Recurrence quantification analysis of the logistic equation with transients

Trulla, Lluis Lligoña; Giuliani, Alessandro; Zbilut, Joseph P.; Webber, Charles L.

doi:10.1016/s0375-9601(96)00741-4

Cited by 407 publications

(313 citation statements)

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“…This method was performed for the measurement of the complexity of the time series in a symmetrical K-dimensional square matrix, which is calculated by computing the Euclidean distances of each vector Xi from all the others (Trulla et al, 1996) The vectors represent the RR interval time series as a trajectory in m dimensional space. The recurrence plot (RP) is the representation of the matrix as a black (for ones) and white (for zeros) image (Niskanen et al, 2004) in which L max is the longest diagonal line segment in a continuous row within the plot.…”

Section: Methods Of Measurement and Analysis Of Hr And Hrv In Dairy Cmentioning

confidence: 99%

Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research

Kovács

Jurkovich

Bakony³

et al. 2014

Animal

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Heart rate (HR) measurements have been used to determine stress in livestock species since the beginning of the 1970s. However, according to the latest studies in veterinary and behaviour-physiological sciences, heart rate variability (HRV) proved to be more precise for studying the activity of the autonomic nervous system. In dairy cattle, HR and HRV indices have been used to detect stress caused by routine management practices, pain or milking. This review provides the significance of HR and HRV measurements in dairy cattle by summarising current knowledge and research results in this area. First, the biological background and the interrelation of the autonomic regulation of cardiovascular function, stress, HR and HRV are discussed. Equipment and methodological approaches developed to measure interbeat intervals and estimate HRV in dairy cattle are described. The methods of HRV analysis in time, frequency and non-linear domains are also explained in detail emphasising their physiological background. Finally, the most important scientific results and potential possibilities for future research are presented.Keywords: heart rate, heart rate variability, stress, welfare, dairy cattle ImplicationsHousing and milking technology, health status and way of handling of animals influence the well-being of animals in intensive dairy farming. Non-invasive methods of assessing stress include heart rate and heart rate variability measurements that involve the monitoring of the autonomic nervous function by digital, high frequency, 24 h multi-channel electrocardiographic recorders. As high-producing cattle breeds are very sensitive to environmental factors, monitoring and decreasing stress is of major importance in terms of both animal welfare and production. IntroductionIn intensive dairy farming, housing and milking systems are main factors in determining the welfare of animals, as adaptation to environmental changes can be challenging for high-producing breeds.Important aspects in the improvement of dairy cattle management systems in respect of animal welfare are the recognition and evaluation of stress. The stressfulness of the technological environment has been examined in many different contexts. Certain welfare studies proved that for intensively farmed cattle, the milking technology (Rushen et al., 2001;Wenzel et al., 2003), fear from given routine treatments (Holst, 1998) and pain (Broom, 1991;Mellor et al., 2000) mean such load that may cause stress (Dantzer and Mormède, 1983;von Borell, 2001) having a negative impact also on milk production (Rushen et al., 2001). Effects of technology and of social interactions can be described not only by classical descriptive behavioural observations (e.g. Milmann, 2013;Theurer et al., 2013), but with physiological measures as well (Hopster and Blokhuis, 1994;Rietmann et al., 2004). Most dairy cattle studies have focused on the stress reactions of animals expressed in neuroendocrine changes that are considered to be reliable indicators of stress and pain in livestock species (...

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Section: Methods Of Measurement and Analysis Of Hr And Hrv In Dairy Cmentioning

confidence: 99%

Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research

Kovács

Jurkovich

Bakony³

et al. 2014

Animal

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“…Studying the DVV from this perspective helps to understand why the DVV does not give the expected results when applied to oscillatory short time series. The computation of the DVV only takes into account the distance between random DVs (vertical black dots) and misses important information about the possible structures that can be used to detect chaos-order 19 and chaos-chaos 20 transitions: diagonal black lines and vertical white lines, 1 respectively. Examples of parameters derived from these structures are how much time the trajectories run parallel paths in the phase space representation (the DET parameter in Table I), complexity measured as the entropy of the black diagonals or the entropy of the vertical white lines (the ENTR or the ENTRWL parameters in Table I), etc.…”

Section: From Dvv To Delay Vector Recurrence Quantification Analmentioning

confidence: 99%

Comparative study of approximate entropy and sample entropy robustness to spikes

Picó

Cuesta–Frau

Aboy

et al. 2011

Artificial Intelligence in Medicine

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Extracting frequency-derived parameters allows for the identification and characterization of acoustic events, such as those obtained in passive acoustic monitoring applications. Situations where it is difficult to achieve the desired frequency resolution to distinguish between similar events occur, for example, in short time oscillating events. One feasible approach to make discrimination among such events is by measuring the complexity or the presence of non-linearities in a time series. Available techniques include the delay vector variance (DVV) and recurrence plot (RP) analysis, which have been used independently for statistical testing, however, the similarities between these two techniques have so far been overlooked. This work suggests a method that combines the DVV method with the recurrence quantification analysis parameters of the RP graphs for the characterization of short oscillating events. In order to establish the confidence intervals, a variant of the pseudo-periodic surrogate algorithm is proposed. This allows one to eliminate the fine details that may indicate the presence of non-linear dynamics, without having to add a large amount of noise, while preserving more efficiently the phase-space shape. The algorithm is verified on both synthetic and real world time series.

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“…One of the main advantages of the RP is that they allow the E-dimensional phase-space trajectory to be investigated through a binary two-dimensional representation of the recurrences of the states. Additionally, the quantification of the number and duration of the recurrences allows us to study the degree of predictability [16] as well as some other characteristics of the underlying dynamic systems: laminar phases [17], unstable periodic orbits [18], etc.…”

Section: Recurrence Plotsmentioning

confidence: 99%

Measuring predictability in ultrasonic signals: An application to scattering material characterization

2014

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In this paper, we present a novel and completely di↵erent approach to the problem of scattering material characterization: measuring the degree of predictability of the time series. Measuring predictability can provide information of the signal strength of the deterministic component of the time series in relation to the whole time series acquired. This relationship can provide information about coherent reflections in material grains with respect to the rest of incoherent noises that typically appear in non-destructive testing using ultrasonics. This is a non-parametric technique commonly used in chaos theory that does not require making any kind of assumptions about attenuation profiles. In highly scattering media (low SNR), it has been shown theoretically that the degree of predictability allows material characterization. The experimental results obtained in this work with 32 cement probes of 4 di↵erent porosities demonstrate the ability of this technique to do classification. It has also been shown that, in this particular application, the measurement of predictability can be used as an indicator of the percentages of porosity of the test samples with great accuracy.Keywords: Ultrasonic Signal Modality, Ultrasonic NDT, Multiple scattering, Noise, Determinism, Chaos Theory, Higher order statistics IntroductionWhen scattering materials are subject to ultrasonic non-destructive testing (NDT), the ultrasonic pulse undergoes some variations that are related to the internal grain microstructure of the specimen. Each grain behaves like a scattering center, producing an echo that when superimposed on other echoes coming from other grains can even hide the echoes produced by a possible defect. Similar situations are found in other related fields such as ultrasound B-mode scans (where the grain noise is called speckle) and in radar with clutter [1].In the literature, a wide range of solutions has been proposed to enhance the detection of small cracks or defects and to reduce (or even eliminate) the e↵ect discussed in each of the above situations. Some of these solutions are signal averaging, auto-and cross-correlation, matched filtering, frequency spectrum analysis [2], spectral correlation [3], and wavelet transformations [4]. The aforementioned analyses discard the information encoded in the grain noise; however, this information can be used to recognize potential di↵erences among materials, tissues, or surfaces. This approach has been employed to characterize materials by extracting temporal signal statistics [1], the resonance frequency [5], and even the penetration depth [6]. Our work continues with this line of thought and proposes a new approach that attempts to extract information about the nature of the signal, thereby characterizing the signal modality. Signal modality characterization is a key concept of a multidisciplinary research topic that includes di↵erent concepts such as signal linearity, stationarity, and stochasticity [7]. The applications of signal modality characterization are becoming mo...

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Recurrence quantification analysis of the logistic equation with transients

Cited by 407 publications

References 10 publications

Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research

Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research

Comparative study of approximate entropy and sample entropy robustness to spikes

Measuring predictability in ultrasonic signals: An application to scattering material characterization

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