Editorial: Horizon 2030: Innovative Applications of Heart Rate Variability

Laborde, Sylvain; Mosley, Emma; Bellenger, Clint R.; Thayer, Julian F.

doi:10.3389/fnins.2022.937086

Cited by 12 publications

(12 citation statements)

References 30 publications

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“…Nowadays, the use of HRV as a polyvalent prognostic tool and reliable health indicator spans across researchers and practitioners from many different fields (Laborde et al, 2022). HRV is also assessed in rat models to increase the knowledge of the role of cardiac vagal modulation in several (patho)physiological processes (e.g., Sgoifo et al, 1997;Wood et al, 2012;Lee et al, 2013;Carnevali and Sgoifo, 2014;Chuang et al, 2017;Morais-Silva et al, 2019).…”

Section: Implications For Translational Researchmentioning

confidence: 99%

Sex differences in heart rate and heart rate variability in rats: Implications for translational research

et al. 2023

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The present study aimed to investigate sex differences in measures of cardiac chronotropy and heart rate variability (HRV) in 132 young adult wild-type Groningen rats (n = 45 females). Electrocardiographic signals were recorded for 48 h in freely moving rats to quantify heart rate (HR) and inter-beat interval (IBI) as measures of cardiac chronotropy, and time- and frequency-domain HRV parameters as physiological readouts of cardiac vagal modulation. Females showed greater vagally-mediated HRV despite having higher HR and shorter IBI than males during undisturbed conditions. Such differences were evident i) at any given level of HRV, and ii) both during the 12-h light/inactive and 12-h dark/active phase of the daily cycle. These findings replicate the paradoxical cardiac chronotropic control reported by human meta-analytic findings, since one would expect greater vagally-mediated HRV to be associated with lower HR and longer IBI. Lastly, the association between some HRV measures and HR was stronger in female than male rats. Overall, the current study in young adult rats provides data illustrating a sex-dependent association between vagally-mediated HRV and indexes of cardiac chronotropy. The current results i) are in line with human findings, ii) suggest to always consider biological sex in the analysis and interpretation of HRV data in rats, and iii) warrant the use of rats for investigating the neuro-hormonal basis and temporal evolution of the impact of sex on the association between vagally-mediated HRV and cardiac chronotropy, which could inform the human condition.

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Section: Implications For Translational Researchmentioning

confidence: 99%

Sex differences in heart rate and heart rate variability in rats: Implications for translational research

et al. 2023

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“…They both focus particularly on heart rate variability as a key biomarker of health and various disease states. The great number of pathological states and functional indicators have been reviewed by Laborde et al [6] and Drury et al [7] and various metrics of HRV are described as well, including time-domain, frequency-domain, and nonlinear analyses [8]. A key conceptual component of these theories is the social engagement system, which is the basis for all attachment phenomena and sociality.…”

Section: Theoretical and Conceptual Issuesmentioning

confidence: 99%

Perspective Chapter: Edge Computing in Digital Epidemiology and Global Health

Drury¹

2023

Edge Computing - Technology, Management and Integration

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Edge computation (EC) will be explored from the viewpoint of complex systems. An evolutionary and ecological context will be described in detail, including the subjects of epigenetics, self-domestication, attachment theory, scientific cosmology, deep learning, and other artificial intelligence issues and the role of wireless data acquisition analysis and feedback. A technical exemplar will be described and examples of potential integration with various systems such as public health and epidemiology, clinical medicine, operations, and fitness will be proposed. Also, various system vulnerabilities and failures will be discussed and policy implications in the global and clinical health and wellness domains will be identified.

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“…As shown in Figures 2-4, the category boundary defined by the HRV-based cutoff point is depicted as a dotted line to classify the mild and moderate CRF categories. According to equations (6)(7)(8), the classification result of each model is associated with a binary variable, C j i . The binary variable, C j i , is set to 1 while the test case is classified as category, i, for HRV metrics, j, and set to 0 otherwise.…”

Section: Figurementioning

confidence: 99%

“…Note that since all weights are set to 1, the model expressed in equation ( 9) employs majority voting to predict a classification decision. That is, model shown in equation ( 9) predicts category, i, while more than one model among those shown in equations (6)(7)(8) classify the target case as category, i. In model expressed in equation ( 9), a higher weight indicates a higher Classification ratio and p-value obtained from t-test between mild and moderate categories for varied (LF/HF) disorder ratio in the active phase.…”

Section: Figurementioning

confidence: 99%

“…, w LH P } in the decreased priority order. Table 5 gives the classification performance comparison between the models provided in equations (6)(7)(8)(9). For models based on single HRV metrics, the linear model expressed in equation ( 7) can achieve the highest accuracy (0.73) for the mild CRF category, and the model shown in equation ( 8) has the highest accuracy (0.88) in the moderate category.…”

Section: Figurementioning

confidence: 99%

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Cancer-related fatigue classification based on heart rate variability signals from wearables

et al. 2023

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BackgroundCancer-related fatigue (CRF) is the most distressing side effect in cancer patients and affects the survival rate. However, most patients do not report their fatigue level. This study is aimed to develop an objective CRF assessment method based on heart rate variability (HRV).MethodsIn this study, patients with lung cancer who received chemotherapy or target therapy were enrolled. Patients wore wearable devices with photoplethysmography that regularly recorded HRV parameters for seven consecutive days and completed the Brief Fatigue Inventory (BFI) questionnaire. The collected parameters were divided into the active and sleep phase parameters to allow tracking of fatigue variation. Statistical analysis was used to identify correlations between fatigue scores and HRV parameters.FindingsIn this study, 60 patients with lung cancer were enrolled. The HRV parameters including the low-frequency/high-frequency (LF/HF) ratio and the LF/HF disorder ratio in the active phase and the sleep phase were extracted. A linear classifier with HRV-based cutoff points achieved correct classification rates of 73 and 88% for mild and moderate fatigue levels, respectively.ConclusionFatigue was effectively identified, and the data were effectively classified using a 24-h HRV device. This objective fatigue monitoring method may enable clinicians to effectively handle fatigue problems.

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Editorial: Horizon 2030: Innovative Applications of Heart Rate Variability

Cited by 12 publications

References 30 publications

Sex differences in heart rate and heart rate variability in rats: Implications for translational research

Sex differences in heart rate and heart rate variability in rats: Implications for translational research

Perspective Chapter: Edge Computing in Digital Epidemiology and Global Health

Cancer-related fatigue classification based on heart rate variability signals from wearables

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