CN110634574A - Heart rate dynamic time irreversibility analysis method based on equivalent arrangement - Google Patents

Heart rate dynamic time irreversibility analysis method based on equivalent arrangement Download PDF

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CN110634574A
CN110634574A CN201911034787.2A CN201911034787A CN110634574A CN 110634574 A CN110634574 A CN 110634574A CN 201911034787 A CN201911034787 A CN 201911034787A CN 110634574 A CN110634574 A CN 110634574A
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heart rate
sequence
time
irreversibility
arrangement
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CN110634574B (en
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姚文坡
王毓芳
李杰奇
郭大庆
尧德中
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University of Electronic Science and Technology of China
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Abstract

The invention discloses a heart rate dynamic time irreversibility analysis method based on equivalent permutation, which is applied to the technical field of physiological signal analysis and aims at solving the problem that the prior art is lack of effective extraction of dynamic nonequilibrium characteristics; the invention innovatively provides a dynamic time irreversible concept, a currently acquired heart rate signal is used as a positive sequence heart rate sequence, and the time reversal sequence is carried out on the positive sequence heart rate sequence to obtain a negative sequence heart rate sequence; then carrying out multi-dimensional phase space reconstruction on the positive sequence heart rate sequence and the negative sequence heart rate sequence; performing equivalent arrangement symbolization on the space vector of the reconstructed positive and negative heart rate sequence, and extracting and identifying the irreversible dynamic time of the heart rate by calculating the irreversible difference between the past positive and negative heart rate sequence and the current positive and negative heart rate sequence based on equivalent arrangement time; the method of the invention can effectively extract and identify the dynamic time irreversible characteristics of healthy young and old heart rates.

Description

Heart rate dynamic time irreversibility analysis method based on equivalent arrangement
Technical Field
The invention belongs to the technical field of physiological signal analysis, and particularly relates to a dynamic imbalance analysis and symbol time sequence analysis technology of a physiological signal.
Background
Heart rate regulation is primarily regulated by sympathetic and parasympathetic nerves and body fluids, and is influenced by various internal (e.g., mental states, hormones, sleep, etc.) and external (e.g., temperature, humidity, sound, etc.) factors, and thus the heart rate regulation system is typically a complex dynamic system. Imbalance is one of the characteristics of a complex heart rate system, and time irreversibility is an effective indicator for measuring imbalance. The time irreversibility mainly measures the difference of the statistical properties of positive and negative sequences, and mainly measures the difference of joint probabilities. Various time irreversible analysis methods exist at present, which effectively extract the unbalanced characteristics of the complex heart rate system, for example, methods of simplifying high-dimensional vectors by using arrangement types, utilizing differences of the out-degree and in-degree of a visual graph network, and the like are adopted. However, although the current time-irreversible analysis of the imbalance of the heart rate dynamics system also involves time delay, multidimensional vectors and the like, the joint probabilities of the vectors are independent of each other, and thus the static imbalance of the heart rate signal is mainly extracted by emphasis, i.e. the current state is not related to the past state, and the past state does not affect the current and subsequent states.
The real heart rate regulation system contains dynamic complex features, namely, the past state can influence the current state or even the future state, so that the quantitative analysis of the imbalance of the heart rate system dynamic is also important research content. The characteristics of unpredictability, complexity and the like of a complex system are effectively measured from the perspective of information theory, however, no effective parameter is available for quantitative analysis of the dynamic imbalance characteristics of the heart rate signals. Since the imbalance is an important feature of the complex heart rate system, the effective extraction of the dynamic imbalance feature is of great significance for analyzing the features of the complex heart rate system.
Disclosure of Invention
In order to solve the technical problems, the invention provides an equivalent arrangement-based heart rate dynamic time irreversibility analysis method, and effective quantitative analysis is carried out on the dynamic time irreversibility by using equivalent arrangement.
The technical scheme adopted by the invention is as follows: a heart rate dynamic time irreversibility analysis method based on equivalence permutation comprises the following steps:
s1, taking the currently acquired heart rate signal as a positive sequence heart rate sequence, and carrying out time reverse sequence on the positive sequence heart rate sequence to obtain a reverse sequence heart rate sequence;
s2, performing m-dimensional phase space reconstruction on the positive sequence heart rate sequence to obtain a first m-dimensional vector; performing m-dimensional phase space reconstruction on the negative sequence heart rate sequence to obtain a second m-dimensional vector;
s3, performing equivalent arrangement symbolization processing on the first m-dimensional vector and the second m-dimensional vector;
s4, counting the probability distribution of the equal arrangement of the positive sequence heart rate sequence and the negative sequence heart rate sequence;
s5, calculating the time irreversibility in an m-dimensional state according to the probability distribution of the equal arrangement of the positive sequence heart rate sequence and the negative sequence heart rate sequence;
s6, increasing the dimension m to m + tau, and repeating the steps S2-S5 to calculate the time irreversibility under the dimension m + tau;
and S7, calculating the heart rate dynamic time irreversibility according to the time irreversibility in the m-dimensional state obtained in the step S5 and the time irreversibility in the m + tau-dimensional state obtained in the step S6.
Further, the step S3 is specifically: respectively carrying out the following equivalent arrangement symbolization processing procedures on the first m-dimensional vector and the second m-dimensional vector to obtain a first equivalent arrangement corresponding to the first m-dimensional vector and a second equivalent arrangement corresponding to the second m-dimensional vector:
s31, constructing an initial arrangement type according to element coordinates after the elements in the vector are sequenced from small to large, namely one coordinate in the initial arrangement type corresponds to one type;
s32, if equivalent elements exist in the vector, correcting all coordinates corresponding to the equivalent elements in the arrangement type into the smallest coordinate in the corresponding equivalent to obtain the corrected arrangement type; otherwise, the arrangement type is not changed;
and S33, taking the arrangement type obtained in the step S32 as equivalent arrangement corresponding to the vector.
Further, step S4 is specifically: and respectively counting the probabilities of various types in the first equivalue permutation and the second equivalue permutation.
Further, the step S5 is a calculation process:
s51, calculating probability differences of corresponding types in the first equi-value permutation and the second equi-value permutation:
Figure BDA0002251165850000021
wherein p isiRepresenting the probability of the ith type, p, in a first permutation of the values-iRepresenting the ith type probability in the second equi-valued permutation;
s52, calculating the time irreversibility in the m-dimensional state according to the probability differences of each type in the first equinox array and the second equinox array obtained in the step S51:
TIRm＝∑iYs<pi,p-i>。
further, step S51 further includes: p is a radical ofi≥p-i。
Further, if pi<p-iThen, then
Figure BDA0002251165850000022
Further, the heart rate dynamic time irreversible in step S7 is specifically: the time irreversibility in the m + tau dimension state and the time irreversibility in the m dimension state.
Further, τ is 1.
The invention has the beneficial effects that: the invention provides an irreversible concept of dynamic time for the first time, and combines a multidimensional phase space and equivalent arrangement symbolization to effectively quantify the dynamic unbalance characteristics of a heart rate system; in view of the characteristics of multidimensional vectors in time irreversible analysis, the arrangement type is adopted to replace the original vectors, thereby effectively simplifying the time irreversible quantitative analysis. Because the symmetric vector means time reversibility, the invention adopts an equivalent arrangement type, more reliably represents the distribution of the vector, and in consideration of the existence of empty arrangement, the arrangement of some vectors does not have a corresponding form, and in order to effectively quantify the dynamic time irreversibility, a parameter Ys based on subtraction is adopted. The dynamic time irreversible parameter dTIR can correctly extract dynamic characteristics in a sequence in a chaotic model and a linear reversible Gaussian sequence, and effectively extract and identify the dynamic time irreversible characteristics of healthy young and old heart rates.
Drawings
FIG. 1 is a flow chart of a protocol of the present invention;
FIG. 2 is a diagram of Lorenz and Gaussian sequences and their dTIRs in place of data provided by an embodiment of the present invention;
FIG. 2(a) shows an unbalanced sequence constructed by a Lorenz chaos model and dTIR of the replaced data; FIG. 2(b) is a non-equilibrium sequence of Gaussian sequence constructs with its dTIR replacing the data;
FIG. 3 is a dTIR of healthy young and old heart rates provided by an embodiment of the present invention;
in which FIG. 3(a) is the dTIR for a healthy young heart rate and FIG. 3(b) is the dTIR for a healthy old heart rate.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
In order to facilitate understanding of the technical contents of the present invention, the following technical terms are now explained:
dynamic time irreversible dTIR
Time-invertible refers to the property that the features of a time series do not change in time-inversible order, and the time-irreversible quantization is of the positive and negative sequences { X (t)1),X(t2),…,X(tm) Measure in conjunction with the difference in probability distribution. The invention defines dynamic time irreversibility (dTIR) as the past and current state descending orderColumn { X (t)1),X(t2),…,X(tm),X(tm+1),…,X(tm+τ) The difference of the probability distribution and the current state sequence { X (t) } is1),X(t2),…,X(tm) Difference in probability difference.
Considering the influence of the past state of a real dynamic system, τ is usually 1. Since dynamic time-irreversible analysis involves the computation of joint probabilities, the sequence can be simplified by the symbols of the time sequence to simplify the probability estimation of the joint vector.
As shown in fig. 1, which is a flowchart of a scheme of the present invention, the method for analyzing irreversibility of heart rate dynamic time based on equivalence permutation of the present invention includes the following steps:
s1, giving a heart rate sequence y (t) ═ { x (1), x (2), …, x (L) }oflength L, and performing time reversal to obtain a reversed heart rate signal y (-t) ═ { x (L), …, x (2), x (1) }.
S2, carrying out phase space reconstruction on the sequential heart rate y (t) to obtain a multi-dimensional vector
Figure BDA0002251165850000041
Where m is the vector dimension, d is the delay,
Figure BDA0002251165850000042
at the same time, the reverse-order heart rate sequence y (-t) in step S1 is also reconstructed into a reverse-order multidimensional vector by phase space reconstruction
Figure BDA0002251165850000043
And S3, mapping the multidimensional vectors with the positive sequence and the negative sequence to the equivalent arrangement type.
The step S3 includes the following sub-steps
S31, carrying out multidimensional vector on heart rate
Figure BDA0002251165850000044
Are ordered from small to large, x (j)1)≤x(j2)≤…≤x(jm)，Constructing an initial arrangement type by using the sorted element coordinates to obtain pi' ═ j1,j2,…,jmAnd the arrangement type of the heart rate reverse-order multi-dimensional vectors is recorded as pi'-。
S32, carrying out equivalence processing on the heart rate arrangement type, arranging the equivalent elements in the vector according to the appearance order if the equivalent elements exist in the vector according to the step S31, and assuming that x (j) isi)＝x(ji+1) And x (j)l)＝x(jl+1)＝x(jl+2) The arrangement type is adjacent to the { ji,ji+1,…,jl,jl+1,jl+2Changing the coordinate corresponding to the equivalent element in the arrangement type into the minimum value of the coordinate in the corresponding equivalent, namely { j }i,ji,…,jl,jl,jlAnd if no equivalent elements exist in the vector, the arrangement type is unchanged.
S33, correcting the equivalent arrangement of the positive sequence heart rate signals to pi, and correcting the equivalent arrangement of the negative sequence to pi-。
S4, counting the arrangement types of the positive sequence heart rate and the negative sequence heart rate, and calculating the probability distribution of the arrangement of the positive sequence heart rate signals and the negative sequence heart rate signals, wherein the probability distribution is piAnd p-i；piRepresenting the probability of the ith type, p, in a positive-sequence equi-value permutation-iRepresenting the probability of the ith type in the reverse-order equi-valent permutation.
S5, calculating the probability difference of the positive sequence heart rate array and the negative sequence heart rate array to obtain m-dimensional time irreversible TIRmWhere i is the rank number of the permutation type probability, and p is assumed initiallyi≥p-iM-dimensional time-irreversible TIRmThe calculation formula is as follows:
Figure BDA0002251165850000045
if p isi<p-iThen p in the formula of this step is addediAnd p-iAnd (4) interchanging.
S6, increasing the dimension to m + tau, repeating the contents in the steps S2 to S5 to obtain the m + tau dimension time irreversible result TIRm+τ. Wherein i is the sequence number of the probability of the permutation type, and the pair formulaOf (a) to (b)i)-p(s-i) ' processing in the manner in step S5; m + tau dimensional time irreversible results TIRm+τThe calculation formula is as follows:
Figure BDA0002251165850000046
and S7, calculating m-dimensional dynamic time irreversibility of the heart rate sequence.
The effects of the present invention will be described below with reference to specific data:
in order to verify the validity of the dynamic time irreversible based on the equivalent permutation, the simulation experiment firstly verifies the matlab2017a software under the Windows operating system by using model data with known characteristics in the matlab software (the analysis result of the invention is not influenced by the operating system and the version of the matlab software), and then the simulation experiment is used for the dynamic time irreversible analysis of the heart rate.
The dTIR is first verified using the model data. Unbalanced sequences use the Lorenz chaotic model (dx/dt ═ σ (y-x), dy/dt ═ x (r-z) y, and dz/dt ═ xy-bz, with initial values set to x (r-z) y and xy-bz, respectively1＝0,y 10 and z1＝1*10-10Control parameters σ 10, b 8/3, and r 28), and the linear random sequence is a gaussian sequence with zero mean unit variance. And simultaneously, 300 sets of linear substitute data are constructed for the two sets of sequences by using a magnitude-phase random scrambling method, and the method provided by the invention is verified by comparing the model sequences with the dTIRs of the substitute data. The proof of theory of the substituted data is: the sequence is linear if the dTIR of the data is between 2.5% and 97.5% quantiles of its replacement data, and non-linear otherwise. Lorenz and the Gaussian sequence and its dTIR in place of the data are shown in FIG. 2. from FIG. 2(a), it can be seen that Lorenz's dTIR is higher than its 97.5% quantile in place of the data, while the Gaussian sequence (Gaussian) shown in FIG. 2(b) is always between 2.5% and 97.5% quantile. Since Lorenz sequence is nonLinear, while the gaussian process is linear, dTIR effectively characterizes the properties of both sets of sequences, and it is known that the isobaric permutation-based dynamic time-irreversible method of the present invention is effective.
The dynamic time of the heart rate signal is then analyzed irreversibly using dTIR. The present invention analyzes two heart rates of the Fantasia data set in the public database PhysioNet, including 20 healthy young and healthy elderly heart rates, respectively, with young people ranging in age from 21 to 34 years, with an average of 25.8 ± 4.3 years, elderly ranging in age from 68 to 85 years, and an average of 74.5 ± 4.4 years. The acquisition time of the electrocardiosignals is 120 minutes, and the sampling frequency is 250 Hz. In the process of permutation symbolization of the heart rate, the lengths of m and d determine the dynamic characteristics of sequences contained in permutations, however, in practical application, the selection of the parameter is mostly judged by adopting enumeration and trial and error methods, and m is 2 and 3, and d is 1 to 5 are selected in the analysis of the heart rate signal. dTIR for healthy young and old heart rates is shown in fig. 3, from which it can be seen that the dynamic time of the old heart rate is irreversibly lower than the healthy young heart rate. By examining the dTIR of the two groups of heart rates dTIR with an independent sample t of healthy young and old heart rates with m 2 as shown in fig. 3(a) and healthy young and old heart rates with m 3 as shown in fig. 3(b), it is known that dTIR can better and significantly distinguish the two groups of heart rates (p <0.05) when the phase space dimension m is 2, wherein the dynamic time of healthy young and old heart rates is not reversible with the best distinguishing effect (p 9.0E-5) when m 2 and d 2. The healthy young heart rate regulating system is in an optimal state, the heart rate signal has high dynamic characteristics, and the regulating capacity of the old heart rate system is reduced along with the increase of the age, so that the dynamic characteristics of the old heart rate system are also reduced. The dTIR of the present invention correctly and efficiently extracts and distinguishes between healthy young and old heart rates with an irreversible dynamic time, and is insensitive to the choice of m and d in the model and heart rate signal, and thus has a high robustness.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (8)

1. A heart rate dynamic time irreversibility analysis method based on equivalence permutation is characterized by comprising the following steps:
s1, taking the currently acquired heart rate signal as a positive sequence heart rate sequence, and carrying out time reverse sequence on the positive sequence heart rate sequence to obtain a reverse sequence heart rate sequence;
s2, performing m-dimensional phase space reconstruction on the positive sequence heart rate sequence to obtain a first m-dimensional vector; performing m-dimensional phase space reconstruction on the negative sequence heart rate sequence to obtain a second m-dimensional vector;
s3, performing equivalent arrangement symbolization processing on the first m-dimensional vector and the second m-dimensional vector;
s4, counting the probability distribution of the equal arrangement of the positive sequence heart rate sequence and the negative sequence heart rate sequence;
s5, calculating the time irreversibility in an m-dimensional state according to the probability distribution of the equal arrangement of the positive sequence heart rate sequence and the negative sequence heart rate sequence;
s6, increasing the dimension m to m + tau, and repeating the steps S2-S5 to calculate the time irreversibility under the dimension m + tau;
and S7, calculating the heart rate dynamic time irreversibility according to the time irreversibility in the m-dimensional state obtained in the step S5 and the time irreversibility in the m + tau-dimensional state obtained in the step S6.
2. The heart rate dynamics time irreversibility analysis method based on equivalence ranking as claimed in claim 1, wherein the step S3 specifically comprises: respectively carrying out the following equivalent arrangement symbolization processing procedures on the first m-dimensional vector and the second m-dimensional vector to obtain a first equivalent arrangement corresponding to the first m-dimensional vector and a second equivalent arrangement corresponding to the second m-dimensional vector:
s31, constructing an initial arrangement type according to element coordinates in the vector after the elements in the vector are sequenced from small to large, namely one coordinate in the initial arrangement type corresponds to one type;
s32, if equivalent elements exist in the vector, correcting all coordinates corresponding to the equivalent elements in the arrangement type into the smallest coordinate in the corresponding equivalent to obtain the corrected arrangement type; otherwise, the arrangement type is not changed;
and S33, taking the arrangement type obtained in the step S32 as equivalent arrangement corresponding to the vector.
3. The heart rate dynamics time irreversibility analysis method based on equivalence permutations as claimed in claim 2, wherein the step S4 specifically comprises: and respectively counting the probabilities of various types in the first equivalue permutation and the second equivalue permutation.
4. The heart rate dynamics time irreversibility analysis method based on equivalence ranking as claimed in claim 3, wherein the step S5 is calculated as:
s51, calculating probability differences of corresponding types in the first equi-value permutation and the second equi-value permutation:
Figure FDA0002251165840000011
wherein p isiRepresenting the probability of the ith type, p, in a first permutation of the values-iRepresenting the ith type probability in the second equi-valued permutation;
s52, calculating the time irreversibility in the m-dimensional state according to the probability differences of each type in the first equinox array and the second equinox array obtained in the step S51:
TIRm＝∑iYs<pi,p-i>。
5. the method for analyzing irreversibility of heart rate dynamics time based on equivalence ranking as claimed in claim 4, wherein step S51 further comprises: p is a radical ofi≥p-i。
6. A process according to claim 5The heart rate dynamic time irreversibility analysis method based on equivalence permutation is characterized in that if p isi<p-iThen, then
7. The method for analyzing irreversibility of heart rate dynamics time based on equivalence permutations according to any one of claims 1-6, wherein the heart rate dynamics time irreversibility in step S7 is specifically: the time irreversibility in the m + tau dimension state and the time irreversibility in the m dimension state.
8. The heart rate dynamics time irreversibility analysis method based on equivalence permutations according to claim 7, wherein τ is 1.
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