CN116720493A - Physiological data processing method, device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a physiological data processing method, a physiological data processing device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: physiological data in a preset time period are acquired through physiological data acquisition equipment; dividing physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; and generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type. The apparatus is for performing the above method. According to the application, the data in a period of time acquired by the physiological data acquisition equipment is divided, and the corresponding sample value is calculated for the divided data, so that the change condition of the long-time data can be observed conveniently, and the accuracy of data processing is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a physiological data processing method, a physiological data processing device, an electronic device, and a storage medium.
Background
With the development of information acquisition and data processing technologies and internet technologies, the intelligent and accurate monitoring and processing of physiological parameters are also receiving attention.
At present, the amplitude of a single-channel physiological signal is generally regarded as a function of time and is a one-dimensional physiological signal, a plurality of signal parameter physiological data calculated from the single-channel physiological signal is regarded as a one-dimensional vector, the representation mode of the one-dimensional vector only can reflect the short-time or comprehensive condition of the data, biological clock rhythm information contained in the physiological data in a period of time cannot be seen, and the processed data cannot accurately reflect the actual condition of a target object.
Disclosure of Invention
The embodiment of the application aims to provide a physiological data processing method, a physiological data processing device, electronic equipment and a storage medium, which are used for processing acquired long-time physiological data and improving the accuracy of data processing.
In a first aspect, an embodiment of the present application provides a physiological data processing method, including: physiological data in a preset time period are acquired through physiological data acquisition equipment; dividing physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; the physiological data is a quantization index of human physiological information; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; and generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
According to the embodiment of the application, the data in a period of time acquired by the physiological data acquisition equipment are divided into the data with the time structure, the corresponding sample value is calculated for the divided data, and the two-dimensional data table is generated according to the physiological data type and the calculated sample value. In the process, a plurality of physiological data divided into a plurality of data intervals extract a plurality of key physiological information in an original single-channel one-dimensional physiological signal and display time evolution rules thereof, so that physiological data change conditions among different data intervals are reflected, and the long-time evolution conditions of the plurality of physiological data can be more accurately expressed in a two-dimensional data table form, so that the accuracy of data processing is improved.
In some embodiments, after generating the two-dimensional data table from the physiological data interval and the corresponding sample value for each physiological data type, the method further comprises: carrying out gray level calculation on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray level values; and generating a gray scale image according to the gray scale value.
The embodiment of the application converts the sample values in the two-dimensional data table into the gray values and generates the gray map according to the gray values. The gray-scale pattern form allows a more visual view of the specific variations in sample values than the two-dimensional data table form.
In some embodiments, gray-scale calculation is performed on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray-scale values, including: obtaining a standard sample value of each physiological data interval of each physiological data type; calculating a standard sample mean and a standard sample standard deviation of the plurality of standard sample values; normalizing the sample value corresponding to each physiological data interval of each physiological data type according to the standard sample mean and the standard sample standard deviation to obtain a first processed sample value; the first processed sample value is mapped to a gray value.
The embodiment of the application obtains the deviation condition of the current sample value and the standard sample value by comparing the sample value of each physiological data type in the two-dimensional data table with the average state of the corresponding standard sample value. And, demonstrate the deviation obtained in the form of gray chart, can see the deviation degree of the average state of the current sample value compared with standard sample value, has solved the poor problem of data readability.
In some embodiments, gray-scale calculation is performed on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray-scale values, including: calculating a sample mean and a sample standard deviation of a plurality of sample values for each physiological data type; carrying out normalization processing on the sample value corresponding to each physiological data interval of each physiological data type according to the sample mean value and the sample standard deviation to obtain a second processed sample value; the second processed sample value is mapped to a gray value.
The embodiment of the application obtains the deviation condition of the current sample value and all sample values by comparing the sample value of each physiological data type in the two-dimensional data table with the average state of all sample values corresponding to the data type. And, the obtained deviation is displayed in the form of a gray scale, besides the deviation degree of the current sample value compared with the average state of all the sample values can be seen, the problem of poor data readability is solved.
In some embodiments, the physiological data type includes a heartbeat interval; according to the physiological data corresponding to each physiological data type in each physiological data interval, calculating a sample value of the corresponding physiological data type comprises the following steps: calculating an interval mean value and an interval standard deviation of a plurality of heartbeat intervals corresponding to each physiological data interval; screening abnormal heartbeat intervals in a plurality of heartbeat intervals corresponding to each physiological data interval according to each interval mean value and each interval standard deviation; and obtaining a sample value of the corresponding physiological data interval according to the ratio relation of the number of abnormal heartbeat intervals and the total number of the plurality of heartbeat intervals.
According to the embodiment of the application, the sample value is obtained through calculation according to the physiological data included in each data interval, so that the obtained sample value can reflect the overall situation of the data interval.
In some embodiments, the physiological data types include human body three-dimensional acceleration detrending high frequency fluctuations; according to the physiological data corresponding to each physiological data type in each physiological data interval, calculating a sample value of the corresponding physiological data type comprises the following steps: calculating the median value of the trend-removing high-frequency fluctuation of the three-dimensional acceleration of the human body corresponding to each physiological data interval; screening out abnormal human body three-dimensional acceleration tendency high-frequency fluctuation in the plurality of human body three-dimensional acceleration tendency high-frequency fluctuation corresponding to each physiological data interval according to each median value; and obtaining a sample value corresponding to the physiological data interval according to the mean value of the trend-removed high-frequency fluctuation of the abnormal three-dimensional acceleration of the human body.
According to the embodiment of the application, the sample value is obtained through calculation according to the physiological data included in each data interval, so that the obtained sample value can reflect the overall situation of the data interval. And the mode of calculating the sample value is different according to different physiological data types, so that the obtained sample value can accurately reflect the change condition of the corresponding physiological data type, and the accuracy of data expression is improved.
In a second aspect, an embodiment of the present application provides a physiological data processing device, including: the acquisition module is used for acquiring physiological data in a preset time period through the physiological data acquisition equipment; the division module is used for dividing the physiological data according to preset time intervals to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; the calculating module is used for calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; and the generation module is used for generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
In some embodiments, the apparatus further comprises: the gray value calculation module is used for carrying out gray calculation on the sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray values; and the gray level map generation module is used for generating a gray level map according to the gray level value.
In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor, a memory, a storage medium and a bus, wherein the processor and the memory are communicated with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method steps of the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium comprising: the computer-readable storage medium stores computer instructions that cause the computer to perform the method steps of the first aspect.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present application;
FIG. 2 (a) is a gray scale diagram of an abnormal sample value according to an embodiment of the present application;
FIG. 2 (b) is a gray scale diagram of a normal sample value according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an outlier heartbeat interval according to an embodiment of the present application;
FIG. 4 is a schematic diagram of outliers of BADF data points provided by embodiments of the application;
FIG. 5 is a schematic diagram of a physiological data processing device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for the purpose of illustration and description only and are not intended to limit the scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this disclosure, illustrates operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure.
In addition, the described embodiments are only some, but not all, embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It is noted that all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
It can be understood that the physiological data processing method provided by the embodiment of the application can be applied to terminal equipment (also called electronic equipment) and a server; the terminal equipment can be a smart phone, a tablet personal computer, a personal digital assistant (Personal Digital Assistant, PDA) and the like; the server may be an application server or a Web server.
In order to facilitate understanding, the application scenario of the physiological data processing method provided by the embodiment of the present application is described below by taking a server as an execution body as an example.
Fig. 1 is a flow chart of a physiological data processing method according to an embodiment of the present application, as shown in fig. 1, the method includes:
step 101, physiological data in a preset time period is acquired through a physiological data acquisition device.
In a specific implementation process, the physiological data acquisition equipment comprises wireless physiological data acquisition equipment, wearable data acquisition equipment, a physiological data acquisition instrument and the like, wherein the wearable data acquisition equipment can be various types of bracelets and the like. The specific type of physiological data acquisition device used may be set according to the actual situation, and the present application is not particularly limited thereto.
The preset time period is a preset time period, for example, physiological data of one day can be collected by the physiological data collection device, physiological data of multiple days can also be collected, and the specific time of the collection of the physiological data can be set according to actual conditions, so that the application is not limited in particular.
The physiological data are used for representing physiological characteristics and change conditions of a user, and comprise various parameters in single-channel one-dimensional physiological signals such as pulse waves, electrocardiograms, electroencephalograms, heartbeat rhythm time sequences, blood pressure time sequences and the like.
Exemplary, the embodiment of the application collects physiological data of a user in one day through a bracelet.
Step 102, dividing physiological data according to a preset observation time window by a server to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data.
In the implementation process, the window length of the preset observation window is preset, and may be 2 minutes as an observation window, 5 minutes as an observation window, or 8 minutes as an observation window, and the sliding step of the observation window may be one observation window length. The length of the window and the sliding steps thereof can be set according to practical situations, and the application is not limited in particular.
By presetting the length of the observation time window and sliding stepping, the acquired physiological data is divided into a plurality of physiological data intervals according to the observation time window, so that one physiological data interval corresponds to one observation time window. It should be noted that, since the division is performed by the observation time window, and the observation time window slides along the time axis in a specific step, the physiological signal acquisition device is a two-dimensional data table formed by a plurality of physiological data and a plurality of data intervals in which physiological signals are continuously acquired, and the physiological signals are expressed as a plurality of physiological data types in the data acquisition time of one day.
The physiological data type is a kind of signal parameter calculated from a specific physiological signal, and includes signal parameters calculated from pulse wave, electrocardiogram, electroencephalogram, time series of heart beat rhythm, time series of blood pressure, etc., such as average pulse rate in observation time window, pulse interval subband power, etc.
Step 103, calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval.
In a specific implementation, the sample value is used for characterizing the feature of the physiological data interval, and the change condition of the collected physiological data in different physiological data intervals can be known according to the sample value. Because the physiological data acquisition device can acquire multiple types of physiological data, each physiological data type corresponds to multiple physiological data intervals, and each physiological data interval comprises multiple physiological data corresponding to the physiological data type. Therefore, it is necessary to calculate a corresponding sample value from the physiological data corresponding to each physiological data type in each physiological data interval to represent the characteristics of the physiological data interval.
And 104, generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
In the implementation process, after obtaining a sample value corresponding to each physiological data interval of each physiological data type, a two-dimensional data table is generated according to the physiological data interval and the sample value corresponding to each physiological data type.
Table 1 is a two-dimensional data table provided in an embodiment of the present application, which may be named as "physiological information quantization index-biological clock rhythm" and is used for representing sample value conditions of each physiological data interval of each physiological data type, where a feature sequence number represents a physiological data type and a time slot represents a physiological data interval. Table 1 shows sample values for 10 physiological data intervals of 13 physiological data types.
TABLE 1
According to the embodiment of the application, the data in a period of time acquired by the physiological data acquisition equipment are divided into the data with the time structure, the corresponding sample value is calculated for the divided data, and the two-dimensional data table is generated according to the physiological data type and the calculated sample value. In the process, a plurality of physiological data divided into a plurality of data intervals extract a plurality of key physiological information in an original single-channel one-dimensional physiological signal and display time evolution rules thereof, so that physiological data change conditions among different data intervals are reflected, and comprehensive time evolution conditions of the plurality of physiological data can be more accurately expressed in a two-dimensional data table form, so that accuracy of data processing is improved.
In some embodiments, after generating the two-dimensional data table from the physiological data interval and the corresponding sample value for each physiological data type, the method further comprises: carrying out gray level calculation on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray level values; and generating a gray scale image according to the gray scale value.
In the implementation process, after the two-dimensional data table is obtained, the change condition of sample values of different data intervals is difficult to be observed from numerous data in the two-dimensional data table due to poor readability of the two-dimensional data table, so that the sample values in the two-dimensional data table are subjected to gray value conversion to generate a gray map. Specifically, the sample values in the two-dimensional data table are mapped to gray values of [0,255] according to the mapping rule.
The mapping rule for mapping the sample value to the gray value is: sample value × scaling ratio+128, where the scaling ratio is determined according to the distribution of sample values, and may be specifically denoted as 128/x, in the implementation process, x may be set to 2, may be set to 3, that is, the scaling ratio may be set to 128/3, may be set to 128/2, and may be specifically set according to practical situations, which is not specifically limited in the present application.
It should be noted that a sample value of 0 represents that the gray value of the sample value is 128, that is, the current sample value has no deviation from the standard sample value; if the sample value is not 0, that is, the current sample value has deviation compared with the standard sample value, and the larger the absolute value of the sample is, the larger the deviation is, the corresponding gray value is calculated according to the mapping rule, if the gray value calculated by a certain sample value according to the mapping rule is larger than 255, the gray value of the sample value takes the value 255, and if the gray value calculated by a certain sample value according to the mapping rule is smaller than 0, the gray value of the sample value takes the value 0.
The embodiment of the application converts the sample values in the two-dimensional data table into the gray values and generates the gray map according to the gray values. The gray-scale pattern form allows a more visual view of the specific variations in sample values than the two-dimensional data table form. According to the gray value mapping rule, the whiter area in the gray map represents that the current physiological data sample is smaller than the standard sample; the darker areas in the gray scale map represent the larger the current physiological data sample compared to the standard sample.
In some embodiments, gray-scale calculation is performed on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray-scale values, including: obtaining a standard sample value of each physiological data interval of each physiological data type; calculating a standard sample mean and a standard sample standard deviation of the plurality of standard sample values; normalizing the sample value corresponding to each physiological data interval of each physiological data type according to the standard sample mean and the standard sample standard deviation to obtain a first processed sample value; the first processed sample value is mapped to a gray value.
In a specific implementation process, the standard sample value is a sample value obtained by calculation according to standard physiological data of the physiological data type in advance. Since the acquired physiological data is divided into a plurality of physiological data intervals, each physiological data interval corresponds to a standard sample value, and each data type corresponds to a plurality of standard sample values.
In order to obtain the deviation degree of the current sample value compared with the standard sample value, the sample value corresponding to each physiological data interval of each physiological data type needs to be normalized according to the standard sample value. The normalization process is as follows:
1) Calculating a standard sample mean and a standard sample standard deviation of the plurality of standard sample values;
2) And carrying out normalization processing on the sample value corresponding to each physiological data interval of each current physiological data type according to the standard sample mean value and the standard sample standard deviation to obtain a processed current sample value.
It should be noted that, when the standard sample mean is regarded as a, the standard sample standard deviation is regarded as b, and the current sample value corresponding to each physiological interval is regarded as c, the current sample value corresponding to each physiological data interval of each physiological data type can be normalized according to the standard sample mean and the standard sample standard deviation, specifically according to the formulaAnd (5) performing calculation. It should be noted that as long asThe deviation degree of the current sample value compared with the standard sample value can be shown, the calculation mode is not limited to the mode shown in the embodiment of the application, and the specific setting can be carried out according to the actual situation.
After the processed sample value is obtained, in order to more clearly and intuitively see the deviation degree of the sample value, the processed sample value is mapped into a gray value according to a mapping rule, and a gray map is generated according to the gray value. Please refer to the above embodiment for specific mapping rules, which are not described herein.
In order to facilitate visual observation of whether a current sample value belongs to a normal sample value or an abnormal sample value relative to a standard sample value, after the current sample value is mapped into a gray level value according to a mapping rule and converted into a gray level image, the gray level image is compared with the gray level image of the abnormal sample value and the gray level image of the normal sample value provided by the embodiment of the application. Fig. 2 (a) is a gray scale diagram of an abnormal sample value provided by the embodiment of the present application, and fig. 2 (b) is a gray scale diagram of a normal sample value provided by the embodiment of the present application. The full black columns in fig. 2 (a) and 2 (b) indicate that no data is collected for the current data interval. It can be intuitively seen whether the current sample value belongs to a normal sample or an abnormal sample from the gray scale map of the current sample value and the gray scale maps shown in fig. 2 (a) and 2 (b).
The embodiment of the application obtains the deviation condition of the current sample value and the standard sample value by comparing the sample value of each physiological data type in the two-dimensional data table with the average state of the corresponding standard sample value. And, demonstrate the deviation obtained in the form of gray chart, can see the deviation degree of the average state of the current sample value compared with standard sample value, has solved the poor problem of data readability.
In some embodiments, gray-scale calculation is performed on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray-scale values, including: calculating a sample mean and a sample standard deviation of a plurality of sample values for each physiological data type; carrying out normalization processing on the sample value corresponding to each physiological data interval of each physiological data type according to the sample mean value and the sample standard deviation to obtain a second processed sample value; the second processed sample value is mapped to a gray value.
In the implementation process, the degree of deviation of the current sample value compared with the standard sample value can be seen by comparing the current sample value with the standard sample value, and the condition that the sample value of each physiological data interval of each physiological data type deviates from the sample average value of all data intervals of the physiological data type in the current physiological data interval can be obtained by comparing the sample value of each physiological data interval of each physiological data type with the sample average value and standard deviation of all physiological data intervals corresponding to the physiological data type.
In order to obtain the situation that the sample value of each physiological data type in each physiological data interval deviates from the sample average value of all the data intervals of the physiological data type, the sample value corresponding to each physiological data interval of each physiological data type needs to be normalized. The normalization process is as follows:
1) Calculating a sample mean and a sample standard deviation of a plurality of sample values for each physiological data type;
2) And carrying out normalization processing on the sample value corresponding to each physiological data interval of each physiological data type according to the sample mean value and the sample standard deviation to obtain a processed sample value.
Note that, the calculation process in the normalization process is referred to the above embodiment, and will not be described herein. After the processed sample value is obtained, in order to more clearly and intuitively see the deviation degree of the sample value, the processed sample value is mapped into a gray value according to a mapping rule, and a gray map is generated according to the gray value. Please refer to the above embodiment for specific mapping rules, which are not described herein.
The embodiment of the application obtains the condition that the current sample value deviates from the average state by comparing the sample value of each physiological data type in the two-dimensional data table with the average state of all sample values corresponding to the data type. And, the obtained deviation is displayed in the form of a gray scale, besides the deviation degree of the current sample value compared with the average state of all the sample values can be seen, the problem of poor data readability is solved.
In some embodiments, the physiological data type includes a heartbeat interval; according to the physiological data corresponding to each physiological data type in each physiological data interval, calculating a sample value of the corresponding physiological data type comprises the following steps: calculating an interval mean value and an interval standard deviation of a plurality of heartbeat intervals corresponding to each physiological data interval; screening abnormal heartbeat intervals in a plurality of heartbeat intervals corresponding to each physiological data interval according to each interval mean value and each interval standard deviation; and obtaining a sample value of the corresponding physiological data interval according to the ratio relation of the number of abnormal heartbeat intervals and the total number of the plurality of heartbeat intervals.
In the implementation process, the physiological data types are various, and the calculation modes of calculating the obtained sample values are different from one physiological data type to another physiological data type. Therefore, in order to facilitate understanding how to calculate a sample value of each physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval, the physiological data type is taken as a certain heartbeat interval parameter for illustration, and the calculation method is as follows: if N cardiac intervals exist in a certain physiological data interval, firstly calculating the mean value and standard deviation of the N cardiac intervals, and then calculating the number duty ratio of the isolated cardiac intervals which are out of the range of the mean value plus or minus twice the standard deviation in the N cardiac intervals. The duty ratio is taken as a sample value of the corresponding physiological data interval. It should be noted that, the heart rate fluctuation intensity of the current physiological data interval can be measured according to the duty ratio condition, so as to reflect the physical condition of the user.
Fig. 3 is a schematic diagram of an outlier heartbeat interval according to an embodiment of the present application, as shown in fig. 3, including a plurality of heartbeat intervals within a certain 400 seconds, and including a positive outlier and a negative outlier in a heartbeat interval sequence. The number of positive outliers and negative outliers can be regarded as the number of outlier heartbeat intervals which are out of the range of the standard deviation of the average plus and minus twice of the N heartbeat intervals.
According to the embodiment of the application, the sample value is obtained through calculation according to the physiological data included in each data interval, so that the obtained sample value can reflect the overall situation of the data interval.
In some embodiments, the physiological data types include human body three-dimensional acceleration detrending high frequency fluctuations; according to the physiological data corresponding to each physiological data type in each physiological data interval, calculating a sample value of the corresponding physiological data type comprises the following steps: calculating the median value of the trend-removing high-frequency fluctuation of the three-dimensional acceleration of the human body corresponding to each physiological data interval; screening out abnormal human body three-dimensional acceleration tendency high-frequency fluctuation in the plurality of human body three-dimensional acceleration tendency high-frequency fluctuation corresponding to each physiological data interval according to each median value; and obtaining a sample value corresponding to the physiological data interval according to the mean value of the trend-removed high-frequency fluctuation of the abnormal three-dimensional acceleration of the human body.
In a specific implementation process, in order to facilitate understanding how to calculate a sample value of a corresponding physiological data type according to physiological data corresponding to each physiological data type in each physiological data interval, a three-dimensional acceleration detrack high-frequency fluctuation (BADF) of a human body is used for illustration, and the calculation mode is as follows: if there are M BADF data points in a certain physiological data interval, the median of the M BADF data points is calculated, then the data points larger than the median in the M BADF data points are found, and the mean of the data points is calculated. The mean value is taken as a sample value of the corresponding physiological data interval.
Fig. 4 is a schematic diagram of an outlier of a BADF data point provided by an embodiment of the present application, where, as shown in fig. 4, a plurality of BADF data points are included within a certain 13 minutes, and a part of BADF data points larger than a median value are included in the plurality of BADF data points, and the average value of the part of BADF data points larger than the median value is calculated as a sample value of a corresponding physiological data interval.
According to the embodiment of the application, the sample value is obtained through calculation according to the physiological data included in each data interval, so that the obtained sample value can reflect the overall situation of the data interval. And the mode of calculating the sample value is different according to different physiological data types, so that the obtained sample value can accurately reflect the change condition of the corresponding physiological data type, and the accuracy of data expression is improved.
It should be noted that, the physiological data processing method provided by the embodiment of the present application is only used for characterizing physiological data, and cannot directly identify, determine or eliminate the etiology or the lesion of a living human or animal body.
Fig. 5 is a schematic structural diagram of a physiological data processing device according to an embodiment of the present application, as shown in fig. 5, where the device includes: an acquisition module 501, a division module 502, a calculation module 503, and a generation module 504, wherein,
the acquisition module 501 is configured to acquire physiological data in a preset time period through a physiological data acquisition device; the dividing module 502 is configured to divide the physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; the physiological data is a quantization index of human physiological information; a calculating module 503, configured to calculate a sample value of each physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; the generating module 504 is configured to generate a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
On the basis of the above embodiment, the device further includes: the gray value calculation module is used for carrying out gray calculation on the sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray values; and the gray level map generation module is used for generating a gray level map according to the gray level value.
On the basis of the above embodiment, the gray value calculating module is specifically configured to: carrying out gray level calculation on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray level values, wherein the method comprises the following steps: obtaining a standard sample value of each physiological data interval of each physiological data type; calculating a standard sample mean and a standard sample standard deviation of the plurality of standard sample values; normalizing the sample value corresponding to each physiological data interval of each physiological data type according to the standard sample mean and the standard sample standard deviation to obtain a first processed sample value; the first processed sample value is mapped to a gray value.
On the basis of the above embodiment, the gray value calculating module is specifically configured to: calculating a sample mean and a sample standard deviation of a plurality of sample values for each physiological data type; carrying out normalization processing on the sample value corresponding to each physiological data interval of each physiological data type according to the sample mean value and the sample standard deviation to obtain a second processed sample value; the second processed sample value is mapped to a gray value.
On the basis of the above embodiment, the physiological data type includes a heartbeat interval; the calculating module 503 is specifically configured to: calculating an interval mean value and an interval standard deviation of a plurality of heartbeat intervals corresponding to each physiological data interval; screening abnormal heartbeat intervals in a plurality of heartbeat intervals corresponding to each physiological data interval according to each interval mean value and each interval standard deviation; and obtaining a sample value of the corresponding physiological data interval according to the ratio relation of the number of abnormal heartbeat intervals and the total number of the plurality of heartbeat intervals.
On the basis of the embodiment, the physiological data types comprise three-dimensional acceleration tendency-removed high-frequency fluctuation of the human body; the calculating module 503 is specifically configured to: calculating the median value of the trend-removing high-frequency fluctuation of the three-dimensional acceleration of the human body corresponding to each physiological data interval; screening out abnormal human body three-dimensional acceleration tendency high-frequency fluctuation in the plurality of human body three-dimensional acceleration tendency high-frequency fluctuation corresponding to each physiological data interval according to each median value; and obtaining a sample value corresponding to the physiological data interval according to the mean value of the trend-removed high-frequency fluctuation of the abnormal three-dimensional acceleration of the human body.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 6, where the electronic device includes a processor (processor) 601, a memory (memory) 602, and a bus 603; wherein the processor 601 and the memory 602 perform communication with each other via the bus 603. The processor 601 is configured to invoke program instructions in the memory 602 to perform the methods provided by the method embodiments described above.
The processor 601 may be an integrated circuit chip having signal processing capabilities. The processor 601 may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. Which may implement or perform the various methods, steps, and logical blocks disclosed in embodiments of the application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 602 may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), and the like.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example comprising: physiological data in a preset time period are acquired through physiological data acquisition equipment; dividing physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; the physiological data is a quantization index of human physiological information; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; and generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above-described method embodiments, for example, including: physiological data in a preset time period are acquired through physiological data acquisition equipment; dividing physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprising a plurality of physiological data; the physiological data is a quantization index of human physiological information; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval; and generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of physiological data processing, the method comprising:
physiological data in a preset time period are acquired through physiological data acquisition equipment;
dividing the physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each physiological data type comprises a plurality of physiological data, and the physiological data is a quantization index of human physiological information;
calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval;
and generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
2. The method of claim 1, wherein after generating a two-dimensional data table from the physiological data interval and the corresponding sample values for each physiological data type, the method further comprises:
carrying out gray level calculation on sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray level values;
and generating a gray scale image according to the gray scale value.
3. The method according to claim 2, wherein the performing gray scale calculation on the sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray scale values includes:
obtaining a standard sample value of each physiological data interval of each physiological data type;
calculating a standard sample mean and a standard sample standard deviation of a plurality of standard sample values;
normalizing the sample value corresponding to each physiological data interval of each physiological data type according to the standard sample mean and the standard sample standard deviation to obtain a first processed sample value;
mapping the first processed sample value to the gray value.
4. The method according to claim 2, wherein the performing gray scale calculation on the sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray scale values includes:
calculating a sample mean and a sample standard deviation of a plurality of sample values for each of the physiological data types;
normalizing the sample value corresponding to each physiological data interval of each physiological data type according to the sample mean value and the sample standard deviation to obtain a second processed sample value;
and mapping the second processed sample value to the gray value.
5. The method of claim 1, wherein the physiological data type comprises a heartbeat interval; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval, including:
calculating an interval mean value and an interval standard deviation of a plurality of heartbeat intervals corresponding to each physiological data interval;
screening abnormal heartbeat intervals in a plurality of heartbeat intervals corresponding to each physiological data interval according to each interval mean value and each interval standard deviation;
and obtaining a sample value of the corresponding physiological data interval according to the ratio relation between the number of abnormal heartbeat intervals and the total number of the plurality of heartbeat intervals.
6. The method of any one of claims 1-5, wherein the physiological data type comprises human three-dimensional acceleration detrending high frequency fluctuations; calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval, including:
calculating the median value of the trend-removing high-frequency fluctuation of the three-dimensional acceleration of the human body corresponding to each physiological data interval;
screening out abnormal human body three-dimensional acceleration tendency high-frequency fluctuation in the plurality of human body three-dimensional acceleration tendency high-frequency fluctuation corresponding to each physiological data interval according to each median value;
and obtaining a sample value of a corresponding physiological data interval according to the mean value of the trend-removed high-frequency fluctuation of the abnormal human body three-dimensional acceleration.
7. A physiological data processing device, the device comprising:
the acquisition module is used for acquiring physiological data in a preset time period through the physiological data acquisition equipment;
the division module is used for dividing the physiological data according to a preset observation time window to obtain a plurality of physiological data intervals; wherein the physiological data comprises physiological data types, each of the physiological data types comprising a plurality of physiological data; the physiological data is a quantization index of human physiological information;
the calculation module is used for calculating a sample value of the corresponding physiological data type according to the physiological data corresponding to each physiological data type in each physiological data interval;
and the generation module is used for generating a two-dimensional data table according to the physiological data interval and the sample value corresponding to each physiological data type.
8. The apparatus of claim 7, wherein the apparatus further comprises:
the gray value calculation module is used for carrying out gray calculation on the sample values of each physiological data type in the two-dimensional data table to obtain corresponding gray values;
and the gray level image generating module is used for generating a gray level image according to the gray level values.
9. An electronic device, comprising: a processor and a memory storing machine readable instructions executable by the processor, which when executed by the processor perform a physiological data processing method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when run by a processor, performs a physiological data processing method according to any of claims 1 to 6.
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