A Critical Review of Ultra-Short-Term Heart Rate Variability Norms Research

Shaffer, Fred; Meehan, Zachary M.; Zerr, C

doi:10.3389/fnins.2020.594880

Cited by 135 publications

(103 citation statements)

References 80 publications

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“…They showed that a 60-s interval is enough time to approximate most HRV parameters. Other studies [ 44 , 45 , 46 ] also demonstrated that ultra-short-term HRV analysis might be an effective surrogate method.…”

Section: Introductionmentioning

confidence: 99%

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

You

Cho

et al. 2021

Medicina

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Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey–Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.

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Section: Introductionmentioning

confidence: 99%

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

You

Cho

et al. 2021

Medicina

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“…Equation (8) illustrates that the PDS envelope of ECG signal is controlled by the spectrum, ( )while the detailed changes in the PDS are associated with the RRIs statistics under various conditions. In Fig.…”

Section: B Power Spectral Analysis Of Clinical Ecg Signalsmentioning

confidence: 99%

“…Hence, HRV is widely used as a prognostic factor for MI risk stratification and management [5][6] [7]. Due to the need for monitoring individual's health and well-being status, the interests in ultra-short-term HRV analysis with its applications in wearable devices and mobile health technology are significantly increasing recently [8][9] [10].…”

Section: Introductionmentioning

confidence: 99%

Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals

Xiang

Clifton

et al. 2021

IEEE J. Biomed. Health Inform.

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Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 ± 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultrashort-term ECG recordings suitable for wearable and mobile health applications in living-free environments.

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“…These metrics were calculated using 30 and 60 seconds sliding windows at 1 second increments. Previous studies have investigated the reliability of various UST HRV features [17,3,18,19,20]. While these studies looked at individual feature correlations between short-term and ultra-short-term metrics, we are interested in whether these features can predict workload.…”

Section: Hrv Metricsmentioning

confidence: 99%

The Impact of Control Interface on Features of Heart Rate Variability

Javaremi¹,

Wu²,

Argall³

2021

Preprint

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Shared human-robot control for assistive machines can improve the independence of individuals with motor impairments. Monitoring elevated levels of workload can enable the assistive autonomy to adjust the control-sharing in an assist-as-needed way, to achieve a balance between user fatigue, stress and independent control. In this work, we aim to investigate how heart-rate variability features can be utilized to monitor elevated levels of mental workload while operating a powered wheelchair, and how that utilization might vary under different control interfaces. To that end, we conducted a 22 person study with three commercial interfaces. Our results show that the validity and reliability of using the ultra-short-term heart-rate variability features as predictors for workload indeed are affected by the type of interface in use.

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A Critical Review of Ultra-Short-Term Heart Rate Variability Norms Research

Cited by 135 publications

References 80 publications

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals

The Impact of Control Interface on Features of Heart Rate Variability

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