Dynamical Response of a Planetary Gear System with Faults Using Recurrence Statistics
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Recurrence quantification analysis
Detecting the reliable transition point embedded in the electroencephalograms (EEGs) is a challenge in the field of epileptic research. In this research, a recurrence quantification analysis (RQA) is proposed to help medical doctors to reveal dynamical characteristics in EEGs of patients suffering from epilepsy. In contrast with traditional chaos methods, the merits of RQA method is that it can measure the complexity of a short and non-stationary signal without any assumptions such as linear, stationary and noiseless noise. In this study, EEGs with generalized epilepsy were collected in Epilepsy Center of Renji Hospital. The test results show that three RQA measurements, i.e. recurrence rate, determinism and entropy can track the complexity changes of brain electrical activity. RQA variables show a large fluctuation in pre-ictal stage, which reflects a transitional state leading to seizure activity. On the contrary, RQA variables fluctuate in relatively small bounds in ictal stage, which is due to organized and self-sustained rhythmic discharge. Therefore, RQA could be a promising approach in prediction and diagnosis for epileptic seizures.
Recurrence quantification analysis
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Recurrence quantification analysis (RQA) is useful in analyzing dynamical systems from a time series s(t). This paper investigates the robustness of RQA in detecting different dynamical regimes with respect to the recorded variable s(t). RQA was applied to time series x(t), y(t) and z(t) of a drifting Rössler system, which are known to have different observability properties. It was found that some characteristics estimated via RQA are heavily influenced by the choice of s(t) in the case of flows but not in the case of maps.
Recurrence quantification analysis
Observability
Robustness
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The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.
Recurrence quantification analysis
Recurrence plot
Plot (graphics)
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Representation
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Recurrence quantification analysis
Recurrence plot
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Oscillation (cell signaling)
SIGNAL (programming language)
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Complex dynamics
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Dynamical system (definition)
Recurrence quantification analysis
Complex system
Black box
Nonlinear dynamical systems
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The present study is aimed at characterizing the EEG dynamics of the Mild cognitive impairment (MCI) subjects compared to that of normal controls using Recurrence Quantification Analysis (RQA) and Cross Recurrence Quantification Analysis (CRQA). EEG from MCI and control subjects are recorded under resting eyes closed (EC) condition. Recurrence rate of RQA and CRQA are calculated for the signals from both groups. RQA method quantifies the regularity/complexity signals and CRQA method quantifies the similarities between the two signals. The CRQA analysis of EEG is carried out for signals between the channels from different lobes. These two measures are combined in a feature space. The clear distinction of the two groups is obtained using this method of combined RQA and CRQA measures.
Recurrence quantification analysis
Recurrence plot
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Recurrence quantification analysis
Sample entropy
Nonlinear dynamical systems
Recurrence plot
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The Recurrence plots (RPs) have been introduced in several different scientific and medical disciplines. The main purpose of recurrence plot is used to of identify the higher dimensional phase space trajectories. RPs are purely graphically representation which have been designed for the detection of hidden dynamical patterns and non-linearity present in the data, the evaluation of error which is caused by observational noise can be done by Recurrence Quantification Analysis (RQA). RQA method is initially used to minimize the error present in the given signals. RQA method is a basically a technique for the analysis of nonlinear data to quantify the number and duration of a dynamical systems. The recurrence plot is used for time series domain for multidimensional signal also. Recurrence is the property of non-stationary and dynamical system to characteristics the time series analysis in phase space trajectories. Recurrence Quantification Analysis is used to derive from recurrence plots, which are based upon distances matrices of time series.
Recurrence quantification analysis
Recurrence plot
Plot (graphics)
Dynamical system (definition)
Representation
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Recurrence quantification analysis
Recurrence plot
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Nonlinear dynamical systems
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