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

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

Modulation of Neurocardiac Function by Oesophageal Stimulation in Humans

1997 ◽  
Vol 92 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Gervais Tougas ◽  
Markad Kamath ◽  
Geena Watteel ◽  
Debbie Fitzpatrick ◽  
Ernest L. Fallen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Power Spectrum ◽  
High Frequency ◽  
Low Frequency ◽  
Sensory Stimulation ◽  
Sensory Stimuli ◽  
Autonomic Reflexes ◽  
Low Frequency Power ◽  
Beat Heart

1. The heart and the oesophagus have similar sensory pathways, and sensations originating from the oesophagus are often difficult to differentiate from those of cardiac origin. We hypothesized that oesophageal sensory stimuli could alter neurocardiac function through autonomic reflexes elicited by these oesophageal stimuli. In the present study, we examined the neurocardiac response to oesophageal stimulation and the effects of electrical and mechanical oesophageal stimulation on the power spectrum of beat-to-beat heart rate variability in male volunteers. 2. In 14 healthy volunteers, beat-to-beat heart rate variability was compared at rest and during oesophageal stimulation, using either electrical (200 μs, 16 mA, 0.2 Hz) or mechanical (0.5 s, 14 ml, 0.2 Hz) stimuli. The power spectrum of beat-to-beat heart rate variability was obtained and its low- and high-frequency components were determined. 3. Distal oesophageal stimulation decreased heart rate slightly (both electrical and mechanical) (P < 0.005), and markedly altered heart rate variability (P < 0.001). Both electrical and mechanical oesophageal stimulation increased the absolute and normalized area of the high-frequency band within the power spectrum (P < 0.001), while simultaneously decreasing the low-frequency power (P < 0.005). 4. In humans, oesophageal stimulation, whether electrical or mechanical, appears to amplify respiratory-driven cardiac vagoafferent modulation while decreasing sympathetic modulation. The technique provides access to vagoafferent fibres and thus may yield useful information on the autonomic effects of visceral or oesophageal sensory stimulation.

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):

Body position affects the power spectrum of heart rate variability during dynamic exercise

1993 ◽  
Vol 66 (3) ◽  
pp. 207-213 ◽  
Author(s):  
Renza Perini ◽  
Claudio Orizio ◽  
Stefania Milesi ◽  
Luca Biancardi ◽  
Giuseppe Baselli ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Power Spectrum ◽  
Body Position ◽  
Dynamic Exercise

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
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