scholarly journals Simple and Fast Continuous Estimation Method of Respiratory Frequency During Sleep using the Number of Extreme Points of Heart Rate Time Series

2007 ◽  
Vol 127 (12) ◽  
pp. 1982-1987 ◽  
Author(s):  
Yutaka Yoshida ◽  
Kiyoko Yokoyama ◽  
Naohiro Ishii
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Estimation Method ◽  
Extreme Points ◽  
Respiratory Frequency ◽  
Continuous Estimation

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kanghyu Lee ◽  
David K. Han ◽  
Hanseok Ko
Keyword(s):  
Heart Rate ◽  
Estimation Method ◽  
Region Of Interest ◽  
Ensemble Empirical Mode Decomposition ◽  
Superior Performance ◽  
Indoor Environments ◽  
Vehicle Interior ◽  
Moving Vehicle ◽  
Ambient Lighting ◽  
Driving Conditions

We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.

Effects of caloric stimulation on respiratory frequency and heart rate and blood pressure variability

2000 ◽  
Vol 53 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Kathrine Jauregui-Renaud ◽  
Kielan Yarrow ◽  
Ruth Oliver ◽  
Michael A Gresty ◽  
Adolfo M Bronstein
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Blood Pressure Variability ◽  
Respiratory Frequency ◽  
Caloric Stimulation

Respiratory sinus arrhythmia in the denervated human heart

1989 ◽  
Vol 67 (4) ◽  
pp. 1447-1455 ◽  
Author(s):  
L. Bernardi ◽  
F. Keller ◽  
M. Sanders ◽  
P. S. Reddy ◽  
B. Griffith ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Sinus Arrhythmia ◽  
Human Heart ◽  
Normal Subjects ◽  
Respiratory Frequency ◽  
Autonomic Tone ◽  
Sinus Arrhythmia ◽  
Transplant Patients ◽  
Myocardial Wall ◽  
Analysis Technique

We performed this study to test whether the denervated human heart has the ability to manifest respiratory sinus arrhythmia (RSA). With the use of a highly sensitive spectral analysis technique (cross correlation) to define beat-to-beat coupling between respiratory frequency and heart rate period (R-R) and hence RSA, we compared the effects of patterned breathing at defined respiratory frequency and tidal volumes (VT), Valsalva and Mueller maneuvers, single deep breaths, and unpatterned spontaneous breathing on RSA in 12 normal volunteers and 8 cardiac allograft transplant recipients. In normal subjects R-R changes closely followed changes in respiratory frequency (P less than 0.001) but were little affected by changes in VT. On the R-R spectrum, an oscillation peak synchronous with respiration was found in heart transplant patients. However, the average magnitude of the respiration-related oscillations was 1.7–7.9% that seen in normal subjects and was proportionally more influenced by changes in VT. Changes in R-R induced by Valsalva and Mueller maneuvers were 3.8 and 4.9% of those seen in normal subjects, respectively, whereas changes in R-R induced by single deep breaths were 14.3% of those seen in normal subjects. The magnitude of RSA was not related to time since the heart transplantation, neither was it related to patient age or sex. Thus the heart has the intrinsic ability to vary heart rate in synchrony with ventilation, consistent with the hypothesis that changes, or rate of changes, in myocardial wall stretch might alter intrinsic heart rate independent of autonomic tone.

On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia

1995 ◽  
Vol 269 (2) ◽  
pp. H480-H486 ◽  
Author(s):  
Y. Yamamoto ◽  
J. O. Fortrat ◽  
R. L. Hughson
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Graining ◽  
Resting Heart Rate ◽  
Breathing Frequency ◽  
Fractal Nature ◽  
Sinus Arrhythmia ◽  
Spectral Exponent ◽  
Random Fractal

The purpose of the present study was to investigate the basic fractal nature of the variability in resting heart rate (HRV), relative to that in breathing frequency (BFV) and tidal volume (TVV), and to test the hypothesis that fractal HRV is due to the fractal BFV and/or TVV in humans. In addition, the possible fractal nature of respiratory volume curves (RVC) and HRV was observed. In the first study, eight subjects were tested while they sat quietly in a comfortable chair for 60 min. Beat-to-beat R-R intervals, i.e., HRV, and breath-by-breath BFV and TVV were measured. In the second study, six subjects were tested while they were in the supine position for 20-30 min. The RVC was monitored continuously together with HRV. Coarse-graining spectral analysis (Yamamoto, Y., and R. L. Hughson, Physica D 68: 250-264, 1993) was applied to these signals to evaluate the percentage of random fractal components in the time series (%Fractal) and the spectral exponent (beta), which characterizes irregularity of the signals. The estimates of beta were determined for each variable only over the range normally used to evaluate HRV. Values for %Fractal and beta of both BFV and TVV were significantly (P < 0.05) greater than those for HRV. In addition, there was no significant (P > 0.05) correlation between the beta values of HRV relative to either BFV (r = 0.14) or TVV (r = 0.34). RVC showed a smooth oscillation as compared with HRV; %Fractal for RVC (42.3 +/- 21.7%, mean +/- SD) was significantly (P < 0.05) lower than that for HRV (78.5 +/- 4.2%).(ABSTRACT TRUNCATED AT 250 WORDS)

An application of fractional differintegration to heart rate variability time series

2013 ◽  
Vol 111 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miguel A. García-González ◽  
Mireya Fernández-Chimeno ◽  
Lluis Capdevila ◽  
Eva Parrado ◽  
Juan Ramos-Castro
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability
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