Time-varying Methods for Characterizing Nonstationary Dynamics of Physiological Systems

2010 ◽  
Vol 49 (05) ◽  
pp. 435-442 ◽  
Author(s):  
N. Selvaraj ◽  
J. Lee ◽  
K. H. Chon
Keyword(s):  
Early Detection ◽  
Blood Loss ◽  
Real Time ◽  
Particle Filtering ◽  
Clinical Monitoring ◽  
Time Varying ◽  
Parametric Approach ◽  
Abrupt Changes ◽  
Spontaneously Breathing ◽  
Ventilated Patients

Summary Background: Accurate and early diagnosis of various diseases and pathological conditions require analysis techniques that can capture time-varying (TV) dynamics. In the pursuit of promising TV signal processing methods applicable to real-time clinical monitoring applications, nonstationary spectral techniques are of great significance. Objectives: We present two potential practical applications of such techniques in quantifying TV physiological dynamics concealed in photoplethysmography (PPG) signals: early detection of blood-volume loss using a non-parametric approach known as variable frequency complex demodulation (VFCDM), and accurate detection of abrupt changes in respiratory rates using a parametric approach known as combined optimal parameter search and multiple mode particle filtering (COPS-MPF). Methods: The VFCDM technique has been tested using ear-PPG signals in two study models: mechanically ventilated patients undergoing surgery in operating room settings and spontaneously breathing conscious healthy subjects subjected to lower body negative pressure (LBNP) in laboratory settings. Extraction of respiratory rates has been tested using COPS-MPF technique in finger-PPG signals collected from healthy volunteers with abrupt changes in respiratory rate ranging from 0.1 to 0.4 Hz. Results: VFCDM method showed promise to detect the blood loss noninvasively in mechanical ventilated patients well before blood losses become apparent to the physician. In spontaneously breathing subjects during LBNP experiments, the early detection and quantification of blood loss was possibleat 40% of LBNP tolerance. COPS-MPF showed high accuracy in detecting the constant as well as sudden changes in respiratory rates as compared to other time-invariant methods. Conclusion: Integration of such robust algorithms into pulse oximeter device may have significant impact in real-time clinical monitoring and point-of-care healthcare settings.

scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

A Multimodal Solution to Blind Source Separation of Moving Sources

2010 ◽  
pp. 107-125
Author(s):  
Syed Mohsen Naqvi ◽  
Yonggang Zhang ◽  
Miao Yu ◽  
Jonathon A. Chambers
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Particle Filtering ◽  
Source Separation ◽  
Visual Modality ◽  
Time Varying ◽  
Video Cameras ◽  
Moving Sources ◽  
Tracking Process ◽  
Reverberant Environment

A novel multimodal solution is proposed to solve the problem of blind source separation (BSS) of moving sources. Since for moving sources the mixing filters are time varying, therefore, the unmixing filters should also be time varying and can be difficult to track in real time. In this solution the visual modality is utilized to facilitate the separation of moving sources. The movement of the sources is detected by a relatively simplistic 3-D tracker based on video cameras. The tracking process is based on particle filtering which provides robust tracking performance. Positions and velocities of the sources are obtained from the 3-D tracker and if the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, a good BSS performance for moving sources in a low reverberant environment can be achieved.

A threshold-based, real-time analysis in early detection of endpoint anomalies using SIEM expertise

2021 ◽  
Vol 2021 (4) ◽  
pp. 7-16
Author(s):  
Sivaraman Eswaran ◽  
Aruna Srinivasan ◽  
Prasad Honnavalli
Keyword(s):  
Early Detection ◽  
Real Time ◽  
Time Analysis ◽  
Real Time Analysis

Study on real-time thermal–mechanical–frictional coupling characteristics of ball bearings based on the inverse thermal network method

2021 ◽  
pp. 135065012199356
Author(s):  
Tie-Jun Li ◽  
Meng-Zhuo Wang ◽  
Chun-Yu Zhao
Keyword(s):  
Real Time ◽  
Thermal Contact Resistance ◽  
Thermal Contact ◽  
Time Varying ◽  
Ball Bearings ◽  
The Real ◽  
Thermal Network ◽  
Network Method ◽  
Thermal Network Method ◽  
Coupling Characteristics

The real-time thermal–mechanical–frictional coupling characteristics of bearings are critical to the accuracy, reliability, and life of entire machines. To obtain the real-time dynamic characteristics of ball bearings, a novel model to calculate point contact dynamic friction in mixed lubrication was firstly presented in this work. The model of time-varying thermal contact resistance under fit between the ring and the ball, between the ring and the housing, and between the ring and the shaft was established using the fractal theory and the heat transfer theory. Furthermore, an inverse thermal network method with time-varying thermal contact resistance was presented. Using these models, the real-time thermal–mechanical–frictional coupling characteristics of ball bearings were obtained. The effectiveness of the presented models was verified by experiment and comparison.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

Stability of real-time hybrid simulation involving time-varying delay and direct integration algorithms

2021 ◽  
pp. 107754632110016
Author(s):  
Liang Huang ◽  
Cheng Chen ◽  
Shenjiang Huang ◽  
Jingfeng Wang
Keyword(s):  
Real Time ◽  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Simulation ◽  
Time Varying ◽  
Discrete Time System ◽  
Time System ◽  
Integration Algorithms ◽  
The Stability ◽  
Continuous Time System

Stability presents a critical issue for real-time hybrid simulation. Actuator delay might destabilize the real-time test without proper compensation. Previous research often assumed real-time hybrid simulation as a continuous-time system; however, it is more appropriately treated as a discrete-time system because of application of digital devices and integration algorithms. By using the Lyapunov–Krasovskii theory, this study explores the convoluted effect of integration algorithms and actuator delay on the stability of real-time hybrid simulation. Both theoretical and numerical analysis results demonstrate that (1) the direct integration algorithm is preferably used for real-time hybrid simulation because of its computational efficiency; (2) the stability analysis of real-time hybrid simulation highly depends on actuator delay models, and the actuator model that accounts for time-varying characteristic will lead to more conservative stability; and (3) the integration step is constrained by the algorithm and structural frequencies. Moreover, when the step is small, the stability of the discrete-time system will approach that of the corresponding continuous-time system. The study establishes a bridge between continuous- and discrete-time systems for stability analysis of real-time hybrid simulation.

scholarly journals Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 980
Author(s):  
Hang Shu ◽  
Wensheng Wang ◽  
Leifeng Guo ◽  
Jérôme Bindelle
Keyword(s):  
Heat Stress ◽  
Early Detection ◽  
Dairy Cows ◽  
Real Time ◽  
Dairy Farms ◽  
Time Measurement ◽  
Heat Strain ◽  
Detection Methods ◽  
Livestock Farming ◽  
Real Time Measurement

In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.

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