Assessment of the Heart Rate Variability during Arousal from Sleep by Cohen’s Class Time-Frequency Distributions

2007 ◽  
pp. 30-33 ◽  
Author(s):  
Martin Mendez ◽  
A.M. Bianchi ◽  
O.P. Villantieri ◽  
S. Cerutti
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Class Time ◽  
Cohen's Class

scholarly journals Cohen’s Class Time-Frequency Distributions for Measurement Signals as a Means of Monitoring Technological Processes

2019 ◽  
Vol 49 (4) ◽  
pp. 252-256
Author(s):  
D. B. Fedosenkov ◽  
A. A. Simikova ◽  
S. M. Kulakov ◽  
B. A. Fedosenkov
Keyword(s):  
Frequency Distributions ◽  
Time Frequency ◽  
Technological Processes ◽  
Class Time ◽  
Cohen's Class

scholarly journals COHEN’S CLASS TIME-FREQUENCY DISTRIBUTIONS FOR MEASUREMENT SIGNALS AS A MEANS OF MONITORING TECHNOLOGICAL PROCESSES

2019 ◽  
Vol 62 (4) ◽  
pp. 324-329
Author(s):  
D. B. Fedosenkov ◽  
A. A. Simikova ◽  
S. M. Kulakov ◽  
B. A. Fedosenkov
Keyword(s):  
Wigner Distribution ◽  
Bulk Material ◽  
Mathematical Tool ◽  
Distribution Map ◽  
Frequency Distributions ◽  
One Dimensional ◽  
Time Frequency ◽  
Technological Processes ◽  
Class Time ◽  
Cohen's Class

The article presents and describes Cohen’s class time-frequency distributions which are expedient to use as a mathematical tool that allows to create a convenient – in terms of information content and semantic clarity – visual-graphical representation of the opera ting modes of various technological processes including processes of ferrous metallurgy. It was noted that a controlling process is usually implemented without simultaneous visual monitoring of each scalar (one-dimensional) coordinate that is under control, but the presence of such monitoring is an important condition for the computer-aided controlling of the dynamics of non-stationary technological processes. To eliminate this drawback, it was proposed to perform synchronous monitoring using the multidimensional Cohen’s class time-frequency distributions, when each measurement scalar signal is specifically represented through one of these distributions, for example, the Wigner-Ville distribution. An expression is given for the generalized distribution of Cohen’s class with a distribution kernel and an ambiguity function. This function allows receiving distributions of various types from the maternal function. The most typical representatives of time-frequency distributions forming this class are given with their available  kernels. The possibility of appearance of interference elements, which make it difficult to identify the controlled modes, on a signal distribution map is proved. Case of the formation of virtual elements within the Wigner-Ville distribution representing a two-component one-dimensional signal is considered. Te conditions are explained for the emergence of parasitic elements on the distribution map, obtained, for example, during realizing the process of multi-component feeding the bulk blast furnace charge materials in the production of sintering mixture. An analytical expression is obtained for the Wigner distribution, which displays a multi-component scalar signal and contains the information (useful) and virtual (parasitic) parts of the time-frequency distribution. A link between the number of bulk material feeders available in the feeding devices unit and the number of parasitic (virtual) elements in the Wigner distribution was determined. Using the dosing process as an example, the effect of the noise components propagation in the Wigner distribution is demonstrated. An example is given to illustrate the penetration of noise into the Wigner distribution and appearance of the virtual concentration in it when displaying a signal waveform with a noisy pause and two sections with different frequencies. An expression for the Wigner distribution in the form of a comb function is obtained. The conclusion was made about the parameters of the distribution periodicity and the required sampling frequency of measurement signals.

APPLICATION OF COHEN'S CLASS TIME-FREQUENCY DISTRIBUTIONS IN THE BELOUSOV–ZHABOTINSKY REACTION ANALYSIS

2004 ◽  
Vol 14 (10) ◽  
pp. 3679-3688 ◽  
Author(s):  
K. DAROWICKI ◽  
W. FELISIAK
Keyword(s):  
Batch Reactor ◽  
Three Dimensional ◽  
Power Spectra ◽  
Kernel Functions ◽  
Function Parameter ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Class Time ◽  
Cohen's Class ◽  
Belousov Zhabotinsky Reaction

The systematic study of application of Cohen's class time-frequency representations in the analysis of periodic BZ oscillations generated in batch reactor has been presented. Several distributions belonging to Cohen's class have been applied in the analysis of selected signal being the register of bromide selective electrode. Among them were Wigner–Ville, Choi–Williams and cone-shaped distributions. The application of mentioned methods allow instantaneous power spectra to be obtained and simultaneously give the possibility of observing evolution of frequency composition of investigated oscillations. The systematic filtering of signal, using so-called kernel functions, allows to eliminate undesirable cross terms and finally leads to the selection of a suitable method for chemical oscillations decomposition. The results presented in the form of three-dimensional pictures, illustrate the decrease in frequency of oscillations of exponential character. On the basis of the obtained results the cone-shaped distribution for kernel function parameter α=1 has been selected as the best method for Belousov–Zhabotinsky oscillations analysis in joint time-frequency domain.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

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