A method for irreversible analysis of physiological signal time based on fuzzy permutation

Abstract

The present invention belongs to the field of physiological signal processing, and discloses a method for analyzing the time irreversibility of physiological signals based on fuzzy permutation, including: Step 1, perform spatial vector reconstruction on the physiological signal sequence to obtain a reconstructed vector sequence, and calculate the control parameters for the fuzzy transformation of the sequence; Step 2, construct and optimize the permutation sequence of the reconstructed sequence; Step 3, perform fuzzy processing on the permutation type; Step 4, obtain the reverse-order physiological signal sequence, and through fuzzy permutation transformation, obtain the time-reverse fuzzy permutation sequence; Step 5, count the permutation type and its probability distribution; Step 6, measure the difference in the probability distribution of the fuzzy permutation of the physiological signal sequence and the reverse-order physiological signal sequence, and quantify the fuzzy permutation time irreversibility of the physiological signal. The present invention effectively associates the spatial structure characteristics of the specific vector with the standard vector, thereby optimizing the construction method of the permutation type and improving the accuracy of the quantitative analysis of the time irreversibility of physiological signals.

Description

Technical Field

[0001] The present invention belongs to the field of physiological signal processing, and specifically relates to a method for analyzing the time irreversibility of physiological signals based on fuzzy permutation. Background Art

[0002] The highly complex characteristics of the physiological system make physiological signals have characteristics such as non-linearity and non-equilibrium. The main manifestation of the non-equilibrium characteristic is time irreversibility. The quantification of time irreversibility requires calculating the joint probability difference between the forward and reverse sequences, which is currently mainly carried out through the coarse-graining method of physiological signals. Since the permutation type characterizes the spatial structure characteristics of the sequence and does not require operations such as interval division, it plays an important role in the analysis of the time irreversibility of physiological signals. However, there are certain deficiencies in the traditional permutation type construction method, that is, it cannot reflect various specific vector space structures. The permutation type is an embodiment of the standard vector structure, that is, the differences of the sorted vector elements are the same. However, in real and complex physiological signals, there are a large number of specific vectors, that is, the differences of the sorted vector elements show diverse specific structures. The relative position relationship of the elements of the specific vector is the same as that of the standard vector, so their permutation types are the same. However, there are significant differences in the differences between the elements of the specific vector, and there are also certain differences in the spatial structure characteristics it contains and the standard vector. Therefore, in the permutation analysis of real physiological signals, using the traditional permutation type to characterize specific vectors has certain deviations, especially in the extraction of signal spatial structure characteristics, probability estimation, and quantification of information parameters, there are large limitations.

[0003] The permutation type plays an important role in the analysis of the time irreversibility of physiological signals, but there are certain defects in the characterization of the specific vector space structure. Therefore, how to optimize the permutation type of specific vectors and improve the extraction accuracy of the spatial structure characteristics of physiological signals is an urgent problem to be solved in the analysis of permutation time irreversibility.

[0004] The permutation type is an effective embodiment of the standard vector space structure characteristics, but when the differences of the sorted vector elements show diverse specific structures, the traditional permutation construction method has great limitations. Therefore, the traditional permutation type cannot characterize the structural characteristics of specific vectors, and the accuracy in the analysis of permutation time irreversibility is not high. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a method for analyzing the time irreversibility of physiological signals based on fuzzy permutation. By calculating the deviation degree of the differential sorted vector elements from the average value, and then using the fuzzy transformation method to generate the probability distribution of the permutation to which the mean deviation degree belongs, the spatial structure characteristics of specific vectors are effectively associated with the standard vector, thereby optimizing the construction method of the permutation type and improving the accuracy of the quantitative analysis of the time irreversibility of physiological signals.

[0006] To achieve the above object, the present invention is implemented by the following technical solutions:

[0007] The present invention is a method for analyzing the time irreversibility of physiological signals based on fuzzy permutation, and the method specifically includes the following steps:

[0008] Step 1: Given a physiological signal sequence X(t) with a length of L, perform spatial vector reconstruction on the physiological signal sequence X(t) to obtain a reconstructed vector sequence and calculate the control parameters of the sequence fuzzy transformation;

[0009] Step 2: Construct and optimize the permutation sequence of the reconstructed sequence

[0010] Step 3: Fuzzify the permutation type to generate a fuzzy permutation type π f =(π i , p i ), and construct a fuzzy permutation sequence π f (t)={π f (1),...π f (t),...π f (L-(m-1)τ)}.

[0011] Step 4: Reverse the physiological signal sequence X(t) in time to obtain a reversed physiological signal sequence X(-t), and through fuzzy permutation transformation, obtain a time-reversed fuzzy permutation sequence π f (-t)={π f (-1),...π f (-t),...π f (-L+(m-1)τ)};

[0012] Step 5: Statistically analyze the permutation types and their probability distributions corresponding to the physiological signal sequence and the reversed physiological signal sequence;

[0013] Step 6: Measure the difference in the probability distributions of the fuzzy permutations of the physiological signal sequence and the reversed physiological signal sequence, and quantify the time irreversibility of the fuzzy permutation of the physiological signal.

[0014] The further improvement of the present invention lies in that: Step 1 specifically includes the following steps:

[0015] Step 1.1: Perform spatial reconstruction with dimensions m and delay τ on the sequence physiological signal sequence X(t)={x(1), x(2),...x(t),...x(L)} to obtain a reconstructed vector sequence The spatial vectors in the reconstructed sequence are where the elements are x(t i) = x(t + (i - 1)τ).

[0016] Step 1.2, calculate the regulation parameter transformed by fuzzy permutation where and L are the mean value and length of the sequence X(t) respectively, and K is an adjustable control parameter.

[0017] The further improvement of the present invention lies in: Step 2, construct and optimize the permutation sequence of the reconstructed sequence Specifically, it includes the following steps:

[0018] Step 2.1, arrange the spatial vector in ascending order, and define the arranged spatial vector as the permutation vector X(j) = {x(j1),..., x(j i ),..., x(j m ). To directly reflect the spatial structure of the vector, use the position j of the original vector element in the permutation vector X(j) i to form the amplitude permutation type π t = (j 1， j2,..., j i ,..., j m );

[0019] Step 2.2, equalize the arranged elements: If there are equal elements in the spatial vector , such as two identical elements x(j i-1 ) = x(j i ) and three identical elements x(j l-2 ) = x(j l-1 ) = x(j l ), modify the coordinates corresponding to the identical elements in the amplitude permutation in Step 2.1 to the maximum value in the corresponding coordinates, that is, (j i , j i ) and (j l , j l , j l ), and construct the equal-value permutation type;

[0020] Step 2.3, construct the permutation sequence of the reconstructed sequence through the equal-value permutation type in Step 2.2

[0021] The further improvement of the present invention lies in: Step 3 specifically includes the following steps:

[0022] Step 3.1, for the permutation vector X(j) arranged in ascending order, calculate the difference between adjacent elements x′(j i ) = x(j i+1 ) - x(j i), construct an ascending difference vector X′(j) = {x′(j1),..., x′(j i ),..., x′(j m-1 )};

[0023] Step 3.2: Calculate the variance σ′(j) of the ascending difference vector X′(j) in Step 3.1:

[0024]

[0025] where μ′(j) is the mean value of the ascending difference vector X′(j);

[0026] Step 3.3: Calculate the probability p of the permutation vector X(j) belonging to the permutation type through a threshold-free fuzzy transformation function i .

[0027] The further improvement of the present invention lies in: in Step 3.3, the threshold-free fuzzy transformation function is the reciprocal function f I and the negative exponential function f E . Through the reciprocal function f I and the negative exponential function f E calculate the probability p of the fuzzy permutation belonging to the permutation type i :

[0028] p i = f E (σ′(j)) = exp(-α|σ′(j)|)

[0029]

[0030] where α is a regulation parameter.

[0031] The further improvement of the present invention lies in: Step 5 for statistically analyzing the permutation types and their probability distributions corresponding to the forward and reverse sequences specifically includes the following steps;

[0032] Step 5.1: Conduct statistics according to the original permutation, classify and organize the permutation types of the physiological signal sequence and the reverse physiological signal sequence, that is, statistically analyze the same original permutation type π i and its membership probability;

[0033] Step 5.2: Calculate the same original permutation type π i of the physiological signal sequence X(t) and its global probability distribution p fi . Assume that the physiological signal sequence X(t) contains N fuzzy permutations of π i , which are respectively π f1 = (π1, p1), π f2 = (π1, p2), π fi = (π1, p j ) and πfN =(π1, p N ), then the global probability p fi of the original permutation is

[0034]

[0035] Step 5.3. Calculate the same original permutation type π j of the physiological signal inverse sequence X(-t) and its global probability distribution p bj . Assume that the physiological signal sequence X(-t) contains M fuzzy permutations of π j , which are respectively π b1 =(π1, p1), π b2 =(π1, p2), π bj =(π1, p j ), and π bM =(π1, p M ). Then the global probability p Bj of the original permutation is

[0036]

[0037] The further improvement of the present invention lies in that: Step 6 specifically includes the following steps:

[0038] Step 6.1. Construct the global probability distributions P F ={p f1 , p f2 ,..., p fj ,...} and P B ={p b1 , p b2 ,..., p bj ,...} respectively through the probability distributions of the fuzzy permutations of the physiological signal sequence and the inverse physiological signal sequence,

[0039] Step 6.2. Calculate the probability distribution difference between P F and P B , and calculate the fuzzy permutation irreversibility fpIR through the permutation probability distribution difference:

[0040]

[0041] The beneficial effect of the present invention is:

[0042] 1. The present invention innovatively transforms the degree of mean deviation of the differential sorting vector through a fuzzy function, which can effectively characterize the deviation between the specific vector arrangement type and the standard vector, realizes the optimization of the physiological signal arrangement type, and can more accurately extract the spatial structure features of the sequence. By means of fuzzy transformation, the problem that the existing arrangement types cannot characterize the spatial structure of specific vectors is solved, and the time irreversibility feature of physiological signals can be more effectively quantified.

[0043] 2. The present invention first sorts the spatial vectors of physiological signals in ascending order, constructs traditional arrangement types and ascending differential vectors, calculates the degree of mean deviation of the ascending differential vectors, that is, the deviation between the specific vector spatial structure and the standard vector structure, and then calculates the probability of the deviation degree belonging to the arrangement through a fuzzy function. A fuzzy arrangement is constructed through traditional arrangement types and probability distributions to extract more effective vector structure feature information.

[0044] 3. The present invention characterizes the spatial structure of vectors through fuzzy arrangement. As the degree of mean deviation of the ascending differential vectors increases, the probability distribution of the belonging arrangement becomes smaller, accurately quantifying the deviation between specific vectors and standard vectors. Compared with existing methods, it can better characterize the features of vectors and has higher precision in the quantitative analysis of time irreversibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart for extracting the equal value distribution characteristics of time series symbols of the present invention.

[0046] Figure 2 is a schematic diagram of the standard vector and specific vector corresponding to the arrangement type.

[0047] Figure 3 is a fuzzy transformation method based on the reciprocal and negative exponential functions.

[0048] Figure 4 is the time irreversibility of the fuzzy arrangement and the original arrangement of healthy young and elderly heart rate signals. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will disclose the embodiments of the present invention in a schematic diagram. For the sake of clarity, many practical details will be described together in the following description. However, it should be understood that these practical details are not used to limit the present invention. That is to say, in some embodiments of the present invention, these practical details are not necessary.

[0050] As Figure 1 shown, the present invention is a method for analyzing the time irreversibility of physiological signals based on fuzzy arrangement, and the method specifically includes the following steps:

[0051] Step 1: Given a physiological signal sequence X(t) of length L, perform spatial vector reconstruction on the physiological signal sequence X(t) to obtain a reconstructed vector sequence and calculate the control parameters of sequence fuzzy transformation.

[0052] Step 1 specifically includes the following steps:

[0053] Step 1.1: Perform spatial reconstruction of dimension m and delay τ on the sequence physiological signal sequence X(t) = {x(1), x(2),... x(t),... x(L)} to obtain a reconstructed vector sequence The spatial vector elements in the reconstructed sequence are where the element is x(t i ) = x(t + (i - 1)τ).

[0054] Step 1.2: Calculate the regulation parameters of fuzzy permutation transformation where and L are the mean and length of the sequence X(t) respectively, and K is an adjustable control parameter.

[0055] Step 2: Construct and optimize the permutation sequence of the reconstructed sequence Specifically includes the following steps:

[0056] Step 2.1: Arrange the spatial vectors in ascending order. After arranging the spatial vectors, define them as the permutation vector X(j) = {x(j1),..., x(j i ),..., x(j m )}. To directly reflect the spatial structure of the vector, use the position j of the original vector element in the permutation vector X(j) i to form the amplitude permutation type π t = (j1, j2,..., j i ,..., j m );

[0057] Step 2.2: Equalization processing of permutation elements: If there are equal elements in the spatial vector such as two identical elements x(j i-1 ) = x(j i ) and three identical elements x(j l-2 ) = x(j l-1 ) = x(j l ), modify the coordinates corresponding to the same elements in the amplitude permutation in Step 2.1 to the maximum value in the corresponding coordinates, that is, (j i , j i ) and (j l , j l , j l) to construct an equivalent permutation type;

[0058] Step 2.3: Construct the permutation sequence of the reconstructed sequence through the equivalent permutation type in Step 2.2

[0059] Step 3: Fuzzify the permutation type to generate a fuzzy permutation type π f =(π i , p i ), and construct a fuzzy permutation sequence π f (t) = {π f (1),... π f (t),... π f (L - (m - 1)τ)}.

[0060] Specifically, it includes the following steps:

[0061] Step 3.1: For the sorted permutation vector X(j), calculate the difference x′(j i ) = x(j i+1 ) - x(j i ), and construct an ascending difference vector X′(j) = {x′(j1),..., x′(j i ),..., x′(j m-1 )};

[0062] Step 3.2: Calculate the variance σ′(j) of the ascending difference vector X′(j) in Step 3.1:

[0063]

[0064] where μ′(j) is the mean of the ascending difference vector X′(j);

[0065] As Figure 2 shown, the solid-line elements are standard vectors, and the differences between the elements of their ascending permutation sequences are the same, i.e., σ′(j) = 0; the dashed-line elements are specific vectors, and their permutation types are the same as those of the standard vectors, but the differences between the elements of their ascending permutation sequences are not the same, σ′(j) > 0.

[0066] Step 3.3: Calculate the probability p i that the permutation vector X(j) belongs to the permutation type through a fuzzy transformation function without a threshold. In the present invention, the reciprocal function f I and the negative exponential function f E are taken as examples to calculate the membership probability p i of the fuzzy permutation, where α is the control parameter in Step 1.2;

[0067] p i = f E(σ′(j)) = exp(-α|σ′(j)|)

[0068]

[0069] As Figure 3 shown, assuming that the permutation sequence of the standard vector is an ideal ascending sequence, σ′(j) = 0, and the probability p i of belonging to the permutation type is 1. As the specificity of the vector increases, σ′(j) increases, and the membership relationship with the standard permutation gradually decreases, and the probability p i of belonging to the permutation type also gradually decreases.

[0070] Step 4: Reverse the physiological signal sequence X(t) in time to obtain the reversed physiological signal sequence X(-t), and through fuzzy permutation transformation, obtain the time-reversed fuzzy permutation sequence π f (-t) = {π f (-1),... π f (-t),... π f (-L + (m - 1)τ)};

[0071] Step 5: Statistically analyze the permutation types and their probability distributions corresponding to the physiological signal sequence and the reversed physiological signal sequence;

[0072] Specifically, it includes the following steps;

[0073] Step 5.1: Statistically analyze according to the original permutation, classify and organize the permutation types of the physiological signal sequence and the reversed physiological signal sequence, that is, statistically analyze the same original permutation type π i and its membership probability;

[0074] Step 5.2: Calculate the same original permutation type π i of the physiological signal sequence X(t) and its global probability distribution p fi . Assuming that the physiological signal sequence X(t) contains N fuzzy permutations of π i , which are respectively π f1 = (π1, p1), π f2 = (π1, p2), π fi = (π1, p j ), and π fN = (π1, p N ), then the global probability p fi of the original permutation is

[0075]

[0076] Step 5.3: Calculate the same original permutation type π j of the reversed physiological signal sequence X(-t) and its global probability distribution p bjPerform calculations assuming that the physiological signal sequence X(-t) contains π j There are M fuzzy permutations of j , which are π b1 =(π1, p1), π b2 =(π1, p2), π bj =(π1, p j ), and π bM =(π1, p M ). Then the global probability p Bj of the original permutation is

[0077]

[0078] Step 6. Measure the difference in the probability distributions of the fuzzy permutations of the physiological signal sequence and the reverse physiological signal sequence, and quantify the time irreversibility of the fuzzy permutation of the physiological signal. Specifically, it includes the following steps:

[0079] Step 6.1. Construct the global probability distributions P F ={p f1 , p f2 ,..., p fj ,...} and P B ={p b1 , p b2 ,..., p bj ,...} through the probability distributions of the fuzzy permutations of the physiological signal sequence and the reverse physiological signal sequence.

[0080] Step 6.2. Calculate the difference in the probability distributions of P F and P B , and calculate the fuzzy permutation irreversibility fpIR through the difference in the permutation probability distributions:

[0081]

[0082] To detect the effect of the time irreversibility analysis method of physiological signals based on fuzzy permutation, the present invention uses the heart rate signals in the PhysioNet public database for simulation tests. The heart rate signals come from the 'Fantasia' database, and 10 groups of heart rate signals of healthy young people and 10 groups of heart rate signals of healthy elderly people are selected. The variance transformation function of the fuzzy permutation time irreversibility fpIR uses the negative exponential function, the control parameter K is 0.2, and a comparative analysis is carried out with the time irreversibility of the original permutation. The dimension of the phase space reconstruction is set to m = 3, the delay τ ranges from 1 to 3, and Ys is the joint probability difference between the forward and reverse sequences. The simulation experiment platform is the Python 3.10 software under the Windows system, and the analysis results of the present invention are not affected by the operating system and the Python software version.

[0083] The analysis results of the original permutation time irreversibility and the fuzzy permutation time irreversibility for the heart rates of healthy young and healthy elderly are as follows Figure 4 shown. It can be seen from the figure that as the human body ages, the time irreversibility characteristics of the heart rate show a significant decrease. This is consistent with the currently widely accepted theory of complexity loss in the brain, that is, as the human body ages from young to old, the complex characteristics of the heart regulation system decline, so its non-equilibrium characteristics also decline. Thus, it can be known that both permutation time irreversibilities can effectively characterize the non-equilibrium characteristics of the decreased heart rate in healthy elderly people.

[0084] From the results of the statistical tests, the t-test results of the original permutation time irreversibility are p = 0.26, 0.13, and 6.5E-5 respectively, while the p-values of the fuzzy permutation time irreversibility are 0.0001, 0.001, and 3.1E-5. According to the characteristics of statistical tests, when p < 0.05, it is considered that there are significant differences between the two sets of data, and the greater the decrease in the p-value, the greater the difference. Therefore, the original permutation time irreversibility can only effectively distinguish the two heart rate signals when the delay is 3, while the fuzzy permutation time irreversibility can effectively distinguish the two signals at all 3 delays, and when the delay is 3, the effect of the fuzzy permutation time irreversibility is better.

[0085] It can be seen from the above simulation results that the method for analyzing the time irreversibility of physiological signals based on fuzzy permutation proposed by the present invention can more effectively characterize the non-equilibrium characteristics of physiological signals.

[0086] The above are only the embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for irreversible analysis of physiological signal time based on fuzzy permutation, characterized in that: The specific steps of the irreversible time analysis method for physiological signals are as follows: Step 1. Given a physiological signal sequence \(X(t)\) with length \(L\), perform spatial vector reconstruction on the physiological signal sequence \(X(t)\) to obtain a reconstructed vector sequence and calculate the control parameter of sequence fuzzy transformation; Step 2: Construct and optimize the permutation sequence of the reconstruction sequence Step 3: Fuzzify the permutation type to generate a fuzzy permutation type π f =(π i , p i ), and construct a fuzzy permutation sequence π f (t) = {π f (1),... π f (t),... π f (L - (m - 1)τ)}; Step 4: Reverse the physiological signal sequence X(t) in time to obtain the reversed physiological signal sequence X(-t), and through fuzzy permutation transformation, obtain the time-reversed fuzzy permutation sequence π f (-t) = {π f (-1),... π f (-t),... π f (-L + (m - 1)τ)}; Step 5: Statistically analyze the permutation types and their probability distributions corresponding to the physiological signal sequence and the reverse physiological signal sequence; Step 6: Measure the difference in the probability distributions of the fuzzy permutations of the physiological signal sequence and the reverse physiological signal sequence, and quantify the irreversible time of the fuzzy permutation of the physiological signal.

2. The method for irreversible analysis of physiological signal time based on fuzzy permutation according to claim 1, wherein: The specific steps of Step 1 are as follows: Step 1.

1. Perform space reconstruction on the sequence physiological signal sequence X(t) = {x(1), x(2),... x(t),... x(L)} with dimension m and delay τ to obtain the reconstructed vector sequence The spatial vectors in the reconstructed sequence are where the elements are x(t i ) = x(t + (i - 1)τ); Step 1.

2. Calculate the regulation parameter for the fuzzy permutation transformation where and L are the mean and length of the sequence X(t), respectively, and K is an adjustable control parameter.

3. A method for irreversible analysis of physiological signal time based on fuzzy permutation according to claim 1, characterized in that: Step 2: Construct and optimize the permutation sequence of the reconstructed sequence Specifically, it includes the following steps: Step 2.1: Arrange the spatial vectors in ascending order. After arranging the spatial vector elements, define the arranged vector X(j) = {x(j1),..., x(j i ),..., x(j m )}. To directly reflect the spatial structure of the vector, use the position j of the original vector elements in the arranged vector X(j) i to form the amplitude arrangement type π t = (j1, j2,..., j i ,..., j m ); Step 2.

2. Arrangement element equalization processing: If there are equal elements in the space vector , modify the coordinates corresponding to the same elements in the amplitude arrangement in Step 2.1 to the maximum value in the corresponding coordinates, that is, (j i , j i ) and (j l , j l , j l ), and construct an equal arrangement type; Step 2.

3. Construct the permutation sequence of the reconstructed sequence through the equivalent permutation type in Step 2.2 4. A method for irreversible analysis of physiological signal time based on fuzzy permutation according to claim 1, characterized in that: The specific steps of Step 3 are as follows: Step 3.

1. For the sorted permutation vector X(j), calculate the difference between adjacent elements x′(j i ) = x(j i+1 ) - x(j i ), and construct an ascending difference vector X′(j) = {x′(j1),..., x′(j i ),..., x′(j m-1 )}; Step 3.2: Calculate the variance σ′(j) of the ascending difference vector X′(j) in Step 3.1: where μ′(j) is the mean of the ascending difference vector X′(j); Step 3.3: Calculate the probability p of the permutation vector X(j) belonging to the permutation type through a fuzzy transformation function without a threshold i .

5. A method for irreversible analysis of physiological signal time based on fuzzy permutation according to claim 4, characterized in that: In step 3.3, the fuzzy transformation function without a threshold is the reciprocal function f I and the negative exponential function f E . Through the reciprocal function f I and the negative exponential function f E , calculate the probability p i : p i = f E (σ′(j)) = exp(-α|σ′(j)|) where α is a regulation parameter.

6. The physiological signal time irreversible analysis method based on fuzzy permutation according to claim 1, wherein: The specific steps for Step 5 to statistically analyze the permutation types and their probability distributions corresponding to the forward and reverse sequences are as follows; Step 5.

1. Statistically classify the arrangement types of the physiological signal sequence and the reverse physiological signal sequence according to the original arrangement, that is, count the same original arrangement type π i and its membership probability; Step 5.

2. Calculate the same original permutation type π of the physiological signal sequence X(t) i and its global probability distribution p fi . Assume that the physiological signal sequence X(t) contains N fuzzy permutations of π i , which are respectively π f1 = (π1, p1), π f2 = (π1, p2), π fi = (π1, p j ), and π fN = (π1, p N ). Then the global probability p fi of the original permutation is Step 5.

3. For the physiological signal inverse sequence X(-t) with the same original permutation type π j and its global probability distribution p bj calculate. Assume that the physiological signal sequence X(-t) contains π j with a total of M fuzzy permutations, which are respectively π b1 =(π1, p1), π b2 =(π1, p2), π bj =(π1, p j ), and π bM =(π1, p M ). Then the global probability p Bj of the original permutation is 7. A method for irreversible analysis of physiological signal time based on fuzzy permutation according to claim 1, characterized in that: The specific steps of Step 6 are as follows: Step 6.

1. Construct the global probability distributions P F ={p f1 , p f2 ,..., p fj ,...} and P B ={p b1 , p b2 ,..., p bj ,...}, Step 6.2, calculate P F and P B 's probability distribution difference, and calculate the fuzzy permutation irreversibility fpIR by arranging the probability distribution difference:

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