Insufficient sampling frequencies skew heart rate variability estimates: Implications for extracting heart rate metrics from neuroimaging and physiological data

Validity of Ultra-Short-Term Heart Rate Variability Derived from Femoral Arterial Pulse Waveform in a British Military Cohort

Various non-electrocardiogram (ECG) based methods are considered reliable sources of heart rate variability (HRV) measurement. However, the ultra-short recording of a femoral arterial waveform has never been validated against the gold-standard ECG-based 300s HRV and was the aim of this study.A validity study was conducted using a sample from the first follow-up of the longitudinal ADVANCE study UK. The participants were adult servicemen (n = 100); similar in age, rank, and deployment period (Afghanistan 2003-2014). The femoral arterial waveforms (14s) from the pulse wave velocity (PWV) assessment, and ECG (300s) were recorded at rest in the supine position using the Vicorder™ and Bittium Faros™ devices, respectively, in the same session. HRV analysis was performed using Kubios Premium. Resting heart rate (HR) and root mean square of successive differences (RMSSD) were reported. The Bland-Altman %plots were constructed to explore the PWV-ECG agreement in HRV measurement. A further exploratory analysis was conducted across methods and durations.The participants' mean age was 38.0 ± 5.3 years. Both PWV-derived HR (r = 0.85) and RMSSD (rs=0.84) showed strong correlations with their 300s-ECG counterparts (p < 0.001). Mean HR was significantly higher with ECG than PWV (mean bias: -12.71 ± 7.73%, 95%CI: -14.25%, -11.18%). In contrast, the difference in RMSSD between the two methods was non-significant [mean bias: -2.90 ± 37.82% (95%CI: -10.40%, 4.60%)] indicating good agreement. An exploratory analysis of 14s ECG-vs-300s ECG measurement revealed strong agreement in both RMSSD and HR.The 14s PWV-derived RMSSD strongly agrees with the gold-standard (300s-ECG-based) RMSSD at rest. Conversely, HR appears method sensitive.

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram

Objective. To develop analytical formulas which can serve as quantitative guidelines for the selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart rate (HR) and heart rate variability (HRV) with a desired level of accuracy.Approach. We developed analytical formulas which relate the ECG sampling rate to conservative bounds on HR and HRV errors: (i) one relating HR and sampling rate to a HR error bound and (ii) the others relating sampling rate to HRV error bounds (in terms of root-mean-square of successive differences (RMSSD) and standard deviation of normal sinus beats (SDNN)). We validated the formulas using experimental data collected from 58 young healthy volunteers which encompass a wide HR and HRV ranges through strenuous exercise.Main results. The results strongly supported the validity of the analytical formulas as well as their tightness. The formulas can be used to (i) predict an upper bound of inaccuracy in HR and HRV for a given sampling rate in conjunction with HR and HRV as well as to (ii) determine a sampling rate to achieve a desired accuracy requirement at a given HR or HRV (or its range).Significance. HR and its variability (HRV) derived from the ECG have been widely utilized in a wide range of research in physiology and psychophysiology. However, there is no established guideline for the selection of the sampling rate for the ECG required to calculate HR and HRV with a desired level of accuracy. Hence, the analytical formulas may guide in selecting sampling rates for the ECG tailored to various applications of HR and HRV.

Heart Rate Variability Code: Does It Exist and Can We Hack It?

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal-analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously.

Do mean values tell the full story? Cardiac cycle and biological sex comparisons in temporally derived neurovascular coupling metrics

Previous reports have noted cerebrovascular regulation differs across the cardiac cycle, with greater regulation occurring within systole. However, this methodological notion has not been meticulously scrutinized during temporally deduced neurovascular coupling (NVC) metrics with additional respect to biological sex. Analyses of 111 healthy individuals (40 females/71 males) were performed where participants engaged in the "Where's Waldo?" paradigm. All NVC parameters were quantified in the posterior and middle cerebral arteries at 310 unique timepoints. Several individuals completed repeat testing which enabled for between-day (3 timepoints) and within-day (7 timepoints) reliability comparisons in 17 and 11 individuals, respectively. One-way analysis of variance compared NVC metrics between diastole, mean, and systole values, as well as differences between biological sexes. Greater absolute cerebral blood velocity (CBv; baseline and peak) and total activation (area under the curve) were noted within systole for both posterior cerebral artery (PCA; P < 0.001) and middle cerebral artery (MCA; P < 0.001) values; however, the relative percent increase in CBv was greater within diastole (P < 0.001). Females had an elevated diastolic and mean CBv and a greater diastolic cerebrovascular conductance (P < 0.050). No sex differences were present for systolic CBv measures and within parameters quantifying the NVC response (area under the curve/relative CBv increase) across the cardiac cycle (P > 0.072). Future investigations seeking to differentiate cerebral regulatory mechanisms between clinical populations may benefit by performing their analyses across the cardiac cycle, as certain pathogenesis may affect one aspect of the cardiac cycle independently. Minimal differences were noted between females and males for metrics characterizing the NVC response across the cardiac cycle.NEW & NOTEWORTHY Neurovascular coupling (NVC) studies commonly assess the mean cerebral hemodynamic response with little consideration for diastole, systole, and biological sex. Greater total activation expressed as the area under the curve was seen within systole compared with mean and diastole. Resting cerebral blood velocity sex differences were more prevalent during diastole when the cerebrovasculature was pressure-passive. Future studies should assess the NVC response across the cardiac cycle as it may help delineate the underlying pathophysiology of various clinical populations.

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme

Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We propose a hybrid intelligent scheme for physiological status prediction, which can be effectively utilized to predict the physiological status of patients and provide a reference for clinical diagnosis. Our proposed scheme initially extracted the attribute information of nonlinear dynamic changes in physiological signals. The maximum discriminant feature subset was selected by employing conditional relevance mutual information feature selection. An optimal subset of features was fed into the particle swarm optimization-support vector machine classifier to perform classification. For the prediction task, the proposed hybrid intelligent scheme was tested on the Sleep Heart Health Study dataset for sleep status prediction. Experimental results demonstrate that our proposed intelligent scheme outperforms the conventional machine learning classification methods.

Repetitive bout of controlled soccer heading does not alter heart rate variability metrics: A preliminary investigation

Objectives

There is elevated unease regarding how repetitive head impacts, such as those associated with soccer heading, contribute to alterations in brain function. This study examined the extent heart rate variability (HRV) and cardiac baroreceptor sensitivity (BRS) metrics are altered immediately following an acute bout of soccer heading.

Methods

Seven male elite soccer players (24.1 ± 1.5 years) completed 40 successful soccer headers in 20-min. The headers were performed under controlled circumstances using a soccer ball launcher located 25 meters away and using an initial ball velocity of 77.5 ± 3.7 km/h (heading condition). An accelerometer (xPatch) on the right mastoid process quantified linear/rotational head accelerations. Participants also completed sham (body contact) and control (non-contact) sessions. A three-lead ECG and finger photoplethysmography characterized short-term spontaneous HRV/cardiac BRS, before and after each condition. The SCAT3 indexed symptom scores pre-post exposures to all three conditions.

Results

During the heading condition, cumulative linear and rotational accelerations experienced were 1,574 ± 97.9 g and 313,761 ± 23,966 rad/s², respectively. Heart rate trended toward an increase from pre- to post-heading (p = 0.063), however HRV metrics in the time-domain (ps > 0.260) and frequency-domain (ps > 0.327) as well as cardiac BRS (ps > 0.144) were not significantly changed following all three conditions. Following the heading condition, SCAT3 symptom severity increased (p = 0.030) with a trend for symptom score augmentation (p = 0.078) compared to control and sham.

Conclusion

Whereas, symptoms as measured by the SCAT3 were induced following an acute bout of controlled soccer heading, these preliminary findings indicate they were not accompanied by alterations to autonomic function. Ultimately, this demonstrates further research is needed to understand the physiological underpinnings of alterations in brain function occurring immediately after a bout of soccer heading and how these may, over time, contribute to long-term neurological impairments.