Functional magnetic resonance complexity measurement method based on multi-scale permutation fuzzy entropy
A functional magnetic resonance and fuzzy entropy technology, applied in the field of signal processing, can solve the problem of high signal-to-noise ratio, and achieve the effect of improving anti-noise performance and stability
Active Publication Date: 2020-09-22
TAIYUAN UNIV OF TECH
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[0004] Aiming at the insufficiency and improvement of the existing fMRI signal complexity stability measurement methods, this method will provide a complexity measurement method that reflects multiple scales, high signal-to-noise ratio, and high stability
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[0020] Such as figure 1 As shown, the fMRI complexity measurement method based on multi-scale permutation fuzzy entropy is implemented by the following steps:
[0021] Step S1: Preprocessing the resting-state fMRI image data.
[0022] Step S2: performing low-pass filtering on the data by using a Butterworth low-pass filter (low-pass Butterworth filter).
[0023] Step S3: Perform data downsampling and average the time series in overlapping windows of length s to construct a continuous coarse-grained time series
[0024] Step S4: Serialize the coarse-grained time series: reconstruct the time series to obtain a new matrix, and arrange the components in each new matrix in ascending order.
[0025] Step S5: Perform phase space reconstruction.
[0026] Step S6: Adopt fuzzy membership function redefine and the distance between
[0027] Step S7: Calculating the fuzzy entropy value of the multi-scale arrangement.
[0028] In the step S1, the pretreatment is performed usi...
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The invention relates to the technical field of signal processing, and particularly relates to a functional magnetic resonance complexity measurement method, namely, multi-scale arrangement fuzzy entropy. The method solves the problems that a traditional complexity measurement method cannot prevent aliasing, the variance on a high scale is large, the anti-noise capability is weak, and the stability is poor. The invention discloses a functional magnetic resonance complexity measurement method, namely, the multi-scale permutation fuzzy entropy. The method is realized by adopting the following steps of S1, preprocessing functional magnetic resonance data; S2, carrying out low-pass filtering; S3, performing down-sampling on the time sequence; S4, symbolizing the time sequence; S5, carrying outphase space reconstruction; S6, adopting a fuzzy membership function; and S7, calculating a multi-scale arrangement fuzzy entropy. According to the method, the aliasing phenomenon of the time sequence during down-sampling is reduced, the variance value on a high scale is reduced, and the method has relatively good anti-noise capability and stability. The method is suitable for complexity analysisof the functional magnetic resonance image data.
Description
technical field [0001] The invention relates to the technical field of signal processing, in particular to a calculation method of multi-scale arrangement fuzzy entropy. Background technique [0002] Functional magnetic resonance imaging (fMRI) technology, which can effectively detect the activation of different functional areas in the cerebral cortex, is one of the most effective means in brain and cognitive science research. However, under the condition of low magnetic field strength, the change of fMRI signal is very weak, and the signal-to-noise of the acquired image data is low due to the unavoidable physiological noise and equipment noise during the data acquisition process. Therefore, accurate and reliable detection of physiological signals from image data with low signal-to-noise ratio is the primary problem to be solved in fMRI-based brain and cognitive science research. [0003] Due to the limitations of its own principles, traditional complexity analysis methods ...
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IPC IPC(8): G01R33/54
CPCG01R33/54
Inventor 王彬牛焱孙婕崔晓红相洁
Owner TAIYUAN UNIV OF TECH