A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

Ellis, Robert J.; Zhu, Beien; Koenig, Julian; Thayer, Julian F.; Wang, Ye

doi:10.1088/0967-3334/36/9/1827

Cited by 74 publications

(45 citation statements)

References 79 publications

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“…First, this is a secondary analysis that used pulse oximetry data. Although pulse oximetry is acceptable for the analyses of HRV, future studies would do well to implement recordings of RVA using ECG at a higher sample-rate (Ellis et al , 2015). In a first step, the analyses focused on difference in structural thickness in selected ROIs, given that the majority of previous studies in healthy subjects addressed measures of structural thickness in association with HRV (Woodward et al , 2008; Winkelmann et al , 2016; Koenig et al 2018; Yoo et al , 2018).…”

Section: Discussionmentioning

confidence: 99%

Brain structural thickness and resting state autonomic function in adolescents with major depression

Koenig

Schreiner

Klimes‐Dougan

et al. 2018

Social Cognitive and Affective Neuroscience

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Major depressive disorder (MDD) has been associated with abnormalities in cortical thickness and autonomic function. Adolescence is a time notable for brain development and MDD onset. In healthy adolescents, greater resting state vagal activity (RVA) is associated with lower cortical thickness. The relationship between brain structural thickness and RVA in adolescents with MDD has not previously been studied. This secondary analysis drew on a sample of 37 non-depressed controls and 53 adolescents with MDD. Resting state heart rate and two indices of RVA (HF-HRV and RMSSD) were recorded during a neuroimaging session. Cortical thickness within fronto-limbic regions of interest was measured using Freesurfer analysis of T1-weighted high-resolution structural images. Self-reports of depression severity showed a significant interaction with cortical thickness of the right insula in predicting RMSSD [t = 2.22, P=0.030, β = 5.44; model fit of the interaction term as indicated by the ‘Bayes Factor’ (BF): 7.58] and HF-HRV (t = 2.09, P=0.041, β = 4.72; BF: 7.94). Clinician ratings of depression severity showed further interactions. Findings underscore the important relationships between RVA and cortical development, suggesting two possible explanations: (i) in adolescent MDD, greater fronto-limbic thickness is compensatory for deficits in autonomic regulation or (ii) increased autonomic arousal results in delayed fronto-limbic maturation. Longitudinal research is necessary to further clarify the nature of the relationship between autonomic functioning and cortical development.

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Section: Discussionmentioning

confidence: 99%

Brain structural thickness and resting state autonomic function in adolescents with major depression

Koenig

Schreiner

Klimes‐Dougan

et al. 2018

Social Cognitive and Affective Neuroscience

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“…Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology", 1996) propose a sampling rate of at least 500 Hz. Furthermore, several authors have advocated a sampling rate of at least 1000 Hz in order to not bias the HRV analysis (Ellis, Zhu, Koenig, Thayer, & Wang, 2015;Hejjel & Roth, 2004;Singh, Manjit, & Vijay Kumar, 2014). Technically, video and plethysmographic system use sampling rates from 50 to 256 Hz (Chuang, et al, 2015;Moreno, et al, 2015;Parak, et al, 2015), i.e.…”

Section: Discussionmentioning

confidence: 99%

“…far below the suggested minimum of 500 Hz. Several studies also stress the importance of using appropriate algorithms to detect R-peak (Ellis, et al, 2015;Peng, et al, 2015) and appropriate correction methods thereafter (Rincon Soler, et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Effects of R-R time series accuracy on heart rate variability indexes

Cassirame

Chevrolat²,

Mourot

2019

Mov Sport Sci/Sci Mot

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Our study aims to quantify the effect of inaccuracy in the R-R times series on heart rate variability (HRV) indexes. Starting from 75 series measured in supine, standing and submaximal exercise situations in 25 healthy male subjects, we generated 15 new R-R time series with accuracy from ±2 to 30 ms. HRV analyses was performed on the 1200 obtained series and compared with the results of the original series. The results showed that during supine, HRV indexes were moderately affected by inaccuracy up to ±30 ms. However, during standing, the indexes were affected to a greater degree and it was even more evident during exercise: in this later case significant difference in the root mean square of successive standard deviations (rMSSD), a commonly used parasympathetic index in sport sciences for both health and performance purposes, were observed when inaccuracy was equal or greater than 4 ms. Also, a relationship between inaccuracy and rMSSD was highlighted: the lower the rMSSD, the greater the error. Our study underscored the importance of the accuracy of R-R measurement for heart rate variability and provides recommendation regarding technology and measurement errors on final results. Résumé --Importance de la précision de la série des intervalles RR sur les indices de variabilité de fréquence cardiaque calculés. Notre étude avait pour but de quantifier l'effet de l'imprécision de la série des intervalles RR sur les indices de variabilité de la fréquence cardiaque (VFC) calculée ultérieurement. Partant de 75 séries mesurées en condition couché, debout et à l'exercice chez 25 hommes en bonne santé, 15 nouvelles séries (précision de ±2 à 30 ms) ont été générées. Des analyses de VFC ont été effectuées et les résultats comparés à la série originale. Couché, les indices de VFC n'étaient que modérément affectés, même pour des erreurs de 30 ms. Debout, les indices étaient plus affectés et cela a été encore plus évident durant l'exercice. Dans ce cas, des différences significatives dans l'indice parasympathique root mean square of successive standard deviations (rMSSD), couramment utilisé en sciences du sport, ont été observé pour des erreurs aussi basses que 4 ms. Ces résultats montrent que plus rMSSD est faible, plus grande est l'erreur générée. Notre étude montre donc l'importance du degré de précision avec lequel la série des intervalles RR pour effectuer une analyse de VFC a été obtenue, ce qui permet de dégager des recommandations pratiques en ce qui concerne les moyens technologiques utilisés et les niveaux d'erreurs acceptables.

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“…Effects of lower sampling frequency on the HRV indicators can be mitigated by signal interpolation [4]. The first step in the pipeline is therefore an oversampling to decrease the error in the estimation of the beat position.…”

Section: Oversamplingmentioning

confidence: 99%

DBD-RCO: Derivative Based Detection and Reverse Combinatorial Optimization to improve heart beat detection for wearable devices

Bizzego

Furlanello²

2017

Preprint

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The diffusion of wearable sensors enables the monitoring of heart physiology in real-life contexts. Wearable technology is characterized by important advantages but also by technical limitations that affect the quality of the collected signals in terms of movement artifacts and presence of noise. Therefore specific signal processing algorithms are required to cope with the lower quality and different characteristic of signals collected with wearable sensor units. Here we propose and validate a pipeline to detect heartbeats in cardiac signals, extract the Inter Beat Intervals (IBI) and compute the Heart Rate Variability (HRV) indicators from wearable devices. In particular, we describe the novel Derivative-Based Detection (DBD) algorithm to estimate the beat position in Blood Volume Pulse (BVP) signals and the Reverse Combinatorial Optimization (RCO) algorithm to identify and correct IBI extraction errors. The pipeline is first validated on data from clinical-grade sensors, then on a benchmark dataset including examples of movement artifacts in a real-life context. The accuracy of the DBD algorithm is assessed in terms of precision and recall of the detection; error in the IBI values is quantified by root mean square error. The reliability of HRV indicators is evaluated by the Bland-Altman ratio. The DBD algorithm performs better than a state-of-art algorithm for both medical-grade and wearable devices. However, as already found in similar studies, worse reliability is found with the BVP signal in computing frequency domain HRV indicators, in particular with wearable devices.keywords: heart beat detection, wearable sensors, inter beat intervals, blood volume pulse, heart rate variability 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a

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A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

Cited by 74 publications

References 79 publications

Brain structural thickness and resting state autonomic function in adolescents with major depression

Brain structural thickness and resting state autonomic function in adolescents with major depression

Effects of R-R time series accuracy on heart rate variability indexes

DBD-RCO: Derivative Based Detection and Reverse Combinatorial Optimization to improve heart beat detection for wearable devices

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