Suitability of Ultra-Short-Term Heart Rate Variability in Military Trainees

Alalyan, Mubarak J; Alkahtani, Shaea; Habib, Syed Shahid; Flatt, Andrew A.

doi:10.3390/healthcare8040409

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…Despite the benefits of HRV testing in health care, the 5-min recording time is impractically long compared with other clinical tests, such as blood pressure measurement and fasting blood sugar, which can be routinely performed in a primary care center or at home. Interest in shorter measurement times prompted researchers to investigate in ultrashort-term HRV test to assess its reliability against short-term tests [18–20]. Previous studies that tested the minimum testing time showed a high correlation between ultra-and short-term analyses [7–9].…”

Section: Discussionmentioning

confidence: 99%

Shortening heart rate variability measurement time to 1 minute using deep learning: Implication of real-time measurement of heart rate variability

Shin,

Lee,

Jeong

et al. 2023

Preprint

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Heart rate variability (HRV) is an effective predictor of cardiovascular diseases. The current standard 5-min recording time is lengthy compared with routine clinical examinations such as blood pressure measurement. Previous studies have observed that the indices of 3-min HRV data are as clinically meaningful as those of 5-min HRV data; however, shorter durations are considered unreliable, and there have been no attempts to challenge this notion. This study aimed to validate the outcomes of 1-min HRV recordings reconstructed using deep learning algorithms. Three-minute HRV recordings from 34,885 participants were included in the analysis. Of the recordings, 60% (20,931), 30% (10,465), and 10% (3,489) were allocated to the training, validation, and test sets, respectively. Data from 1-min excerpts of the 3-min recordings were used as the input for the deep learning models to predict the data of the 3-min recordings. Various deep learning models were applied to each indicator, and the model that produced the lowest mean absolute error was selected as that particular indicator’s learning model. There was no statistical difference between the values of the 1-min recordings reconstructed by deep learning and those of the 3-min recordings. The 1-min recordings reconstructed by deep learning demonstrated a higher correlation with the 3-min recordings when compared with the 1-min recordings that were not processed by deep learning. They also strongly agreed with the 3-min recordings in the Bland–Altman analysis. The 1-min HRV recordings reconstructed by deep learning were as reliable as the 3-min HRV recordings, suggesting that a 1-min recording could serve as a proxy for real-time HRV monitoring in the future. Keywords: heart rate variability; deep learning; Bland-Altman analysis; ultrashort-term; one minute HRV

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Section: Discussionmentioning

confidence: 99%

Shortening heart rate variability measurement time to 1 minute using deep learning: Implication of real-time measurement of heart rate variability

Shin,

Lee,

Jeong

et al. 2023

Preprint

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“…The use of ultra-short time sequences was investigated in several studies that compared the analysis of ultra-short time measurements with short time measurements demonstrating high correlation between measurements ( 63 – 65 ). Baek et al ( 66 ) investigated the reliability of this method and concluded that ultra-short time analysis is reliable and comparable to the standard 5-min interval analysis.…”

Section: Methodsmentioning

confidence: 99%

Autonomic changes as reaction to experimental social stress in an inpatient psychosomatic cohort

et al. 2022

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ObjectivesPatients with psychosomatic disorders suffer from social isolation that might further lead to destabilization and exacerbation of bodily symptoms via autonomic pathways. We aimed to investigate the influence of controlled social stress (model of social ostracism) on the autonomic nerve system (ANS) in an inpatient cohort with psychosomatic disorders.MethodsWe examined heart rate variability (HRV), skin conductance (SC) and skin temperature (ST) as well as ECG-derived respiration rate (EDR) and subjective reports on stress during exposure to experimental social stress (cyberball game). Data were collected from 123 participants (f:m = 88:35, 42.01 ± 13.54 years) on admission and upon discharge from the university psychosomatic clinic. All data were recorded during baseline, inclusion and exclusion phases of the cyberball game as well as during the recovery phase.ResultsWe found significant changes between admission and discharge with a decline in parasympathetic-related HRV parameters (SDRR −3.20 ± 1.30 ms, p = 0.026; RMSSD: −3.77 ± 1.28 ms, p = 0.007) as well as a decrease in SC (−0.04 ± 0.17 μS, p = 0.019) and EDR (−0.01 ± 0.01 Hz, p = 0.007), suggesting a drop in sympathetic tonus, with no changes in ST (p = 0.089) and subjective stress levels (p = 0.322). HRV parameters decreased during the cyberball game (SDRR p = 0.026; RMSSD p = 0.002; lnHF p < 0.001). In contrast, both SC (p < 0.001) and EDR (p < 0.001) increased during the game with SC being slightly lower during the exclusion phase. This can point toward a stimulation of sympathetic nervous system during game participation, which was concordant with the rise in subjective stress values (p < 0.001). ST showed a continuous, unspecific rise over time (p < 0.001).ConclusionOur data demonstrate the decrease of ANS parameters during experimental social stress when data upon discharge were compared to those upon admission. These results are partially contradictory to previous studies that showed a rise in HRV in a psychiatric cohort over the course of (outpatient) treatment. Further research is required to help attributing these differences to effects of treatment or acute states relating to admission to or discharge from a psychosomatic department.

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“…HRV metrics were obtained as previously described [ 26 , 27 ]. Three electrodes were placed in a modified lead II configuration with the ground electrode placed at the midpoint of the left clavicle.…”

Section: Methodsmentioning

confidence: 99%

“…From the 15 min recording, a 5 min segment with minimal ectopic beats and artifacts was selected for HRV analysis. Filtering of RR intervals for artifacts and ectopic beats was completed using customized software (LabChart v. 8.1.13 Windows, ADInstruments Pty Ltd., New South Wales, Australia) as previously described [ 26 ]. Time-domain parameters included the mean RR interval (reflects resting heart rate), standard deviation of normal-to-normal RR intervals (SDNN, reflects global variability), and root mean square of successive differences in RR intervals (RMSSD, reflects cardio-vagal control) [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

Heart Rate Variability and Chronotype in Young Adult Men

et al. 2022

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Whether morning heart rate variability (HRV) predicts the magnitude of its circadian variation in the absence of disease or is influenced by chronotype is unclear. We aimed to quantify associations between (1) morning HRV and its diurnal change, and (2) morning HRV and a Morningness–Eveningness Questionnaire (MEQ)-derived chronotype. Resting electrocardiograms were obtained in the morning and evening on separate days in a counterbalanced order to determine the mean RR interval, root mean square of successive differences (RMSSD), and standard deviation of normal-to-normal RR intervals (SDNN) in 23 healthy men (24.6 ± 3.4 yrs; body mass index: 25.3 ± 2.8 kg/m2). The MEQ was completed during the first laboratory visit. Morning RMSSD and SDNN were significantly higher (Ps < 0.05) than evening values. Morning RMSSD and SDNN were associated with their absolute (Ps < 0.0001), and relative diurnal changes (Ps < 0.01). No associations were observed between HRV parameters and the MEQ chronotypes (Ps > 0.09). Morning HRV was a stronger determinant of its evening change than chronotype. Greater diurnal variation in HRV was dependent on higher morning values. Strategies to improve basal HRV may therefore support healthier cardio-autonomic circadian profiles in healthy young men.

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Suitability of Ultra-Short-Term Heart Rate Variability in Military Trainees

Cited by 8 publications

References 35 publications

Shortening heart rate variability measurement time to 1 minute using deep learning: Implication of real-time measurement of heart rate variability

Shortening heart rate variability measurement time to 1 minute using deep learning: Implication of real-time measurement of heart rate variability

Autonomic changes as reaction to experimental social stress in an inpatient psychosomatic cohort

Heart Rate Variability and Chronotype in Young Adult Men

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