Heart Rate Dynamics and their Relation with the Cyclic Alternating Pattern of Sleep in Normal Subjects and NFLE Patients

2017 ◽  
Vol 16 (02) ◽  
pp. 1750012 ◽  
Author(s):  
Jose S. González ◽  
Guadalupe Dorantes ◽  
Alfonso Alba ◽  
Martin O. Méndez ◽  
Sergio Camacho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Healthy Subjects ◽  
Parametric Bootstrap ◽  
Nrem Sleep ◽  
Normal Subjects ◽  
Sleep Time ◽  
Cyclic Alternating Pattern ◽  
Heart Rate Dynamics ◽  
Two Populations

The aim of this work is to study the behavior of the autonomic system through variations in the heart rate (HR) during the Cyclic Alternating Pattern (CAP) which is formed by A-phases. The analysis was carried out in 10 healthy subjects and 10 patients with Nocturnal Front Lobe Epilepsy (NFLE) that underwent one whole night of polysomnographic recordings. In order to assess the relation of A-phases with the cardiovascular system, two time domain features were computed: the amplitude reduction and time delay of the minimum of the R-R intervals with respect to A-phases onset. In addition, the same process was performed over randomly chosen R-R interval segments during the NREM sleep for baseline comparisons. A non-parametric bootstrap procedure was used to test differences of the kurtosis values of two populations. The results suggest that the onset of the A-phases is correlated with a significant increase of the HR that peaks at around 4[Formula: see text]s after the A-phase onset, independently of the A-phase subtype and sleep time for both healthy subjects and NFLE patients. Furthermore, the behavior of the reduction in the R-R intervals during the A-phases was significantly different for NFLE patients with respect to control subjects.

250 AI-Supported Sleep Staging from Activity and Heart Rate

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Class Imbalance ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Temporal Correlations ◽  
Stage Classification ◽  
Small Form Factor

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

scholarly journals Analysis of Heart Rate Dynamics Before and During Meditation

2021 ◽  
Vol 17 (05) ◽  
pp. 100
Author(s):  
Mohammad Karimi Moridani ◽  
Tina Habikazemi ◽  
Nahid Khoramabadi
Keyword(s):  
Heart Rate ◽  
Vital Signs ◽  
Normal Subjects ◽  
Support Vector ◽  
Nonlinear Characteristics ◽  
Rate Analysis ◽  
High Tension ◽  
Heart Rate Dynamics ◽  
Heart Rate Analysis ◽  
Sensitivity Specificity

<p>Heart rate is one of the most important vital signs. People usually face high tension in routine life, and if we found an effective method to control the heart rate, it would be very desirable. One of the goals of this paper is to examine changes in heart rate before and during meditation. Another goal is that what impact could have meditation on the human heartbeat.</p><p>To heart rate analysis before and during meditation, available heart rate signals have been used for the Physionet database that contains 10 normal subjects and 8 subjects that meditation practice has been done on them. In this paper, first is paid to extract linear and nonlinear characteristics of heart rate and then is paid to the best combination of features to identify two intervals before and during meditation using MLP and SVM classifiers with the help of sensitivity, specificity and accuracy measurements.</p><p>The achieved results in this paper showed that choosing the best combination of a feature to make a meaningful difference between two intervals before and during meditation includes two-time features (Mean HR, SDNN), a frequency feature ( ), and three nonlinear characteristics   ( ). Also, using the support vector machine had better results than the MLP neural network. The sensitivity, specificity, and accuracy of the mean and standard deviation obtained respectively like 92.73  0.23, 89.05 0.67, 89.97 0.23 by using MLP and respectively like 95.96 0.09, 93.80 0.16, and 94.90 0.14 by using SVM.</p>As a result, using meditation can reduce the stress and anxiety of patients by effects on heart rate, and the treatment process speeds up and have an important role in improving the performance of the system.

scholarly journals Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenbin Ma ◽  
Haoran Xu ◽  
Muyang Yan ◽  
Jie Huang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
Healthy Subjects ◽  
Multiscale Entropy ◽  
Healthy Population ◽  
Poincaré Plot ◽  
Longitudinal Changes ◽  
Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p &lt; 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p &lt; 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p &lt; 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p &lt; 0.01).Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

scholarly journals P1748 Left atrial diastolic and systolic functions modulate the response to the standardised Valsalva maneuver in normal subjects

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
R Musci ◽  
G Teodone ◽  
P Pollice ◽  
A I Guaricci ◽  
P Barbier
Keyword(s):  
Heart Rate ◽  
Healthy Subjects ◽  
Left Atrial ◽  
Valsalva Maneuver ◽  
Velocity Ratio ◽  
Normal Subjects ◽  
Left Ventricular ◽  
Volume Index ◽  
Published Data ◽  
Minimum Volume

Abstract Background Although the Valsalva maneuver (VM) is being advocated by current guidelines to identify with echocardiography patients with increased left ventricular (LV) filling pressures using a decrease in mitral E/A velocity &gt; 0.5 as cutoff, there are limited published data for both patients and the normal response to the maneuver in healthy subjects. Purpose To assess LV and left atrial (LA) physiology during a standardized VM (VMs) in normal subjects. Methods The VMs was performed in 50 healthy subjects (M:F 38:12; age 40 ± 12 y.; HR 70 ± 11 bpm; BSA 1.81 ± 11 m2), instructed to forcefully exhale for 20 seconds without an initial deep breath into a tube connected to a sphygmomanometer, maintaining a 25-35 mmHg pressure. The VM was repeated 2 times at 5 minute intervals to record sequentially in the apical 4-chamber view: 1. LV and LA volumes; 2. Transmitral flow velocities. LA diastolic reservoir function (LAres) was calculated as: (maximum – minimum volume) / minimum volume x 100. Results. During the VMs, in all subjects LV indexed end-diastolic (-14 ± 7 ml/m2, -31 ± 15 %) and end-systolic (-6 ± 4 ml/m2, -31 ± 18 %) volumes, and stroke volume index (-9 ± 5 ml/m2, -30 ± 15 %) decreased similarly with unchanged LV ejection fraction %, and LA maximum and minimum volume indices both decreased (respectively -8 ± 6 ml/m2, -3 ± 3 ml/m2;-32 ± 25 %) with high variability. Mitral peak E velocity also decreased (-22 ± 13 cm/s, -27 ± 14 %) in all subjects, whereas peak A velocity change varied, such that a "pseudo-abnormal" decrease of E/A &gt; 0.5 was seen in 18 subjects (37 %). At baseline, this subgroup had lower heart rate (66 ± 11 vs 73 ± 10 bpm, p= .026), higher LAres (193 ± 67 vs 145 ± 47 %, p= .006), lower peak A velocity (50 ± 12 vs 58 ± 12 cm/s, p= .04) and higher E/A (1.8±.6 vs 1.4±.3, p= .004). During VMs, LV and LA volumes decreased similarly in all subjects, but increase in heart rate was higher (12 ± 8 vs 6 ± 5 bpm, p= .023), and peak A wave increased instead of decreasing (20 ± 20 % vs -8 ± 18 %, p&lt; .001) in the subjects with "pseudo-abnormal" decrease of E/A. During VMs, decrease in E/A was mainly determined (regression analysis, r: .76, p= .029) by baseline LAres (B= -.71) and change in LAres during VMs (B= -.47), whereas an increase in peak A velocity (r: .46, p= .031) was mainly determined by degree of HR increase (B= .41) and baseline LV EF (B= .3). Conclusions During VMs, a "pseudo-abnormal" decrease of the E/A velocity ratio is present in almost 40 % of normal subjects, and is determined by the interplay of the baseline diastolic compliance and the increase in systolic function of the LA during VM. These results may influence the accuracy of the VMs in the detection of increased LV filling pressures in patients.

Circadian aspects of apparent correlation dimension in human heart rate dynamics

1995 ◽  
Vol 269 (1) ◽  
pp. H130-H134 ◽  
Author(s):  
P. Van Leeuwen ◽  
H. Bettermann ◽  
U. An der Heiden ◽  
H. C. Kummell
Keyword(s):  
Heart Rate ◽  
Healthy Subjects ◽  
Human Heart ◽  
Holter Monitoring ◽  
Cardiac Dynamics ◽  
Dimension Analysis ◽  
Heart Rate Dynamics ◽  
Apparent Correlation ◽  
The Difference ◽  
Heart Rate Complexity

The purpose of this study was to examine changes in complexity of cardiac dynamics over 24 h. With use of Holter monitoring, 27 24-h electrocardiogram recordings were obtained from 15 healthy subjects. For each recording, the apparent dimension (DA) was calculated for consecutive sections of 500 heartbeats. These were used to determine nighttime and daytime dimension (D(An) and D(Ad), respectively) as well as the difference between D(An) and D(Ad) (delta DA). Mean 24-h DA, D(An), and D(Ad) were 5.9 +/- 0.3, 6.3 +/- 0.5, and 5.6 +/- 0.6, respectively. D(An) was significantly higher than D(Ad) (P < 0.001), with a mean delta DA of 0.6 +/- 0.7. Furthermore, 67% of delta DA values were significantly different from zero at the 0.05 level. The results show that dimension analysis may be applied to heart rate dynamics to reveal circadian differences of heart rate complexity. We suggest that the decreased complexity during daytime may result from the synchronization of physiological functions. The increase in complexity at night would then correspond to an uncoupling of these functions during the regenerative period.

scholarly journals Association of long-term exposure to traffic-related PM10 with heart rate variability and heart rate dynamics in healthy subjects

2019 ◽  
Vol 125 ◽  
pp. 107-116 ◽  
Author(s):  
Delphine Meier-Girard ◽  
Edgar Delgado-Eckert ◽  
Emmanuel Schaffner ◽  
Christian Schindler ◽  
Nino Künzli ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Healthy Subjects ◽  
Heart Rate Dynamics

Effect of exercise on nasal mucous velocity and nasal airflow resistance in normal subjects

1979 ◽  
Vol 46 (2) ◽  
pp. 369-371 ◽  
Author(s):  
K. Saketkhoo ◽  
I. Kaplan ◽  
M. A. Sackner
Keyword(s):  
Heart Rate ◽  
Healthy Subjects ◽  
Normal Subjects ◽  
Cycle Ergometer ◽  
Submaximal Exercise ◽  
Base Line ◽  
Nasal Airflow ◽  
Airflow Resistance ◽  
The Mean

Nasal mucous velocity and nasal airflow resistance were measured in nine healthy subjects before, during 5 min, and 1 h after submaximal exercise of 20 min with a cycle ergometer set in such a way that heart rate ranged from 125 to 135 beats/min. Nasal mucous velocity rose from a base line of 7.6–12.7 mm/min during exercise and returned to the base-line value 5 and 60 min after exercise. The mean expiratory nasal airflow resistance at a flow of 0.4 l/s decreased from a base line of 1.6–0.6 cmH2O . (l/s)-1 during exercise and returned to the baseline value 5 and 60 min after exercise.

scholarly journals Effect of standing on neurohumoral responses and plasma volume in healthy subjects

1998 ◽  
Vol 84 (3) ◽  
pp. 914-921 ◽  
Author(s):  
Giris Jacob ◽  
Andrew C. Ertl ◽  
John R. Shannon ◽  
Raffaello Furlan ◽  
Rose Marie Robertson ◽  
...  
Keyword(s):  
Heart Rate ◽  
Plasma Renin Activity ◽  
Plasma Volume ◽  
Healthy Subjects ◽  
Plasma Renin ◽  
Urine Osmolality ◽  
Normal Subjects ◽  
Renin Activity ◽  
Blue Dye ◽  
Upright Posture

Upright posture leads to rapid pooling of blood in the lower extremities and shifts plasma fluid into surrounding tissues. This results in a decrease in plasma volume (PV) and in hemoconcentration. There has been no integrative evaluation of concomitant neurohumoral and PV shifts with upright posture in normal subjects. We studied 10 healthy subjects after 3 days of stable Na+ and K+ intake. PV was assessed by the Evans blue dye method and by changes in hematocrit. Norepinephrine (NE), NE spillover, epinephrine (Epi), vasopressin, plasma renin activity, aldosterone, osmolarity, and kidney response expressed by urine osmolality and by Na+ and K+ excretion of the subjects in the supine and standing postures were all measured. We found that PV fell by 13% (375 ± 35 ml plasma) over ∼14 min, after which time it remained relatively stable. There was a concomitant decrease in systolic blood pressure and an increase in heart rate that peaked at the time of maximal decrease in PV. Plasma Epi and NE increased rapidly to this point. Epi approached baseline by 20 min of standing. NE spillover increased 80% and clearance decreased 30% with 30 min of standing. The increase in plasma renin activity correlated with an increase in aldosterone. Vasopressin increased progressively, but there was no change in plasma osmolarity. The kidney response showed a significant decrease in Na+ and an increase in K+ excretion with upright posture. We conclude that a cascade of neurohumoral events occurs with upright posture, some of which particularly coincide with the decrease in PV. Plasma Epi levels may contribute to the increment in heart rate with maintained upright posture.

Reproducibility of time domain indexes of heart rate variability in normal subjects and patients with vasovagal syncope

2002 ◽  
Author(s):  
G.E. Kochiadakis ◽  
P.J. Lees ◽  
E.M. Kanoupakis ◽  
N.E. Igoumenidis ◽  
G.I. Chlouverakis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Vasovagal Syncope ◽  
Normal Subjects

Prediction of PAF Attacks using Time-Domain Measures of Heart Rate Variability

2018 ◽  
pp. 536-542
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Paroxysmal Atrial Fibrillation ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Normal Subjects ◽  
Time Interval ◽  
K Nearest Neighbor

Paroxysmal atrial fibrillation (PAF) is the mainly encountered type of arrhythmia and there is no validated method to predict a PAF attack before it occurs. In this study, predicting the PAF event was aimed using time-domain heart rate variability (HRV) measures in k- nearest neighbor (k-nn) classifier. Traditional time-domain HRV measures were analyzed in every 5-minute segments from 49 normal subjects, 25 patients with PAF attack and 25 patients with no attack within 45 minutes. All features were investigated whether they showed statistically significance. Significant features were classified by k-nn for odd numbers of neighbors between 1 and 19. This setup was run with two different configurations as study 1 to discriminate patients with PAF attack from normals and patients with no attack, and study 2 to discriminate patients with PAF attack from patients with no attack. SDNN, RMSSD and pNN50 measures were found to show statistically significant differences with p less than 0.05 in segments of 0-5 min, 2.5-7.5 min and 5-10 min intervals only. The maximum classification accuracy was obtained in the time interval of 2.5-7.5 minutes with %79 for Study 1 and just before attack with %80 for Study 2 in the time interval of 0-5 minutes. Results showed that the prediction of PAF events was possible when the classification between normal subjects from PAF patients was accurate. PAF attack can be determined 2.5 minutes earlier by simple classifier algorithms.

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