syndrome ◽  
Frequency Domain ◽  
Chronic Fatigue ◽  
Myalgic Encephalomyelitis ◽  
Healthy Controls ◽  
Fatigue Syndrome ◽  
Potential Biomarker ◽  
Fatigue Severity

Abstract Background Heart rate variability (HRV) is an objective, non-invasive tool to assessing autonomic dysfunction in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). People with CFS/ME tend to have lower HRV; however, in the literature there are only a few previous studies (most of them inconclusive) on their association with illness-related complaints. To address this issue, we assessed the value of different diurnal HRV parameters as potential biomarker in CFS/ME and also investigated the relationship between these HRV indices and self-reported symptoms in individuals with CFS/ME. Methods In this case–control study, 45 female patients who met the 1994 CDC/Fukuda definition for CFS/ME and 25 age- and gender-matched healthy controls underwent HRV recording-resting state tests. The intervals between consecutive heartbeats (RR) were continuously recorded over three 5-min periods. Time- and frequency-domain analyses were applied to estimate HRV variables. Demographic and clinical features, and self-reported symptom measures were also recorded. Results CFS/ME patients showed significantly higher scores in all symptom questionnaires (p < 0.001), decreased RR intervals (p < 0.01), and decreased HRV time- and frequency-domain parameters (p < 0.005), except for the LF/HF ratio than in the healthy controls. Overall, the correlation analysis reached significant associations between the questionnaires scores and HRV time- and frequency-domain measurements (p < 0.05). Furthermore, separate linear regression analyses showed significant relationships between self-reported fatigue symptoms and mean RR (p = 0.005), RMSSD (p = 0.0268) and HFnu indices (p = 0.0067) in CFS/ME patients, but not in healthy controls. Conclusions Our findings suggest that ANS dysfunction presenting as increased sympathetic hyperactivity may contribute to fatigue severity in individuals with ME/CFS. Further studies comparing short- and long-term HRV recording and self-reported outcome measures with previous studies in larger CFS/ME cohorts are urgently warranted.

scholarly journals Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Cao ◽  
Bailu Ye ◽  
Linghui Yang ◽  
Fei Lu ◽  
Luping Fang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Characteristic Curve ◽  
Clinical Analysis ◽  
Healthy Controls ◽  
Heart Failure Patients ◽  
Rr Interval ◽  
Frequency Components

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls.

Heart rate variability interacts with recovery of resting-state connectivity in amygdala circuitry and the persistence of symptoms after sport-related concussion

Neurology ◽  
2020 ◽  
Vol 95 (20 Supplement 1) ◽  
pp. S15.2-S16
Author(s):  
Kevin Bickart ◽  
Christopher Andrew Sheridan ◽  
Corey M. Thibeault ◽  
Robert Hamilton ◽  
James LeVangie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Resting State ◽  
Autonomic Function ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Network Recovery ◽  
Symptom Persistence ◽  
Resting State Connectivity ◽  
Over Time

ObjectiveWe investigated longitudinal trajectories of resting-state fMRI (rsfMRI), autonomic function, and graded symptoms after sport-related concussion (SRC).BackgroundLimbic circuitry may be particularly vulnerable to traumatic brain injury, which could explain the affective and autonomic dysfunction that some patients develop. Relatively few studies have performed longitudinal rsfMRI analyses in concussion and fewer have combined imaging with autonomic and symptom data. We leveraged published limbic rsfMRI networks centered on the amygdala that include core affective and autonomic structures to test whether athletes with SRC would have altered connectivity, and that network recovery would be related to measures of autonomic function and symptom persistence.Design/MethodsWe compared rsfMRI connectivity of amygdala networks in college athletes with SRC (N = 31, female = 14) at three time points after concussion (T1 = 4 days, T2 = 10–14 days, T3 = 2–3 months) and matched controls with no concussion (in-sport control [ISC] N = 36, female = 17).ResultsSRCs show greater amygdala network connectivity as compared to ISCs (T1 p = 0.003, T2 p = 0.014) that normalizes over time (T3 p = 0.182). However, SRCs with higher versus lower heart rate variability (HRV), as measured by pNN50 at T1, have opposing trajectories of connectivity. That is, SRCs with higher HRV have connectivity that starts high and normalizes over time (T1 p = 0.001, T2 p = 0.055, T3 p = 0.576) whereas SRCs with lower HRV have connectivity that increases over time (T1 p = 0.429, T2 p = 0.050, T3 p = 0.002). Furthermore, SRCs with greatest connectivity at T3, presumably the least recovered, have the most symptoms on the Graded Symptom Checklist at ∼3 months (r = 0.635, p = 0.001).ConclusionsHeightened connectivity of amygdala circuitry acutely after a concussion and its normalization over time may be protective, and with HRV, may be a biomarker of symptom persistence.

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