CN109589103B - Blood pressure data processing method - Google Patents

Blood pressure data processing method Download PDF

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CN109589103B
CN109589103B CN201811615982.XA CN201811615982A CN109589103B CN 109589103 B CN109589103 B CN 109589103B CN 201811615982 A CN201811615982 A CN 201811615982A CN 109589103 B CN109589103 B CN 109589103B
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金涛
江浩
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BEIJING LI'AN SHENGHUA TECHNOLOGY Co.,Ltd.
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Abstract

The invention provides a blood pressure data processing method which comprises the steps of acquiring an acquired diastolic pressure discrete data sequence { dp }iAnd a sequence of systolic discrete data { sp }i}; determining the diastolic discrete data sequence { dp)iAnd a sequence of systolic discrete data { sp }iInvalid data in (c); obtaining an estimated value corresponding to the position of the invalid data; the estimated values are based on a sequence of discrete data of diastolic pressure { dp }iCorresponding cumulative discrete sequence and systolic discrete data sequence spiCalculating a corresponding accumulated discrete sequence; replacing the invalid data with the estimated value corresponding to the position of the invalid data to obtain an effective diastolic discrete data sequence { dpe }iAnd the effective systolic discrete data sequence { spe }i}; discrete data sequence { dpe according to the effective diastolic pressureiAnd the effective systolic discrete data sequence { spe }i}. The invention provides a specific method for collecting, processing and judging blood pressure related data, which can realize automatic and accurate blood pressure judgment.

Description

Blood pressure data processing method
Technical Field
The invention relates to the field of data processing, in particular to a blood pressure data processing method.
Background
Blood pressure abnormalities cause various degrees of damage to the individual organs of the human body, and therefore, it is necessary to monitor the blood pressure in a timely manner. The collection of blood pressure is divided into diastolic pressure collection and systolic pressure collection, so that strong dependence exists on human labor in the collection process of blood pressure data, and the research on the aspects of judgment of the validity of the collected data, data estimation based on the collected data and the like is relatively insufficient.
Disclosure of Invention
In order to solve the technical problem, the invention provides a blood pressure data processing method. The invention is realized by the following technical scheme:
a blood pressure data processing method, comprising:
acquiring an acquired diastolic pressure discrete data sequence { dpiAnd a sequence of systolic discrete data { sp }i}；
Determining the diastolic discrete data sequence { dp)iAnd a sequence of systolic discrete data { sp }iInvalid data in (c);
position of invalid dataA corresponding estimate value; the estimated values are based on a sequence of discrete data of diastolic pressure { dp }iCorresponding cumulative discrete sequence and systolic discrete data sequence spiCalculating a corresponding accumulated discrete sequence;
replacing the invalid data with the estimated value corresponding to the position of the invalid data to obtain an effective diastolic discrete data sequence { dpe }iAnd the effective systolic discrete data sequence { spe }i}。
Further, the obtaining of the estimation value corresponding to the position of the invalid data includes:
obtaining the discontinuous discrete data sequence { x after the invalid data is removed from the original continuous discrete data sequenceiThe subscripts of the discontinuous discrete data sequence elements do not comprise data subscripts to be estimated, and the estimated data subscripts are subscripts corresponding to invalid data;
according to the formula
Figure BDA0001925832700000021
Computing the non-contiguous discrete data sequence { xiCorresponding discontinuous cumulative discrete sequence
Figure BDA0001925832700000022
Accumulating discrete sequences at said discontinuity
Figure BDA0001925832700000023
Inserting four internal adding points between every two adjacent points at equal intervals to obtain a reference discrete sequence;
constructing a signature sequence from the reference discrete sequence
Figure BDA0001925832700000024
Calculating a first parameter matrix S and a second parameter matrix X according to the characteristic sequence;
calculating estimation parameters a and b according to the first parameter matrix S and the second parameter matrix X;
calculating a fitting function according to the estimated parameters a, b
Figure BDA0001925832700000025
According to the fitting function
Figure BDA0001925832700000026
And calculating an estimation value corresponding to the subscript to be estimated in the original continuous discrete data sequence by using the subscript to be estimated.
Further, comprising:
the specific value of the internal increment point is obtained by the following formula:
Figure BDA0001925832700000031
Figure BDA0001925832700000032
Figure BDA0001925832700000033
Figure BDA0001925832700000034
further, the first parameter matrix S is obtained according to the formula
Figure BDA0001925832700000035
Obtaining, the second parameter matrix X is obtained according to the formula
Figure BDA0001925832700000036
Where l is the maximum subscript of the non-contiguous discrete data sequence.
Further, the estimated parameters a, b are represented by the formula (a, b)T＝(STS)-1STAnd X is obtained by calculation.
Further, the fitting function is based on
Figure BDA0001925832700000037
And calculating an estimation value corresponding to the subscript to be estimated in the original continuous discrete data sequence by using the subscript to be estimated comprises the following steps:
acquiring a subscript t to be estimated;
calculating a fitting function
Figure BDA0001925832700000038
First correlation value of
Figure BDA0001925832700000039
And a second correlation value
Figure BDA00019258327000000310
And taking the difference between the first correlation value and the second correlation value as an estimated value.
The embodiment of the invention provides a blood pressure data processing method which can automatically realize the collection of blood pressure data, the illegal value processing of the blood pressure data and the estimation of the blood pressure data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a blood pressure data processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for invalid data acquisition according to an embodiment of the present invention;
FIG. 3 is a flow chart of an estimation method according to an embodiment of the present invention;
FIG. 4 is a flow chart of a specific calculation method of the estimated value according to an embodiment of the present invention;
fig. 5 is a flowchart of a method for acquiring systolic pressure and diastolic pressure according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a blood pressure data processing method, which comprises the following steps of:
s101, acquiring a collected diastolic pressure discrete data sequence { dpiAnd a sequence of systolic discrete data { sp }i}；
S102, obtaining the diastolic pressure discrete data sequence { dpi} and the systolic discrete data sequence { spiA corresponding second characteristic number characterizing a representative value for determining a sequence data state of a discrete data sequence at a position of the characteristic number in the discrete data sequence.
S103, according to the first characteristic numberAnd said sequence of discrete data of diastolic pressure { dpiGet the first reference discrete data sequence { dpy }i-discretizing a data sequence { sp) according to said second characteristic number and said systolic pressureiGet the second reference discrete data sequence { spy }i}。
S104, acquiring the first reference discrete data sequence { dpyiA corresponding third characteristic number and the second reference discrete data sequence { spy }iA corresponding fourth characteristic number.
S105, determining the diastolic discrete data sequence { dp) according to the first characteristic number and the third characteristic numberiInvalid data in (c); determining the systolic discrete data sequence { sp according to the second characteristic number and the fourth characteristic numberiInvalid data in (c).
S106, according to the diastolic pressure discrete data sequence { dpiEstimating said diastolic discrete data sequence { dp }iA substitute value for invalid data in (j); discrete data sequence { sp according to the systolic pressureiEstimating the systolic discrete data sequence { sp }iSubstitute value of invalid data in.
S107, replacing invalid data with the substitute value of the invalid data to obtain a valid diastolic discrete data sequence { dpe }iAnd the effective systolic discrete data sequence { spe }i}。
Further, still include:
s108, dispersing data sequences { dpe according to the effective diastolic pressureiAnd the effective systolic discrete data sequence { spe }iJudging whether the risk of hypertension or hypotension exists; and if so, giving a prompt.
Specifically, the effective systolic discrete data sequence { spe ] is extractediIs greater than 140, and calculates the discrete data sequence { spe } of the effective systolic blood pressureiJudging that the risk of hypertension exists if the occurrence frequency is greater than a preset threshold value; and/or extracting discrete data sequences { dpe) of effective diastolic blood pressureiGreater than 90 of the sequence { dpe } and calculating its variance in the effective diastolic pressure discrete data sequence { dpe }iFrequency of occurrence inAnd if the occurrence frequency is greater than a preset threshold value, judging that the risk of hypertension exists.
Specifically, the effective systolic discrete data sequence { spe ] is extractediThe data smaller than 90 and calculating the discrete data sequence { spe ] at the effective systolic blood pressureiJudging that the risk of hypotension exists if the occurrence frequency is larger than a preset threshold value; and/or extracting discrete data sequences { dpe) of effective diastolic blood pressureiData less than 60 and calculating its discrete data sequence at said effective diastolic pressure { dpe }iAnd (4) judging that the risk of hypotension exists if the occurrence frequency is greater than a preset threshold value.
Specifically, in this embodiment of the present invention, the first feature number, the second feature number, the third feature number, and the fourth feature number are calculated by using the same method, and taking the method for acquiring the first feature number as an example, the method includes: discrete data sequences { dp) of diastolic blood pressure in order of absolute value from top to bottomiSorting; if the diastolic discrete data sequence { dpiIf the sequence is an odd sequence, taking the data ranked in the middle as a first feature number; if the diastolic discrete data sequence { dpiIf it is an even number, the average value of two data ranked in the middle is taken as the first feature number.
Specifically, in the embodiment of the invention, the first reference discrete data sequence { dpy }iAnd a second reference discrete data sequence { spy }iThe acquisition methods are the same, and a first reference discrete data sequence { dpy }iAs an example, include: the first reference discrete data sequence { dpyiEach element in the } satisfies the formula:
Figure BDA0001925832700000061
wherein
Figure BDA0001925832700000062
A first feature value is identified.
Further, determining the diastolic discrete data sequence { dp) according to the first characteristic number and the third characteristic numberiInvalid data in (f) and according to saidThe second characteristic number and the fourth characteristic number determine the systolic discrete data sequence { sp }iThe invalid data in (d) can adopt the same invalid data judgment method to determine the diastolic discrete data sequence { dp) according to the first characteristic number and the third characteristic numberiThe invalid data in the data are, for example, as shown in fig. 2, including:
acquiring the diastolic discrete data sequence { dp)iThe dispersion of each element in the lattice, the dispersion being according to a formula
Figure BDA0001925832700000071
Where K is the discrete decision coefficient. The value in the embodiment of the invention is 0.7; lambda is the third characteristic number, Q, P are respectively the discrete data sequence of diastolic pressure { dpiAnd a first reference discrete data sequence { dpy }iStandard deviation of, e is the natural logarithm;
and if the dispersion exceeds a preset threshold value, determining that the data is invalid. In the embodiment of the present invention, the preset threshold value is 14.
It should be emphasized that the dispersion determination formula, the dispersion determination coefficient and the preset threshold in the embodiment of the present invention are set by studying the variation rule of the diastolic pressure and the systolic pressure and various situations of abnormality during the acquisition of the diastolic pressure and the systolic pressure in the embodiment of the present invention, and are not arbitrarily changed.
In particular, according to said diastolic discrete data sequence { dp)iEstimating said diastolic discrete data sequence { dp }iA substitute value for invalid data in { sp } and, according to said systolic blood pressure discrete data sequence { spiEstimating the systolic discrete data sequence { sp }iThe methods for replacing the invalid data in the data are the same, and the invalid data are obtained by adopting the data estimation method provided by the embodiment of the invention. In a specific implementation process, the invalid data is removed, the corresponding position of the invalid data is used as a position parameter for processing, a support vector machine is adopted to estimate the position parameter, and the estimation method can use the prior art, so that the embodiment of the invention is not specifically limited.
In order to obtain a more accurate estimation value, an embodiment of the present invention further discloses an estimation method, as shown in fig. 3, including:
s10, acquiring a discontinuous discrete data sequence { x ] with the original continuous discrete data sequence and eliminating invalid dataiAnd the subscripts of the non-continuous discrete data sequence elements do not comprise data subscripts to be estimated, and the estimated data subscripts are subscripts corresponding to invalid data.
For example, for a discrete data sequence x comprising 10 elementsiIf the elements with subscripts of 3 and 6 are eliminated as invalid data, the obtained discontinuous discrete data sequence is x0，x1，x2，x4，x5，x7，x8，x9。
S20, according to a formula
Figure BDA0001925832700000081
Computing the non-contiguous discrete data sequence { xiCorresponding discontinuous cumulative discrete sequence
Figure BDA0001925832700000082
S30, in the discontinuous accumulative discrete sequence
Figure BDA0001925832700000083
And inserting four internal adding points between every two adjacent points at equal intervals to obtain a reference discrete sequence.
Specifically, the specific value of the internal increment point is obtained by the following formula
Figure BDA0001925832700000084
Figure BDA0001925832700000085
Figure BDA0001925832700000086
Figure BDA0001925832700000087
S40, constructing a characteristic sequence according to the reference discrete sequence
Figure BDA0001925832700000088
And S50, calculating a first parameter matrix S and a second parameter matrix X according to the characteristic sequence.
Specifically, the first parameter matrix S is obtained according to a formula
Figure BDA0001925832700000089
Obtaining, the second parameter matrix X is obtained according to the formula
Figure BDA0001925832700000091
Where l is the maximum subscript of the non-contiguous discrete data sequence.
And S60, calculating estimation parameters a and b according to the first parameter matrix S and the second parameter matrix X.
Specifically, the estimated parameters a, b are represented by the formula (a, b)T＝(STS)-1STAnd X is obtained by calculation.
S70, calculating a fitting function according to the estimation parameters a and b
Figure BDA0001925832700000092
In particular, the amount of the solvent to be used,
Figure BDA0001925832700000093
s80, according to the fitting function
Figure BDA0001925832700000094
And calculating an estimation value corresponding to the subscript to be estimated in the original continuous discrete data sequence by using the subscript to be estimated.
Specifically, the embodiment of the present invention provides a specific calculation method of an estimated value, as shown in fig. 4, including:
s801, obtaining a subscript t to be estimated.
S802, calculating a fitting function
Figure BDA0001925832700000095
First correlation value of
Figure BDA0001925832700000096
And a second correlation value
Figure BDA0001925832700000097
And S803, taking the difference between the first correlation value and the second correlation value as an estimated value.
Further, an embodiment of the present invention provides a method for acquiring systolic pressure and diastolic pressure, as shown in fig. 5, including:
s201, calculating a pulse wave signal curve according to the collected pulse wave signals.
S202, calculating an envelope curve of the pulse wave signal curve.
S203, locating a peak value point M on the envelope line.
And S204, positioning a systolic pressure representative point S between the envelope initial point A and the peak point M according to a preset algorithm, and positioning a diastolic pressure representative point D between the envelope peak point M and the envelope middle point B.
Specifically, the preset algorithm conforms to a formula
Figure BDA0001925832700000101
Wherein | S | is the ordinate of the point S on the envelope, | M | is the ordinate of the point M on the envelope, and | D | is the ordinate of the point D on the envelope. Wherein K1,K2The filtering parameter and the amplifying parameter in the process of acquiring the pulse wave signal are related and are a fixed numerical value.
And S205, projecting M, S, D on the static pressure curve to obtain diastolic pressure and systolic pressure.
The embodiment of the invention provides a blood pressure data processing method which can automatically realize the collection of blood pressure data, the illegal value processing of the blood pressure data and the estimation of the blood pressure data.
It should be understood that reference to "a plurality" herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (2)

1. A method for processing blood pressure data, comprising:
acquiring an acquired diastolic pressure discrete data sequence { dpiAnd a sequence of systolic discrete data { sp }i}；
Determining the diastolic discrete data sequence { dp)iAnd a sequence of systolic discrete data { sp }iInvalid data in (c);
obtaining an estimated value corresponding to the position of the invalid data; the estimated values are based on a sequence of discrete data of diastolic pressure { dp }iCorresponding cumulative discrete sequence and systolic discrete data sequence spiCalculating a corresponding accumulated discrete sequence;
replacing the invalid data with the estimated value corresponding to the position of the invalid data to obtain an effective diastolic discrete data sequence { dpe }iAnd the effective systolic discrete data sequence spei}；
The obtaining of the estimation value corresponding to the position of the invalid data includes:
obtaining the discontinuous discrete data sequence { x after the invalid data is removed from the original continuous discrete data sequenceiThe subscripts of the discontinuous discrete data sequence elements do not comprise data subscripts to be estimated, and the data subscripts to be estimated are subscripts corresponding to invalid data;
according to the formula
Figure FDA0003048118310000011
Computing the non-contiguous discrete data sequence { xiCorresponding discontinuous cumulative discrete sequence
Figure FDA0003048118310000012
Accumulating discrete sequences at said discontinuity
Figure FDA0003048118310000013
Inserting four internal adding points between every two adjacent points at equal intervals to obtain a reference discrete sequence;
constructing a signature sequence from the reference discrete sequence
Figure FDA0003048118310000014
Calculating a first parameter matrix S and a second parameter matrix X according to the characteristic sequence;
calculating estimation parameters a and b according to the first parameter matrix S and the second parameter matrix X;
calculating a fitting function according to the estimation parameters a and b;
according to the fitting function
Figure FDA0003048118310000021
Calculating an estimation value corresponding to the subscript to be estimated in the original continuous discrete data sequence according to the subscript to be estimated;
whereinThe first parameter matrix S is obtained according to the formula
Figure FDA0003048118310000022
Obtaining, the second parameter matrix X is obtained according to the formula
Figure FDA0003048118310000023
Obtaining, wherein l is the maximum subscript of the non-continuous discrete data sequence; the estimated parameters a, b are represented by the formula (a, b)T＝(STS)-1STCalculating to obtain X;
according to the fitting function
Figure FDA0003048118310000024
And calculating an estimation value corresponding to the subscript to be estimated in the original continuous discrete data sequence by using the subscript to be estimated comprises the following steps:
acquiring a subscript t to be estimated;
calculating a fitting function
Figure FDA0003048118310000025
First correlation value of
Figure FDA0003048118310000026
And a second correlation value
Figure FDA0003048118310000027
And taking the difference between the first correlation value and the second correlation value as an estimated value.
2. The method of claim 1, comprising:
the specific value of the internal increment point is obtained by the following formula:
Figure FDA0003048118310000031
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CN104853672A (en) * 2012-07-20 2015-08-19 恩多菲斯控股有限公司 Transducer interface system and method
CN107252313A (en) * 2017-05-25 2017-10-17 深圳市卡迪赛克科技有限公司 The monitoring method and system of a kind of safe driving, automobile, readable storage medium storing program for executing

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