scholarly journals Influence of Hypoxia and Hypercapnia on Sleep State-Dependent Heart Rate Variability Behavior in Newborn Lambs

SLEEP ◽  
2012 ◽  
Author(s):  
Alain Beuchée ◽  
Alfredo I. Hernández ◽  
Charles Duvareille ◽  
David Daniel ◽  
Nathalie Samson ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep State ◽  
State Dependent

249 Overnight heart rate variability depends on age, gender, and day of the week: a field observation using a smart bed platform

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A100-A101
Author(s):  
Shawn Barr ◽  
Kwanghyun Sohn ◽  
Gary Garcia
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Physiological Stress ◽  
Large Population ◽  
Population Level ◽  
Large Set ◽  
Sleep State ◽  
Cross Sectional ◽  
Day Of The Week ◽  
Broad Interest

Abstract Introduction Heart rate variability (HRV) is commonly used to assess the activity of the autonomic nervous system (ANS). ANS function changes, reflected in HRV, result from factors including lifestyle, aging, cardiorespiratory illnesses, sleep state, and physiological stress. Despite broad interest in HRV, few studies have established normative overnight HRV values for a large population. To better understand population level HRV changes, ecologically-valid, overnight sleep SDNN (standard deviation of all normal heartbeat intervals, lower HRV is reflected by lower SDNN) values have been analyzed for a large sample of Sleep Number 360 smart bed users. Methods Overnight SDNN values were obtained over the course of 18.2M sleep sessions from 379,225 sleepers (48 ± 14.7 sessions/user). 50.9 percent of sleepers were female. The age was normally distributed with mean ± SD of 52.8 ± 12.7 years (range 21 to 84). Heartbeat intervals used to compute SDNN were extracted from a ballistocardiogram (BCG). BCG-based HRV estimation during sleep has previously been validated against ECG-based HRV with an R-square of 0.5. Results Using a Generalized Linear Model, significant cross-sectional associations with SDNN were observed for three variables of interest: age, gender, and day-of-the-week. For sleepers under 50, SDNN declined at a rate of about 2.1 ms/year, then leveled off for sleepers aged 50-65, and increased slightly thereafter. Women under 50 displayed lower, more slowly declining, SDNN values than men, but this trend reversed for sleepers over 50. Throughout the week, SDNN values followed a U-shaped (women) or L-shaped (men) pattern, where values were highest during the weekend and lowest at mid-week. Conclusion Using a smart bed to unobtrusively measure overnight SDNN values for a large set of sleepers in an ecologically valid environment, reveals significant effects of age, gender, and day of the week on overnight SDNN. Support (if any):

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

scholarly journals Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring

2017 ◽  
Vol 113 ◽  
pp. 104-113 ◽  
Author(s):  
Jan Werth ◽  
Xi Long ◽  
Elly Zwartkruis-Pelgrim ◽  
Hendrik Niemarkt ◽  
Wei Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Patient Monitoring ◽  
Sleep State

scholarly journals Time- and state-dependent analysis of autonomic control in narcolepsy: higher heart rate with normal heart rate variability independent of sleep fragmentation

2014 ◽  
Vol 24 (2) ◽  
pp. 206-214 ◽  
Author(s):  
Wisse P. van der Meijden ◽  
Rolf Fronczek ◽  
Robert H. A. M. Reijntjes ◽  
Eleonora P. M. Corssmit ◽  
Nienke R. Biermasz ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Autonomic Control ◽  
Sleep Fragmentation ◽  
Normal Heart ◽  
State Dependent ◽  
Normal Heart Rate

Time- and state-dependent analysis of autonomic control in narcolepsy: higher heart rate with normal heart rate variability

2013 ◽  
Vol 14 ◽  
pp. e205
Author(s):  
W. Van Der Meijden ◽  
R. Fronczek ◽  
R. Reijntjes ◽  
G. Lammers ◽  
G. Van Dijk ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Autonomic Control ◽  
Normal Heart ◽  
State Dependent ◽  
Normal Heart Rate

scholarly journals Analysis on power spectrum and base-scale entropy for heart rate variability signals modulated by reversed sleep state

2014 ◽  
Vol 63 (19) ◽  
pp. 198703
Author(s):  
Liu Da-Zhao ◽  
Wang Jun ◽  
Li Jin ◽  
Li Yu ◽  
Xu Wen-Min ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Power Spectrum ◽  
Sleep State

Differentiation of Physiological States under Sensory Deprivation

2000 ◽  
Vol 39 (02) ◽  
pp. 168-170 ◽  
Author(s):  
M. Nakao ◽  
M. Yamamoto ◽  
M. Kimura ◽  
K. Iwata
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Eye Movement ◽  
Rem Sleep ◽  
Physiological State ◽  
Sensory Deprivation ◽  
Rapid Eye Movement ◽  
State Dependent ◽  
Normal Sleep ◽  
Electroencephalogram Eeg

Abstract:We attempt to differentiate the physiological state during sensory deprivation (SD) from normal sleep and wakefulness in terms of electroencephalogram (EEG) and heart rate variability (HRV) dynamics. KullbackLeibler (K-L) divergence is employed to quantify differences between their state-dependent dynamics. As a result, the dynamics of EEG and HRV during SD are found to be far distant from any representative dynamics of natural states of sleep and wakefulness. However, relatively speaking, the findings in SD can be categorized into two patterns. (a) The dynamics of HRV during SD is similar to that of rapid eye movement (REM) sleep, and the dynamics of EEG during SD is similar to that of wakefulness. (b) The dynamics of both HRV and EEG during SD are similar to that of REM. Such dissociation between states classified by EEG and HRV dynamics frequently takes place during SD. These findings suggest the peculiarity of the physiological state during the SD distinct from sleep and wakefulness.

scholarly journals Sleep in the Intensive Care Unit through the Lens of Breathing and Heart Rate Variability: A Cross-Sectional Study

2021 ◽  
Author(s):  
Wolfgang Ganglberger ◽  
Parimala Velpula Krishnamurthy ◽  
Syed A. Quadri ◽  
Ryan A. Tesh ◽  
Abigail A. Bucklin ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Adult Patients ◽  
Sleep Stages ◽  
Sleep Laboratory ◽  
Sleep State ◽  
Sleep Physiology ◽  
Medical Icus

Background. Full polysomnography, the gold standard of sleep measurement, is impractical for widespread use in the intensive care unit (ICU). Wrist-worn actigraphy and subjective sleep assessments do not measure sleep physiology adequately. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods. Methods. We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in covariate-matched sleep laboratory patients. We analyzed the agreement of the determined sleep stages between the HRV- and breathing-based models, computed sleep indices, and quantified breathing variables during sleep. Results. We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p<0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median = 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median = 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients. Conclusions. Cardiovascular and respiratory signals encode sleep state information, which can be utilized to measure sleep state in the ICU. Using these easily measurable variables can provide automated information about sleep in the ICU.

Sign in / Sign up

Export Citation Format

Share Document