CN107928652B - Heart monitoring method based on pulse rate variability - Google Patents
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- 101100517648 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) NUM1 gene Proteins 0.000 claims description 3
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- A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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Abstract
The invention discloses a heart monitoring method based on pulse rate variability in the field of pulse rate variability analysis, which comprises the steps of obtaining a cardiac cycle and obtaining a cardiac cycle array, making a cardiac cycle array pulse rate scatter diagram and a disease pulse rate scatter diagram, making a specific comparison and determining whether the obtained scatter diagram belongs to a scatter diagram of a certain disease or not, giving suggestions according to results and the like. The invention analyzes the cardiac cycle through the accurate cardiac cycle captured by the continuous real-time pulse rate change, fully utilizes the pulse rate, can realize real-time, non-inductive and portable monitoring, can also meet the requirements of the public and provides effective suggestions.
Description
Technical Field
The invention relates to the field of pulse rate variability analysis, in particular to a heart monitoring method based on pulse rate variability.
Background
The current analysis technical means for the pulse rate variability are limited, and especially, the method is limited to the analysis of the single value of the pulse rate. With the development of science and technology, people have more accurate requirements on sustainable monitoring equipment, which is not only a simple pulse rate variability result, but also more important to know what health information the pulse rate variability can represent, and a new analysis method capable of obtaining the useful health state of a user is urgently required for the monitoring of the real-time continuous pulse rate variability and the analysis of the real-time continuous pulse rate variability.
Meanwhile, the heart rate variability obtained by monitoring the cardiac cycle of a human body through the electrocardiographic technology is very inconvenient in the field of real-time monitoring, is difficult to popularize in families, is difficult to meet the requirements of the public on portability and no sense, and can influence the sleep of a user particularly during the monitoring in the sleep process.
The above-mentioned drawbacks are worth solving.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a heart monitoring method based on pulse rate variability, which provides useful suggestions for the pertinence of a user by presenting a scatter diagram and analyzing the scatter diagram for the pulse rate variability of the user within a period of time.
The technical scheme of the invention is as follows:
a method of cardiac monitoring based on pulse rate variability comprising the steps of:
step 1, obtaining a specific cardiac cycle at a fixed detection frequency within a certain time period to obtain a cardiac cycle array A0;
step 2, making a corresponding cardiac cycle array pulse rate scattergram according to the cardiac cycle array, and making a pulse rate scattergram of diseases;
step 3, specifically comparing the obtained cardiac cycle array pulse rate scatter diagram with the pulse rate scatter diagram of the disease, and determining whether the obtained cardiac cycle array pulse rate scatter diagram belongs to the scatter diagram of the disease;
and 4, obtaining the final state of the pulse rate scatter diagram of the user according to the comparison result obtained in the previous step, giving a reasonable suggestion to the user, and suggesting the user to go to a hospital for a doctor in time if the pulse rate scatter diagram of the user is in an abnormal state.
The present invention according to the above aspect is characterized in that, in step 1, if N cardiac cycles are acquired in one minute within a time period set to Ts to Te, a length array of N × (Te-Ts) × 60 is required to store the results of all acquired cardiac cycles, which is a cardiac cycle array a 0.
Further, in the step 2, in the pulse rate scattergram corresponding to the cardiac cycle array, the first value of the cardiac cycle array a0 to nx (Te-Ts) × 60-1 is taken as the abscissa, and the second value of the cardiac cycle array a0 to nx (Te-Ts) × 60 is taken as the ordinate.
The invention according to the above scheme is characterized in that in the detection process of step 1, the amount of exercise of the wrist of the human body is judged, if the amount of exercise of the monitoring bracelet exceeds a certain threshold, the hand of the human body does not meet the measurement condition at the current measurement time, and the last measurement result is retained in the detection result.
The present invention according to the above aspect is characterized in that, in the step 2, the disease pulse rate scattergram includes a disease specific pulse rate scattergram and a normal state specific pulse rate scattergram, and specifically, in a corresponding state, at least 20 pulse rate scattergrams in the same type state are acquired, and the characteristics thereof are analyzed to establish the specific pulse rate scattergram.
The invention according to the above scheme is characterized in that according to the obtained cardiac cycle array pulse rate scattergram and the pulse rate scattergram of the disease, the judgment parameter of the cardiac cycle array pulse rate scattergram and the judgment parameter of the specific pulse rate scattergram are obtained through calculation, the judgment parameters are compared, and the type of the cardiac cycle array pulse rate scattergram is judged through a clustering method.
Further, the calculation process of the judgment parameters of the pulse rate scattergram of the cardiac cycle array specifically comprises the following steps:
(1) in the heart cycle array pulse rate scatter diagram, points corresponding to the abscissa are respectively taken as a vertical line L0 towards a diagonal function Y-X, and points scattered on the heart cycle array pulse rate scatter diagram L0 are sequentially found from the points corresponding to the abscissa and stored in an array A1;
(2) sequentially drawing a circle Rn by taking points in the array A1 as the center of the circle and taking 50 as the radius, judging the pulse rate point number in the Rn to be NUM, and storing the pulse rate point numbers NUM in all the Rn in the array A2 n;
(3) sequentially judging NUM (number of NUM) of all A2n not equal to zero, and counting the number of NUM (number of NUM) of all A2n larger than 10 as NUM 1;
(4) sequentially judging the distances of all points A2n with NUM larger than 5, and storing the distances in A3 n;
(5) accumulating NUM with the point number more than 5 in A2n in sequence to obtain NUM _ total;
(6) sorting the numerical values in A3n in a descending order, and finding out the maximum value to be marked as Lmax;
(7) comparing Lmax, NUM1 and NUM _ total which can represent the characteristics of the pulse rate scatter diagram with the parameters of the scatter diagram in the pulse rate scatter diagram database;
(8) according to the judgment method of cluster analysis, the similarity of the parameters of the pulse rate scatter diagram in the pulse rate scatter diagram database is calculated, and the state of the corresponding pulse rate scatter diagram with the highest similarity is taken as a final result.
Further, in the step (8), the type of the pulse rate scatter diagram in the similar mapping database is judged by the euclidean distance.
Furthermore, in the specific determination process, the degree of similarity between the obtained parameters and the parameters of the pulse map in the data is calculated, that is, the euclidean distance between the obtained parameters and the parameters of the pulse map in the data is calculated to obtain the mark matrix M,
mn ═ m (Distance _ Lmaxn, Distance _ NUM1N, Distance _ NUM _ totalin), where N ═ 1, 2, 3 … N;
and drawing circles on all the values of the M matrix respectively, calculating the areas of all the circles, and determining the type corresponding to the circle with the minimum area.
The invention according to the scheme has the advantages that:
the invention fully utilizes the pulse rate by capturing the accurate cardiac cycle through the change of the continuous real-time pulse rate and by a special analysis means of the cardiac cycle.
The invention adopts the pulse frequency of the pulse, can accurately calculate the cardiac cycle, is simpler and more convenient than the method for detecting the cardiac cycle by electrocardio, does not need to be pasted with more electrode plates, is suitable for real-time measurement and can not influence the sleep quality in the real-time measurement at night.
The invention can realize real-time, non-inductive and portable monitoring, can also meet the requirements of the public and provides effective suggestions.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention.
Fig. 2 is a schematic diagram of the calculation of the cardiac cycle of the present invention.
FIG. 3 is a pulse rate scattergram of the present invention.
Fig. 4 is a schematic diagram of the invention plotting Y ═ X perpendicular in a pulse rate scattergram.
Fig. 5 is a schematic diagram of a pulse rate scattergram according to the present invention, in which a circle is drawn with a center 50 of a point of an a1 array as a radius.
Detailed Description
The invention is further described with reference to the following figures and embodiments:
as shown in fig. 1, a heart monitoring method based on pulse rate variability includes the following steps:
1. as shown in fig. 2, a specific cardiac cycle is acquired at a fixed detection frequency over a period of time, resulting in an array of cardiac cycles a 0. In fig. 2, R refers to a peak of the pulse rate waveform and the R-R interval refers to a cardiac cycle.
A certain time period value refers to a certain time period within 0 to 24 hours (e.g., 0 o 'clock to 8 o' clock); the fixed detection frequency means that N cardiac cycles are acquired in one minute, and through experimental verification, 50 to 59 cardiac cycles are acquired in one minute. In the present embodiment, if N cardiac cycles are acquired in one minute during the time period set to Ts to Te, an array of lengths of N × (Te-Ts) × 60 is required to store the results of all acquired cardiac cycles, which is cardiac cycle array a 0.
In the detection process, whether the cardiac cycle result is within a preset normal value range or not is judged, in order to ensure that the cardiac cycle result is within the preset normal value range as much as possible, the motion amount of the wrist of the human body needs to be judged firstly, if the motion amount of the monitoring bracelet exceeds a certain threshold (namely the motion amount of the monitoring bracelet reaches 3 per second), the hand motion of the human body at the current measurement moment does not meet the measurement condition, and the last measurement result is reserved in the detection result.
2. As shown in fig. 3, a corresponding cardiac cycle array pulse rate scattergram is created from the cardiac cycle array, and a pulse rate scattergram of a disease is created.
In the pulse rate scatter diagram corresponding to the cardiac cycle array, the first value of the cardiac cycle array A0 to N x (Te-Ts) x 60-1 values are used as abscissa, and the second value of the cardiac cycle array A0 to N x (Te-Ts) x 60 values are used as ordinate, and the unit is the value of the cardiac cycle.
The pulse rate scatter diagram of the disease comprises a specific pulse rate scatter diagram of the disease and a specific pulse rate scatter diagram under a normal state, specifically under the corresponding state, at least 20 pulse rate scatter diagrams under the same state are obtained, the characteristics of the pulse rate scatter diagrams are analyzed, and the specific pulse rate scatter diagram is established (the manufacturing method is the same as that of the pulse rate scatter diagram of the cardiac cycle array). The shapes of the pulse rate scattergrams in different states are different, and the shapes of the scattergrams include a comet shape, a rocket shape, a fan shape, a short bar shape and the like, wherein the comet shape corresponds to a specific pulse rate scattergram in a normal state, and the other shapes correspond to specific pulse rate scattergrams in a non-normal state.
3. And specifically comparing the obtained cardiac cycle array pulse rate scatter diagram with the pulse rate scatter diagram of the disease to determine whether the obtained cardiac cycle array pulse rate scatter diagram belongs to the scatter diagram of the disease.
As shown in fig. 4-5, in this embodiment, according to the obtained cardiac cycle array pulse rate scattergram and the pulse rate scattergram of the disease, the judgment parameter of the cardiac cycle array pulse rate scattergram and the judgment parameter of the specific pulse rate scattergram are obtained by calculation, and the two are compared, and which category the cardiac cycle array pulse rate scattergram belongs to is judged by a clustering method. Specifically, the method comprises the following steps:
(1) in the heart cycle array pulse rate scattergram, points corresponding to the abscissa from 1 to nx (Te-Ts) × 60-1 are respectively marked as vertical lines L0(L0 refers to a set of the abscissa with vertical lines, and the maximum number is N × (Te-Ts) × 60-1) to a diagonal function Y ═ X, and points scattered on the heart cycle array pulse rate scattergram at L0 are sequentially found from 1 to nx (Te-Ts) × 60-1 and stored in an array a1, and in fig. 4-5, "vertical line 1", "vertical line 2" and "vertical line" are all one line in L0.
(2) In the direction of the solid arrow in fig. 5, a circle Rn (a circle indicated by a dotted line in fig. 5) is drawn with a point in the array a1 as the center and 50 as the radius in this order, the number of pulse rate points in Rn is determined to be NUM, and the number of pulse rate points NUM in all Rn is stored in the array A2 n.
(3) And sequentially judging NUM (number of NUM) of all A2n not equal to zero, and counting the number of NUM (number of NUM) of all A2n larger than 10 as NUM 1.
(4) The distances of all the points of A2n with NUM larger than 5 are sequentially judged and stored in A3 n.
(5) And sequentially accumulating NUM with the point number larger than 5 in A2n to obtain NUM _ total.
(6) Sorting the values in A3n in descending order to find the largest value as Lmax.
(7) Lmax, NUM1, and NUM _ total, which are representative of the characteristics of the pulse rate scattergram (i.e., the characteristics of the different pulse rate scattergrams), are compared with the parameters of the scattergram in the pulse rate scattergram database.
(8) According to the judgment method of the cluster analysis, the similarity of the parameters of the pulse rate scatter diagram in the pulse rate scatter diagram database (comprising the disease pulse rate scatter diagram and the normal pulse rate scatter diagram) is calculated, and the state of the corresponding pulse rate scatter diagram with the highest similarity in the pulse rate scatter diagram database is taken as a final result. Specifically, the type of the pulse rate scatter diagram in the similar mapping database is judged through the Euclidean distance. In the specific judgment process, the similarity degree between the obtained parameters and the parameters of the pulse map in the data is calculated, namely the Euclidean distance between the obtained parameters and the parameters of the pulse map in the data is calculated to obtain a marking matrix M, Mn＝(Distance_Lmaxn，Distance_NUM1n，Distance_NUM_totaln) Wherein N is 1, 2, 3 … N; and drawing circles on all the values of the M matrix respectively, calculating the areas of all the circles, and determining the type corresponding to the circle with the minimum area.
4. And according to the comparison result obtained in the last step, obtaining the final state of the pulse rate scatter diagram of the user, giving a reasonable suggestion to the user, and if the pulse rate scatter diagram of the user is in an abnormal state, suggesting the user to go to a hospital for a doctor in time.
The characteristic parameter used by the invention is an analysis method of the pulse rate, the pulse rate refers to the beating frequency of the pulse, the cardiac cycle can be accurately calculated, and the method of detecting the cardiac cycle by relative electrocardio is simpler and more convenient, does not need to be pasted with more electrode slices, is suitable for real-time measurement and does not influence the sleep quality in real-time measurement at night.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
The invention is described above with reference to the accompanying drawings, which are illustrative, and it is obvious that the implementation of the invention is not limited in the above manner, and it is within the scope of the invention to adopt various modifications of the inventive method concept and technical solution, or to apply the inventive concept and technical solution to other fields without modification.
Claims (4)
1. A method of cardiac monitoring based on pulse rate variability comprising the steps of:
step 1, obtaining a specific cardiac cycle at a fixed detection frequency within a certain time period to obtain a cardiac cycle array A0;
step 2, making a corresponding cardiac cycle array pulse rate scattergram according to the cardiac cycle array, and making a disease pulse rate scattergram, wherein the disease pulse rate scattergram comprises a disease specific pulse rate scattergram and a specific pulse rate scattergram in a normal state, specifically obtaining at least 20 pulse rate scattergrams in the same state in the corresponding state, analyzing the characteristics of the pulse rate scattergrams, and establishing the specific pulse rate scattergram;
step 3, specifically comparing the obtained cardiac cycle array pulse rate scattergram with the pulse rate scattergram of a disease, determining whether the obtained cardiac cycle array pulse rate scattergram belongs to the scattergram of a certain disease, obtaining a judgment parameter of the cardiac cycle array pulse rate scattergram and a judgment parameter of the specific pulse rate scattergram according to the obtained cardiac cycle array pulse rate scattergram and the pulse rate scattergram of the disease, comparing the judgment parameters with the judgment parameter of the specific pulse rate scattergram, judging which type the cardiac cycle array pulse rate scattergram belongs to by a clustering method, wherein the calculation process of the judgment parameters of the cardiac cycle array pulse rate scattergram specifically comprises the following steps:
(1) in the heart cycle array pulse rate scatter diagram, points corresponding to the abscissa are respectively taken as a vertical line L0 towards a diagonal function Y-X, and points scattered on the heart cycle array pulse rate scatter diagram L0 are sequentially found from the points corresponding to the abscissa and stored in an array A1;
(2) sequentially drawing a circle Rn by taking points in the array A1 as the center of the circle and taking 50 as the radius, judging the pulse rate point number in the Rn to be NUM, and storing the pulse rate point numbers NUM in all the Rn in the array A2 n;
(3) sequentially judging NUM (number of NUM) of all A2n not equal to zero, and counting the number of NUM (number of NUM) of all A2n larger than 10 as NUM 1;
(4) sequentially judging the distances of all points A2n with NUM larger than 5, and storing the distances in A3 n;
(5) accumulating NUM with the point number more than 5 in A2n in sequence to obtain NUM _ total;
(6) sorting the numerical values in A3n in a descending order, and finding out the maximum value to be marked as Lmax;
(7) comparing Lmax, NUM1 and NUM _ total which can represent the characteristics of the pulse rate scatter diagram with the parameters of the scatter diagram in the pulse rate scatter diagram database;
(8) according to a judgment method of cluster analysis, calculating the similarity of the parameters of the pulse rate scatter diagram in the pulse rate scatter diagram database, and taking the state of the corresponding pulse rate scatter diagram with the highest similarity as a final result;
and 4, obtaining the final state of the pulse rate scatter diagram of the user according to the comparison result obtained in the previous step, giving a reasonable suggestion to the user, and suggesting the user to go to a hospital for a doctor in time if the pulse rate scatter diagram of the user is in an abnormal state.
2. The method for cardiac monitoring based on pulse rate variability according to claim 1, wherein in step 1, if N cardiac cycles are acquired in one minute during the time period set to Ts to Te, then a length array of N x (Te-Ts) x 60 is needed to store the results of all acquired cardiac cycles, which is cardiac cycle array a 0.
3. The method for cardiac monitoring based on pulse rate variability according to claim 2, wherein in the step 2, the first value of the cardiac cycle array a0 to nx (Te-Ts) x 60 "1 is plotted as the abscissa and the second value of a0 to nx (Te-Ts) x 60 is plotted as the ordinate in the pulse rate scattergram corresponding to the cardiac cycle array.
4. The method for cardiac monitoring based on pulse rate variability according to claim 1, wherein in the step (8), the type of pulse rate scatter plot in the similar mapping database is judged by Euclidean distance, and in the specific judgment process, the degree of similarity between the obtained parameters and the parameters of the pulse pattern in the data is calculated, namely the Euclidean distance between the obtained parameters and the parameters of the pulse pattern in the data is calculated to obtain the mark matrix M,
Mn＝(Distance_Lmaxn，Distance_NUM1n，Distance_NUM_totaln) Wherein N is 1, 2, 3 … N;
and drawing circles on all the values of the M matrix respectively, calculating the areas of all the circles, and determining the type corresponding to the circle with the minimum area.
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