Detection of beat-to-beat intervals from wrist photoplethysmography in patients with sinus rhythm and atrial fibrillation after surgery

Tarniceriu, Adrian; Harju, Jarkko; Vehkaoja, Antti; Parák, Jakub; Delgado-Gonzalo, Ricard; Renevey, Philippe; Yli‐Hankala, Arvi; Korhonen, Ilkka

doi:10.1109/bhi.2018.8333387

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…Optical wrist heart rate monitor manufactured by PulseOn Ltd was used in the collection of beat-to-beat interval data. The device uses proprietary algorithms for heartbeat detection and signal quality assessment so that it provides only reliable beat-to-beat intervals to the user [7]. Data was collected from 18 students (average age 28 years, range 21-42 years) with no history of diagnosed sleep disorders.…”

Section: Methodsmentioning

confidence: 99%

Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography

Molkkari¹,

Tenhunen²,

Tarniceriu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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We assess the feasibility of heart rate variability (HRV) estimated from interbeat interval (IBI) data measured with wrist-worn photoplethysmography device for sleep stage classification. In particular, we examine fractal correlations in the IBIs as the function of both time and scale.Optical heart rate sensor by PulseOn Ltd was utilized for monitoring IBIs from 18 healthy young adult subjects. Reference ambulatory polysomnography recordings were scored by a sleep physician. The HRV was studied by detrended fluctuation analysis by computing scale-dependent spectra of scaling exponents α(s). Dynamic changes were tracked by calculating the spectra α(s, t) in moving temporal windows whose length varied with the scale.The dynamic landscapes of the alpha spectra show distinctive fractal correlations according to the underlying sleep stages. Respiratory effects, blood pressure variations, and thermoregulatory influence appear to be discernible as well. Classification of the alpha spectra yields up to 73 %, 60 % and 54 % average accuracies for 3-class (wake, REM, NREM), 4-class (wake, REM, N1+2, N3) and 5-class (wake, REM, N1, N2, N3) cases, respectively.

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Section: Methodsmentioning

confidence: 99%

Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography

Molkkari¹,

Tenhunen²,

Tarniceriu³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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“…16 A comparison of RR intervals (ie, the time between successive heart beats) from a wrist-based PPG device and an ECG device showed that PPG data from wrist devices were accurate enough for both HRV analysis and to differentiate between sinus rhythm and atrial fibrillation cases. 17 Standards for HRV were set by the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. 18 Benchmark HRV values from 24 h measurements have been published.…”

Section: Introductionmentioning

confidence: 99%

Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study

Natarajan

Pantelopoulos

Emir-Farinas

et al. 2020

The Lancet Digital Health

131

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Background Heart rate variability, or the variation in the time interval between consecutive heart beats, is a non-invasive dynamic metric of the autonomic nervous system and an independent risk factor for cardiovascular death. Consumer wrist-worn tracking devices using photoplethysmography, such as Fitbit, now provide the unique potential of continuously measuring surrogates of sympathetic and parasympathetic nervous system activity through the analysis of interbeat intervals. We aimed to leverage wrist-worn trackers to derive and describe diverse measures of cardiac autonomic function among Fitbit device users. Methods In this cross-sectional study, we collected interbeat interval data that are sent to a central database from Fitbit devices during a randomly selected 24 h period. Age, sex, body-mass index, and steps per day in the 90 days preceding the measurement were extracted. Interbeat interval data were cleaned and heart rate variability features were computed. We analysed heart rate variability metrics across the time (measured via the root mean square of successive RR interval differences [RMSSD] and SD of the RR interval [SDRR]), frequency (measured by high-frequency and low-frequency power), and graphical (measured by Poincare plots) domains. We considered 5 min windows for the time and frequency domain metrics and 60 min measurements for graphical domain metrics. Data from participants were analysed to establish the correlation between heart rate variability metrics and age, sex, time of day, and physical activity. We also determined benchmarks for heart rate variability (HRV) metrics among the users. Findings We included data from 8 203 261 Fitbit users, collected on Sept 1, 2018. HRV metrics decrease with age, and parasympathetic function declines faster than sympathetic function. We observe a strong diurnal variation in the heart rate variability. SDRR, low-frequency power, and Poincare S 2 show a significant variation with sex, whereas such a difference is not seen with RMSSD, high-frequency power, and Poincare S 1. For males, when measured from 0600 h to 0700 h, the mean low-frequency power decreased by a factor of 66•5% and high-frequency power decreased by a factor of 82•0% from the age of 20 years to 60 years. For females, the equivalent factors were 69•3% and 80•9%, respectively. Comparing low-frequency power between males and females at the ages of 40-41 years, measured from 0600 h to 0700 h, we found excess power in males, with a Cohen's d effect size of 0•33. For high-frequency power, the equivalent effect size was-0•04. Increased daily physical activity, across age and sex, was highly correlated with improvement in diverse measures of heart rate variability in a dose-dependent manner. We provide benchmark tables for RMSSD, SDRR, high and low frequency powers, and Poincare S 1 and S 2 , separately for different ages and sex and computed at two times of the day. Interpretation Diverse metrics of cardiac autonomic health can be derived from wrist-worn trackers. Empirical distributions of hea...

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“…During the experiment, heart rate was measured to obtain data about the variable stress (Gaffey and Wirth, 2014; Sacha, 2014). The Fitbit Charge 4 makes use of a photoplethysmography technology which is supported to be an accurate alternative to the electrocardiogram (Allen, 2007; Singh et al , 2018; Tarniceriu et al , 2018). Heart rate measurement took place four times each session: before entering the condition to set a baseline measure, after exposure and adaptation to the condition, after the task given where a stressful situation was created and after filling in the questionnaire.…”

Section: Methodsmentioning

confidence: 99%

The effect of three different volumes of indoor greenery on employee well-being

Elbertse

Steenbekkers

2023

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Purpose This study aims to explore the effect of different volumes of indoor greenery on perceived stress, stress, perceived productivity, productivity and perceived workplace satisfaction to support employees’ well-being. Design/methodology/approach In a cross-over experimental design, a homogeneous group of students was randomised in different orders where each participant experienced the same three conditions. Different volumes of indoor greenery were added to the experiment room (0%, 0.5% and 8%). With a Fitbit, sudoku and a questionnaire, the five variables were measured. Findings Findings show that perceived stress and heart rate (physical stress) are lower in the 8% condition. Productivity scores did not improve significantly, however perceived productivity did in the 8% condition. Positive trends could be seen for the variables in the 0.5% condition. Furthermore, 2/3 of the participants preferred the 8% condition, whereas the other 1/3 preferred the 0.5% condition. Overall, 1/3 of the participants mentioned that their optimal volume of greenery would be more than 0.5% but less than 8%. This research shows that the implementation of 8% indoor greenery contributes to a happier and more relaxing office place where employees get the feeling of being less stressed and more productive. Research limitations/implications This research found that the largest volume of plants used (8%) has the most positive effect on the variables studied in this research. This study shows that participants’ heart rate, and therefore physical stress, became lower in the 8% condition. In this 8% condition, participants experienced less stress and perceived themselves to be more productive. Besides, 2/3 of the participants preferred being in the 8% condition, suggesting that after adding this volume of indoor greenery employees will be more satisfied with their workplace. Practical implications Where previous research discovered that indoor greenery has a positive effect on variables like (perceived) stress, (perceived) productivity and perceived workplace satisfaction, this research also provides support for the effect of different volumes of indoor greenery. Results obtained by the spatial coverage ratio approach can be easily applied to future research and practice. Social implications Indoor greenery can contribute to making the office a happier and more relaxing place where employees get the feeling of being less stressed and more productive which in the long term might contribute to the overall well-being of employees. Originality/value To the best of the authors’ knowledge, this is the first study focusing on the effect of different volumes of indoor greenery on these five variables.

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Detection of beat-to-beat intervals from wrist photoplethysmography in patients with sinus rhythm and atrial fibrillation after surgery

Cited by 14 publications

References 12 publications

Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography

Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography

Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study

The effect of three different volumes of indoor greenery on employee well-being

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