Segmentation of EEG during sleep using time-varying autoregressive modeling

1989 ◽  
Vol 61 (6) ◽  
pp. 447-455 ◽  
Author(s):  
N. Amir ◽  
I. Gath
Keyword(s):  
Time Varying ◽  
Autoregressive Modeling

Time-Varying Autoregressive Modeling for Analyzing Transient Frequencies

1997 ◽  
Vol 36 (Part 2, No. 4B) ◽  
pp. L522-L523 ◽  
Author(s):  
Satoshi Kawata ◽  
Toshitaka Shigeoka ◽  
Osamu Nakamura
Keyword(s):  
Time Varying ◽  
Autoregressive Modeling

scholarly journals Comparison of nonparametric and parametric methods for time-frequency heart rate variability analysis in a rodent model of cardiovascular disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242147
Author(s):  
Emily M. Wong ◽  
Fern Tablin ◽  
Edward S. Schelegle
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fourier Transform ◽  
Ozone Exposure ◽  
Heart Rate Variability Analysis ◽  
Time Varying ◽  
Autoregressive Modeling ◽  
Short Time Fourier Transform ◽  
Modeling Methods ◽  
Short Time

The aim of time-varying heart rate variability spectral analysis is to detect and quantify changes in the heart rate variability spectrum components during nonstationary events. Of the methods available, the nonparametric short-time Fourier Transform and parametric time-varying autoregressive modeling are the most commonly employed. The current study (1) compares short-time Fourier Transform and autoregressive modeling methods influence on heart rate variability spectral characteristics over time and during an experimental ozone exposure in mature adult spontaneously hypertensive rats, (2) evaluates the agreement between short-time Fourier Transform and autoregressive modeling method results, and (3) describes the advantages and disadvantages of each method. Although similar trends were detected during ozone exposure, statistical comparisons identified significant differences between short-time Fourier Transform and autoregressive modeling analysis results. Significant differences were observed between methods for LF power (p ≤ 0.014); HF power (p ≤ 0.011); total power (p ≤ 0.027); and normalized HF power (p = 0.05). Furthermore, inconsistencies between exposure-related observations accentuated the lack of agreement between short-time Fourier Transform and autoregressive modeling overall. Thus, the short-time Fourier Transform and autoregressive modeling methods for time-varying heart rate variability analysis could not be considered interchangeable for evaluations with or without interventions that are known to affect cardio-autonomic activity.

scholarly journals Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

2018 ◽  
Vol 18 (2) ◽  
pp. 563-589 ◽  
Author(s):  
Basuraj Bhowmik ◽  
Manu Krishnan ◽  
Budhaditya Hazra ◽  
Vikram Pakrashi
Keyword(s):  
Spectral Analysis ◽  
Damage Detection ◽  
Real Time ◽  
Structural Damage ◽  
Streaming Data ◽  
Degree Of Freedom ◽  
Time Varying ◽  
Structural Damage Detection ◽  
Autoregressive Modeling ◽  
Singular Spectral Analysis

A novel baseline-free approach for continuous online damage detection of multidegree of freedom vibrating structures using recursive singular spectral analysis in conjunction with time-varying autoregressive modeling is proposed in this article. The acceleration data are used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by time-varying autoregressive modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear/nonlinear states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focused on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection are missing that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The recursive singular spectral analysis algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order time-varying autoregressive models are applied on the transformed responses to improve detectability. Numerical simulations performed on a five-degree of freedom nonlinear system and on a single degree of freedom system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real-time, reference-free structural health monitoring.

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