CN112971792B - Individual state monitoring and analyzing method and equipment based on continuous physiological data - Google Patents
Individual state monitoring and analyzing method and equipment based on continuous physiological data Download PDFInfo
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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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
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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
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
- A61B5/7289—Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
Abstract
The application discloses a continuous physiological data-based individualized state monitoring analysis method, which comprises the following steps: finding n index vectors which are closest to the input vector in an index database G to form a matrix D; for each index vector x of matrix D j Finding the n index vectors closest to the n index vectors and forming a matrix D j (ii) a Calculating a difference between the input vector and its estimated vector as a first difference V re (ii) a Calculating each index vector x in the matrix D j And its estimated vector V jest And form a difference library G re (ii) a Calculating difference library G re Mean value of (a) 1 Sum variance Σ 1 (ii) a Calculating difference library G re Mahalanobis distance squared maximum h: calculating a lambda value of a chi-square distribution 0.95 quantile point with the degree of freedom k; calculating to obtain an approximate real covariance matrix; calculating the Mahalanobis distance h re (ii) a The specific value of SCI is then calculated by the chi-squared cumulative distribution function with the degree of freedom k.
Description
Technical Field
The invention relates to a technology for monitoring and analyzing human physiological parameters, in particular to a method and equipment for acquiring basic physiological signals of a registration object through wearable equipment, learning continuous physiological signals and further quantitatively analyzing physiological state changes.
Background
The guardian technology has emerged in the last century. From early bedside monitoring, to mobile monitoring, and to current wearable physiological monitoring. In the field of physiological monitoring, there is an important concept of how to characterize changes in physiological state and quantify the changes in physiological state. The traditional method usually relies on human experience to observe the change of absolute value of one or more physiological indexes, and when the observed indexes exceed a certain threshold, an observer can think that the state is abnormal, so as to take the next strain measure. This subjective experience is currently the dominant method in most industries and industries. However, as the system becomes more complex, this method can no longer meet the actual task needs, because the establishment of the threshold value in many problems is accompanied by strong personal subjectivity, and for complex systems, the experience of the individual is often inaccurate; on the other hand, due to the complexity of the system, when the observed index exceeds the actually set threshold value, the system is already in a state of breakdown or a rushing edge, and the subjective experience method cannot play a role of early warning, so that the effect of the method in many practical applications is limited. This is especially true for monitoring the condition of the human body. The human body is a complex system, the organs such as heart, liver, lung and the like are connected with each other in a myriad of ways, the change of the state of the human body is difficult to see if the index of one organ is observed, the individuation difference between people is very large, the fixed threshold value of one person is not suitable for the other person, and the work of a pathological monitoring person is greatly complicated.
Disclosure of Invention
In view of the above problems, the present application aims to provide an individualized state monitoring and analyzing method based on continuous physiological data, which is not based on the physiological parameter monitoring and analyzing technology of the traditional threshold method, but identifies the physiological state change through the physiological time series longitudinal comparison analysis of the monitored object, thereby realizing more sensitive, specific and individualized physiological state monitoring.
The application relates to a personalized physiological state monitoring and analyzing method, which comprises the following steps:
taking a vector formed by the numerical values of k physiological parameters in the same time window as an index vector, wherein k is a natural number greater than 1;
forming an index database G by using a plurality of index vectors of the individual under a normal state;
taking the index vector of the current time window of the individual as an input vector V in ；
In an index database G, n index vectors which are closest to the input vector are found through Euclidean distance or a kernel function, and the n index vectors form a matrix D;
for each index vector x of matrix D j In the index database G, n index vectors which are closest to the index database G are found through Euclidean distances or kernel functions, and a matrix D is formed j ；
Calculating a difference between the input vector and its estimated vector as a first difference V re ；
Calculating each index vector x in the matrix D j And its estimated vector V jest As a second difference value; all the second differences form a difference library G re ；
Calculation difference library G re Mean value of (a) 1 Sum variance sigma 1 ；
Calculating difference library G re Mahalanobis distance squared maximum h:
wherein x is i Is a difference library G re The difference vector of (1);
the lambda value of 0.95 quantile of chi-square distribution with the degree of freedom k is calculated:
wherein f (x) is a chi-squared distribution density function with a degree of freedom k;
and (3) calculating to obtain an approximate real covariance matrix:
∑＝∑ 1 ·h/λ；
calculating the Mahalanobis distance h re ：
h re ＝(V re -μ 1 ) T ∑ -1 (V re -μ 1 )；
The specific value of SCI is then calculated by the chi-squared cumulative distribution function with the degree of freedom k:
preferably, at least one of the plurality of physiological parameters comprises an electrocardiographic signal or a respiratory signal.
Preferably, the plurality of physiological parameters may further include a body position signal and a body movement signal.
Preferably, the index database is stored in a computing platform; the search of the predetermined number of index vectors, the calculation of the estimation vector, the calculation of the first difference value, the calculation of the second difference value, the calculation of the distribution of the first difference value in the whole second difference value, and the calculation of the state change index are all performed on the calculation platform.
Preferably, the computing platform is locally located, worn on the individual;
the physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual;
the plurality of physiological parameters are transmitted to the computing platform; the computing platform obtains the indicator vector from the received plurality of physiological parameters.
Preferably, the computing platform is remotely located;
the physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual;
the plurality of physiological parameters are sent to the computing platform through wireless transmission; the computing platform obtains the indicator vector from the received plurality of physiological parameters.
Preferably, the state change index is between 0 and 1, with higher values indicating a greater deviation from the normal state.
Preferably, if the state change index is greater than 0 and less than a predetermined value, the input vector is included as one index vector of the index database.
The individualized state monitoring analysis device based on continuous physiological data of the application comprises: a computing platform;
the computing platform comprises an index database unit, an estimation unit, a difference unit and a state change index unit;
the computing platform is configured to form a vector by using the values of the k physiological parameters of the individual in the same time window as an index vector;
the index database unit uses a plurality of index vectors of the individual under a normal state to form an index database G;
the estimation unit is used for calculating an estimation vector corresponding to the index vector input to the estimation unit;
the difference unit is used for calculating the difference between the two index vectors;
the state change index unit is used for calculating a state change index SCI reflecting the degree of the current state of the individual deviating from the normal state of the individual;
the computing platform takes the index vector of the current time window of the individual as an input vector V in Based on the input vector V in Selecting the input vector V from the index database G of the index database unit in The estimation unit calculates an estimated vector V of the input vector based on the predetermined number of index vectors est (ii) a The index vectors with the preset number form a matrix D;
for each index vector x in matrix D j The calculation platform finds out the closest preset number of index vectors from the index database G of the index database unit, and the estimation unit calculates the corresponding estimation vector V according to the preset number of index vectors jest ；
The difference unit calculates the input vector V in And its estimated vector V est The difference between them is taken as a first difference V re (ii) a The difference unit calculates each index vector x in the matrix D j And its estimated vector V jest As second difference values, all second difference values form a difference value library G re ；
State change index unit calculating differenceValue bank G re Mean value of (a) 1 Sum variance Σ 1 ；
State change index unit calculation difference library G re Mahalanobis distance squared maximum h:
wherein x is i Is a difference library G re The difference vector of (1);
the state change index unit calculates the lambda value of 0.95 quantile of chi-square distribution with the degree of freedom k:
wherein f (x) is a chi-squared distribution density function with a degree of freedom k;
the state change index unit calculates to obtain an approximate real covariance matrix:
∑＝∑ 1 ·h/λ；
the state change index unit calculates the Mahalanobis distance h re ：
h re ＝(V re -μ 1 ) T ∑ -1 (V re -μ 1 )；
Then the state change index unit calculates the specific value of SCI by the chi-square distribution cumulative distribution function with the degree of freedom k:
preferably, the device is worn on the individual; the physiological signals sensed by the physiological parameter sensors worn by the individuals are processed to obtain the physiological parameters.
Compared with the traditional subjective experience threshold method, the method considers the similarity of the front and back states of a single individual and investigates the cooperative similarity relation among multiple indexes, so that the problem of large difference among individuals is solved. On the other hand, even if some single indexes are still in the normal fluctuation range, the physiological state change condition can be predicted more early.
Drawings
FIG. 1 is a flow chart of wearable system data acquisition and transmission;
FIG. 2 is a wearable system and physiological signals acquired by the wearable system;
FIG. 3 is a graph of heart rate, respiration rate, and three-axis acceleration signals with a 30s sliding time window;
FIG. 4 is a flowchart of the overall physiological state monitoring analysis;
FIG. 5a is a diagram illustrating experimental results of a simulated high altitude hypoxic environment according to the first embodiment;
FIG. 5b is a graph showing the experimental results of the simulated high altitude hypoxic environment in the second embodiment;
fig. 6 is a patient case for rescue of sudden atrial fibrillation according to the third embodiment.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings.
Firstly, data acquisition is carried out by wearing a corset on a monitored object, the worn corset comprises an electrocardio lead interface, a respiration sensor, an acceleration sensor and a blood oxygen saturation acquisition device, electrocardio signals, respiration signals, body position physical movement signals and blood oxygen saturation can be acquired respectively, and the acquired signals are integrated into a signal acquisition box. The data transmission module can be used for carrying out central computing on the collected data, and the central computing platform can be local or remote. The signal flow diagram is shown in fig. 1.
The wearable system can be a vest or a chest strap, electrocardio, respiration and three-axis acceleration sensor signals are collected, and the electrocardio signals and the respiration signals are further processed to obtain a heart rate and respiration rate time sequence. The wearable system and its acquired raw physiological signals (electrocardiogram, respiration, triaxial acceleration) and heart rate and respiration rate time series are shown in fig. 2.
And sequentially acting on various original signal indexes of the original heart rate and respiration rate signals through smooth filtering, and extracting a heart rate index, a respiration rate index and a triaxial acceleration index of a wearer from the original heart rate and respiration rate signals. Two-dimensional vectors are formed by heart rate and respiratory rate median in a 30s time window (black rectangular frame in fig. 3), current vital signs of a tester are represented, and vectors of a monitored person in a healthy and stable state are usually selected to construct a physiological index state vector library.
After the data is transmitted to the central computing platform, the central computing platform provides independent computing resources for each monitored person, and an independent physiological index state base is established by monitoring physiological parameters for a period of time and is represented by G. After the establishment of G is completed, individual physiological state monitoring analysis can be carried out, and the input index vector is set as V in Given a V in Then, the system will find V from G in The most similar 15 vectors form a D matrix, and the degree of similarity can be characterized by euclidean distance, kernel function, etc., where ≦ indicates kernel function operation,
two vectors x are described i And x j The degree of closeness is given by the following formula:
wherein the kernel function
Legal kernel functions such as gaussian kernel and trigonometric function kernel can be used.
In finding the optimal estimation vector V est In time, the present application employs three methods.
Method one, after obtaining matrix D, applying optimization theory to search V in Optimal estimate vector V est The problem of (2) is converted into an optimization problem of the pre-solved parameter w:
w≥0
wherein, the matrix E uses frobenius norm, D is AND V in The matrix of the closest 15 vectors, E being a noise matrix in the form of 15 x 15, E being a noise vector in the form of 15 x 1, w being a coefficient vector in the form of 15 x 1.
In the second method, further regularization is performed on the parameter w, for example, for sparsification of the coefficient w, we will convert the problem of finding the most similar vector into the following optimization problem:
w≥0
wherein the matrix E uses frobenius norm, the coefficient w vector uses L1 norm, lambda 1 Is a penalty term coefficient.
Method three, the noise part can also be simplified, only considering the noise vector e:
w≥0
where e is a noise vector in the form of 15 x 1 and the norm of the coefficient w is L2 norm.
After obtaining the coefficient w by the above method, the sum V can be obtained in Is estimated to be the optimal estimation vector V est ：
v est ＝Dw
Then consider V est And V in Difference value V of re ：
v re ＝v in -v est
Through V re Can reflect the similarity between the two vectors to describe whether the state is changed or not, when the input vector V is in Vector V selected from computing system est Similarly, the current physiological state is considered not to change much, and if the difference between the input vector and the vector selected by the computing system is large, the current physiological state can be considered to change.
Except for the observation of V re The value of the value reflects the change of the physiological state, and the more stable method is to observe V re Distribution of (2).
Further, based on changes in a plurality of physiological parameters, an estimation can be made of a State Change Index (SCI).
When processing a template library G sample, in order to eliminate the influence of abnormal vectors on a result, only sample points in an equal probability range corresponding to 95% quantile points are reserved, so that a calculated residual is no longer a random sample, and an element absolute value of an estimated covariance matrix is small, so that an approximate real covariance matrix needs to be deduced.
Residual G by sample G re Calculating the mean and variance μ 1 ，∑ 1 。
Calculating the maximum squared mahalanobis distance:
wherein x is i Is G re The sample residual vector of (1). And (3) calculating the lambda value of a chi-square distribution 0.95 quantile with the degree of freedom of the characteristic dimension of the sample:
where f (x) is the chi-squared distribution density function with degrees of freedom as the characteristic dimension of the sample.
And (3) calculating to obtain an approximate real covariance matrix:
∑＝∑ 1 ·h/λ
according to the currently calculated residual V re In the sample G re Residual distribution (μ) 1 Σ) determines the magnitude of the state change index, the closer to 1 the farther from the state index library G.
First, the mahalanobis distance is calculated:
h re ＝(V re -μ 1 ) T ∑ -1 (V re -μ 1 )
then, calculating a specific numerical value of the SCI through a chi-square distribution cumulative distribution function with the degree of freedom as a sample characteristic dimension k:
at this timeCan be according to V re In case of deviation, further selecting V in And adding the state vector into a physiological index state vector library to serve as a new state of the system to enrich a state index library G.
One advantage of the computing platform is that when new state index data V is obtained in When the system comes, the system can be updated regularly, so that the system can automatically monitor for a long time and give early warning in time.
Firstly, in order to verify the accuracy and feasibility of the algorithm, a simulated plateau experiment is carried out on healthy people, physiological parameters of a tester in one day of daily life are used as a state vector library G in the experiment, in a simulated plateau hypoxic environment, the tester can respectively conduct three actions of static reading, fast walking and leg lifting in a simulated hypoxia environment with the altitude of four kilometers, the activity intensity close to the previous day is kept as much as possible, and whether the current physiological sign of the tester deviates from the state vector library G or not can be verified accurately.
Example one
FIG. 5a is a graph of the results of a simulated high altitude hypoxia experiment performed by a subject in order to better observe the hypoxic environment of the subject in which the subject is located, according to a first method.
At the very beginning of the experiment, the tester was still at normal altitude with a current state vector of V in ＝[73.2602，25.0000]And through similarity comparison, taking out 15 vectors from G to form a matrix D:
| heart rate | Respiration rate |
| 73.2602 | 25 |
| 73.7101 | 25 |
| 72.3765 | 25 |
| 74.4417 | 25 |
| 74.4417 | 25 |
| 72.0289 | 25 |
| 71.8572 | 25 |
| 71.8564 | 25 |
| 71.6847 | 25 |
| 71.3445 | 25 |
| 75.2824 | 25 |
| 73.3496 | 26 |
| 70.9220 | 25 |
| 72.9931 | 24 |
| 73.6206 | 26 |
The final calculated optimal estimated value V est ＝[73.9942，25.0637]，
Difference V re ＝[0.3160，0.0637]Calculating V re At G re Distribution of
SCI index of 0.1584, and deviation from state vector library G is small. Instead, we take the vector after the plateau experiment started: v in ＝[92.7359，22.0000]The corresponding D matrix is:
| heart rate | Respiration rate |
| 76.9231 | 23 |
| 79.1557 | 24 |
| 77.7202 | 23.5 |
| 78.7402 | 24 |
| 76.4332 | 23 |
| 80.0000 | 25 |
| 77.6208 | 24 |
| 74.0741 | 22.5 |
| 72.8160 | 22 |
| 72.5523 | 22 |
| 81.3032 | 26 |
| 76.6295 | 24 |
| 71.6855 | 22 |
| 78.3290 | 25 |
| 73.7101 | 21 |
The final calculated optimal estimated value V est ＝[77.7888，24.0335]，
Difference V re ＝[14.9471，2.0335]The calculated SCI index reached 0.98, deviating significantly from the state vector library G. SCI index is between 0 and 1, with higher values indicating a greater deviation from daily status. As can be seen from FIG. 5a, the physiological status of the subject under the hypoxic environment changes (blood oxygen saturation is decreased), the SCI index is also increased significantly, and the change of the physiological status of the subject can be effectively represented.
Example two
FIG. 5b is a graph illustrating the results of a simulated high altitude hypoxia experiment performed by a subject, wherein FIG. 5b is performed according to a second method in which a blood oxygenation signal is applied to better observe the hypoxic environment of the subject.
At the very beginning of the experiment, the tester was still at normal altitude with a current state vector of V in ＝[68.1818，22.0000]And through similarity comparison, taking out 15 vectors from G to form a matrix D:
| heart rate | Respiration rate |
| 68.3766 | 22 |
| 67.7966 | 22 |
| 68.9655 | 22 |
| 67.2316 | 22 |
| 67.2274 | 22 |
| 69.1648 | 22 |
| 67.0391 | 22 |
| 67.0391 | 22 |
| 68.1818 | 23 |
| 68.1818 | 21 |
| 68.3766 | 23 |
| 65.9341 | 22 |
| 67.7966 | 21 |
| 67.7966 | 21 |
| 65.9341 | 22 |
The final calculated optimal estimated value V est ＝[67.8312，21.9314]，
Difference V re ＝[0.3505，0.0685]Calculating V re At G re Distribution of
SCI index of 0.0397, and deviation from state vector library G is small. Instead, we take the vector after the plateau experiment starts: v in ＝[96.6186，16]The corresponding D matrix is:
| heart rate | Respiration rate |
| 69.9714 | 19 |
| 75.4717 | 22 |
| 68.9655 | 19 |
| 72.2892 | 21 |
| 76.9231 | 23.5 |
| 73.1707 | 22 |
| 67.0391 | 19 |
| 67.0391 | 19 |
| 71.0059 | 21 |
| 71.0059 | 21 |
| 68.5714 | 20 |
| 72.5082 | 22 |
| 74.5342 | 23 |
| 70.3818 | 21 |
| 72.2918 | 22 |
The final calculated optimal estimated value V est ＝[75.1937，22.7126]，
Difference V re ＝[21.4248，6.7126]The calculated SCI index reached 0.99, deviating significantly from the state vector library G. SCI index is between 0 and 1, with higher values indicating a greater deviation from daily status. As can be seen from FIG. 5b, the physiological status of the subject under the hypoxic environment changes (blood oxygen saturation is decreased), the SCI index is also significantly increased, and the change of the physiological status of the subject can be effectively represented.
EXAMPLE III
Fig. 6 is a patient case for rescue of a sudden severe atrial fibrillation. Method three is implemented in fig. 6. Selecting the physiological data of a patient in a relatively steady state for one day to construct a physiological state vector library G, where (a) in fig. 6 is data of a certain morning where vital signs are steady after rescue treatment, for example, about 52 minutes at 08 am, and the current state vector is: v in ＝[51.2820，22]And through similarity comparison, taking out 15 vectors from G to form a matrix D:
| heart rate | Respiration rate |
| 51.3921 | 22 |
| 51.3921 | 22 |
| 51.3921 | 22 |
| 51.0638 | 22 |
| 51.0638 | 22 |
| 50.9556 | 22 |
| 50.8475 | 22 |
| 51.7241 | 22 |
| 50.6329 | 22 |
| 51.2821 | 21 |
| 52.0610 | 22 |
| 50.4202 | 22 |
| 50.4202 | 22 |
| 51.1729 | 23 |
| 52.1739 | 22 |
The final calculated optimized estimated value V est ＝[51.2051，21.9986]The difference vector is: v re ＝[0.0769，0.0014]Calculate it at G re Distribution:
the SCI value of (1) is 0.0853, and the SCI value is mostly at the normal level. In FIG. 6 (b), the data collected on the day of atrial fibrillation of the patient is shown, and the current input vector is V at about 19 PM and 40 PM in ＝[72.5，12.5]The corresponding matrix D is:
| heart rate | Appealing rate |
| 51.8361 | 14 |
| 50.6329 | 13 |
| 51.0638 | 14 |
| 52.4017 | 16 |
| 50.0000 | 13 |
| 52.2878 | 16 |
| 50.6329 | 14 |
| 50.6329 | 14 |
| 49.7925 | 13 |
| 53.5725 | 18 |
| 49.3868 | 13 |
| 51.8361 | 16 |
| 50.2092 | 14 |
| 50.2092 | 14 |
| 50.2092 | 14 |
The calculated optimal estimate vector: v est ＝[53.5725，18]Difference value V re ＝[18.9275，5.5]The calculated SCI was as high as 0.99, and it was shown from medical records that the patient did feel uncomfortable from afternoon and atrial fibrillation occurred around 21 o' clock and 50, but SCI was comparable to normal levelsStarting from point 19 and 40, the value is always at a higher level.
In the application, the physiological state change is identified through the longitudinal comparison and analysis of the physiological time sequence of the monitored object, so that more sensitive, specific and individualized physiological state monitoring is realized.
The computing platform can be realized by a single chip microcomputer, a DSP, a computer and the like, and the index database unit, the estimation unit, the difference unit and the state change index unit can be functional modules realized by programs on the computing platform.
Unless defined otherwise, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The materials, methods, and examples set forth in this application are illustrative only and not intended to be limiting.
Although the present invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description and are intended to be included within the scope of the present invention.
Claims (11)
1. A method of personalized state monitoring analysis based on continuous physiological data, comprising:
taking a vector formed by the numerical values of k physiological parameters in the same time window as an index vector, wherein k is a natural number greater than 1;
forming an index database G by using a plurality of index vectors of the individual under a normal state;
taking the index vector of the current time window of the individual as an input vector V in ；
In an index database G, n index vectors which are closest to the input vector are found through Euclidean distance or a kernel function, and the n index vectors form a matrix D;
for each index vector x of matrix D j In the index database G, n index vectors which are closest to the index database G are found through Euclidean distance or kernel function, and a matrix D is formed j ；
Calculating a difference between the input vector and its estimated vector as a first difference V re ；
Calculating each index vector x in the matrix D j And its estimated vector V jest As a second difference value; all the second differences form a difference library G re ；
Calculating difference library G re Mean value of (a) 1 Sum variance sigma 1 ；
Calculation difference library G re Mahalanobis distance squared maximum h:
wherein x is i Is a difference library G re The difference vector of (1);
the lambda value of 0.95 quantile of chi-square distribution with the degree of freedom k is calculated:
wherein f (x) is a chi-squared distribution density function having a degree of freedom k;
and (3) calculating to obtain an approximate real covariance matrix:
∑＝∑ 1 ·h/λ；
calculating the Mahalanobis distance h re ：
h re ＝(V re -μ 1 ) T ∑ -1 (V re -μ 1 )；
Then, the specific value of the state change index SCI is calculated by a chi-square distribution cumulative distribution function with the degree of freedom k:
2. the method of claim 1, wherein:
the k physiological parameters at least comprise electrocardiosignals or respiration signals.
3. The method of claim 1, wherein:
the k physiological parameters may further include a body position signal and a body motion signal.
4. The method of claim 1, wherein:
the index database is stored in a computing platform; the search of the n index vectors, the calculation of the estimation vector, the calculation of the first difference value, the calculation of the second difference value, the calculation of the distribution of the first difference value in the whole second difference value, and the calculation of the state change index SCI are all performed on the computing platform.
5. The method of claim 4, wherein:
the computing platform is located locally and worn on the individual;
the k physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual;
the k physiological parameters are transmitted to the computing platform; the computing platform obtains the indicator vector from the received plurality of physiological parameters.
6. The method of claim 4, wherein:
the computing platform is remotely located;
the k physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual;
the k physiological parameters are sent to the computing platform through wireless transmission; the computing platform obtains the indicator vector from the received plurality of physiological parameters.
7. The method of claim 1, wherein:
the state change index SCI is between 0 and 1, with higher values indicating a greater deviation from the normal state.
8. The method of claim 1, wherein:
if the state change index SCI is greater than 0 and less than a predetermined value, the input vector is included as an index vector of the index database.
9. An individualized status monitoring and analysis device based on continuous physiological data, comprising: a computing platform;
the computing platform comprises an index database unit, an estimation unit, a difference unit and a state change index unit;
the computing platform is configured to form a vector by using the values of the k physiological parameters of the individual in the same time window as an index vector;
the index database unit uses a plurality of index vectors of the individual under a normal state to form an index database G;
the estimation unit is used for calculating an estimation vector corresponding to the index vector input to the estimation unit;
the difference unit is used for calculating the difference between the two index vectors;
the state change index unit is used for calculating a state change index SCI reflecting the degree of the current state of the individual deviating from the normal state of the individual;
the computing platform takes the index vector of the current time window of the individual as an input vector V in Based on the input vector V in Selecting the input vector V from the index database G of the index database unit in The estimation unit calculates an estimated vector V of the input vector based on the predetermined number of index vectors est (ii) a The index vectors with the preset number form a matrix D;
for each index vector x in matrix D j The computing platform finds and stores the index data from the index database G of the index database unitThe estimation unit calculates an estimation vector V corresponding to the index vectors of the predetermined number based on the index vectors of the predetermined number jest ；
The difference unit calculates the input vector V in And its estimated vector V est The difference between them is taken as a first difference V re (ii) a The difference unit calculates each index vector x in the matrix D j And its estimated vector V jest As second difference values, all second difference values form a difference value library G re ；
State change index unit calculation difference library G re Mean value of (a) 1 Sum variance Σ 1 ；
State change index unit calculation difference library G re Mahalanobis distance squared maximum h:
wherein x is i Is a difference library G re The difference vector of (1);
the state change index unit calculates the lambda value of 0.95 quantile of chi-square distribution with the degree of freedom k:
wherein f (x) is a chi-squared distribution density function with a degree of freedom k;
the state change index unit calculates to obtain an approximate real covariance matrix:
∑＝∑ 1 ·h/λ：
the state change index unit calculates the Mahalanobis distance h re ：
h re ＝(V re -μ 1 ) T ∑ -1 (V re -μ 1 )；
Then, the state change index unit calculates a specific value of the state change index SCI by a chi-square distribution cumulative distribution function with a degree of freedom k:
10. the apparatus of claim 9, wherein:
the device is worn on the individual; the k physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual.
11. The apparatus of claim 9, wherein:
the computing platform is remotely located; the k physiological parameters are obtained by processing physiological signals sensed by a physiological parameter sensor worn by the individual; and the k physiological parameters are transmitted to the computing platform in a wireless mode.
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