A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak, Suraj Kumar; Pradhan, Bikash; Mohanty, Biswaranjan; Sivaraman, Jayaraman; Ray, Sirsendu Sekhar; Wawrzyniak, Jolanta; Jarzębski, Maciej; Pal, Kunal

doi:10.3390/a16090433

Cited by 8 publications

(2 citation statements)

References 179 publications

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“…Recent studies have focused on the analysis of HR variability (HRV). By analyzing HR data measured by wearable sensors and extracting signals and features from PPG or ECG, these studies have become valuable screening or diagnostic tools in various clinical specialties, such as screening for diabetes, myocardial infarction, and sleep apnea [28]. However, it is crucial to acknowledge that the reliability of HRV information relies on the integrity of the original HR data.…”

Section: Discussionmentioning

confidence: 99%

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

Xia,

Wung,

Chen

et al. 2024

Sensors

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The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects’ diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

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Section: Discussionmentioning

confidence: 99%

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

Xia,

Wung,

Chen

et al. 2024

Sensors

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“…Various time and frequency domain indices are used to analyze the HRV. An overview of HRV metrics is detailed in a number of reviews [ 3 , 4 , 5 , 6 , 7 ]. It uses time domain indices such as the standard deviation of normal-to-normal (NN) intervals (the time elapsing between two consecutive R waves in the ECG with normal sinus rhythm) (abbreviated as SDNN), the percentage of consecutive NN intervals differing by more than 50 ms (PNN50), the square root of the mean sum of squared differences between adjacent NN intervals (RMSSD), etc.…”

Section: Introductionmentioning

confidence: 99%

Full-Term and Preterm Newborns Differ More Significantly in Photoplethysmographic Waveform Variability than Heart Rate Variability

Kiselev,

Mureeva,

Skazkina

et al. 2024

Life

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Background: Features of cardiovascular autonomic regulation in infants are poorly studied compared with adults. However, the clinical significance of autonomic dysfunction in infants is very high. The goal of our research was to study the temporal and frequency-dependent features, as well as low-frequency synchronization in cardiovascular autonomic regulation in full-term vs. preterm newborns, based on the analysis of their heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV). Methods: The study included three groups of newborns: 64 full-term newborns (with a gestational age at birth of 37–40 weeks) with a physiological course of the neonatal adaptation; 23 full-term newborns (with a gestational age at birth of 37–40 weeks) with a pathological course of the neonatal adaptation; and 17 preterm newborns (with a postconceptional age of 34 weeks or more). We conducted spectral analysis of HRV and PPGV, along with an assessment of the synchronization strength between low-frequency oscillations in HRV and in PPGV (synchronization index). We employed several options for the boundaries of the high-frequency (HF) band: 0.15–0.40 Hz, 0.2–2 Hz, 0.15–0.8 Hz, and 0.24–1.04 Hz. Results: Preterm newborns had higher heart rate, RMSSD, and PNN50 values relative to both groups of full-term newborns. Values of SDNN index and synchronization index (S index) were similar in all groups of newborns. Differences in frequency domain indices of HRV between groups of newborns depended on the considered options of HF band boundaries. Values of frequency domain indices of PPGV demonstrated similar differences between groups, regardless of the boundaries of considered options of HF bands and the location of PPG signal recording (forehead or leg). An increase in sympathetic influences on peripheral blood flow and a decrease in respiratory influences were observed along the following gradient: healthy full-term newborns → preterm newborns → full-term newborns with pathology. Conclusions: Differences in frequency domain indices of autonomic regulation between the studied groups of newborns depended on the boundaries of the considered options of the HF band. Frequency domain indices of PPGV revealed significantly more pronounced differences between groups of newborns than analogous HRV indicators. An increase in sympathetic influences on peripheral blood flow and a decrease in respiratory influences were observed along the following gradient: healthy full-term newborns → preterm newborns → full-term newborns with pathology.

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The autonomous nervous system and the cholinergic anti-inflammatory reflex in postoperative neurocognitive disorders

Lammers-Lietz,

Spies,

Maggioni

2024

Current Opinion in Anaesthesiology

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Purpose of review Postoperative delirium (POD) is a common and serious complication after surgery. It is associated with postoperative neurocognitive disorder (PNCD). The vagal cholinergic anti-inflammatory pathway (CAP) has been hypothesized to play a role in POD/PNCD and may be a target for interventions such as transcutaneous auricular stimulation (taVNS). We aim to review associations of heart rate variability (HRV) as an indicator of vagal function with POD and postoperative immune reaction as well as taVNS as a potential preventive intervention for POD. Recent findings Autonomous nervous system (ANS) dysfunction was a common finding in studies analysing HRV in POD and postoperative cognitive dysfunction, but results were heterogeneous. There was no evidence from HRV analysis that vagal activity prevents overshooting postoperative immune activation, but HRV may help to identify patients at risk for postoperative infections. Animal studies and preliminary trials suggest that taVNS may be used to prevent POD/PNCD. Summary Our review provides no evidence that CAP suppression is associated with POD/PNCD. Future studies should consider that high vagal tone may also mediate immunosuppression in surgical patients, yielding an increased risk for postoperative infections. Although taVNS is a promising approach to prevent POD/POCD, future studies should take these concerns into account.

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A Review of Methods and Applications for a Heart Rate Variability Analysis

Cited by 8 publications

References 179 publications

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

Full-Term and Preterm Newborns Differ More Significantly in Photoplethysmographic Waveform Variability than Heart Rate Variability

The autonomous nervous system and the cholinergic anti-inflammatory reflex in postoperative neurocognitive disorders

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