CN116759102A - Analysis management system based on heart rehabilitation data - Google Patents
Analysis management system based on heart rehabilitation data Download PDFInfo
- Publication number
- CN116759102A CN116759102A CN202311034707.XA CN202311034707A CN116759102A CN 116759102 A CN116759102 A CN 116759102A CN 202311034707 A CN202311034707 A CN 202311034707A CN 116759102 A CN116759102 A CN 116759102A
- Authority
- CN
- China
- Prior art keywords
- data
- heart sound
- sound data
- sub
- motion parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 19
- 238000007405 data analysis Methods 0.000 claims abstract description 54
- 230000033001 locomotion Effects 0.000 claims description 138
- 238000006073 displacement reaction Methods 0.000 claims description 33
- 230000010354 integration Effects 0.000 claims description 23
- 238000012512 characterization method Methods 0.000 claims description 20
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 208000019622 heart disease Diseases 0.000 claims description 17
- 238000007726 management method Methods 0.000 claims description 16
- 239000000853 adhesive Substances 0.000 claims description 15
- 230000001070 adhesive effect Effects 0.000 claims description 15
- 238000002554 cardiac rehabilitation Methods 0.000 claims description 14
- 238000013500 data storage Methods 0.000 claims description 12
- 239000000284 extract Substances 0.000 claims description 7
- 238000000034 method Methods 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 201000010099 disease Diseases 0.000 description 7
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 7
- 238000001514 detection method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000017531 blood circulation Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 239000012790 adhesive layer Substances 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 239000000523 sample Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 208000002330 Congenital Heart Defects Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005831 heart abnormality Effects 0.000 description 1
- 210000003709 heart valve Anatomy 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 210000002435 tendon Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention relates to the field of medical data analysis, in particular to an analysis management system based on heart rehabilitation data.
Description
Technical Field
The invention relates to the field of medical data analysis, in particular to an analysis management system based on heart rehabilitation data.
Background
Heart sounds are a compound sound generated by opening and closing of heart valves, diastole and contraction of tendons and muscles, impact of blood flow and vibration of heart vessel walls, are the most basic parameters for clinically evaluating the mechanical functional state of the heart, and various portable modern medical data analysis systems for analyzing heart sound data are generated along with the development of sensor technology and computer technology.
For example, chinese patent: CN108324266a discloses a household heart monitoring system based on electrocardiograph heart sound analysis, which comprises a portable electrocardiograph heart sound instrument and a disposable application thereof, wherein the disposable application comprises a patch with an adhesive layer, the patch is provided with a heart sound probe and a plurality of electrocardiograph electrodes, the electrocardiograph electrodes and the heart sound probe are exposed out of the adhesive layer, and an electrocardiograph and a heart sound chart are input into the electrocardiograph heart sound instrument; the electrocardiograph and the phonocardiogram which are synchronously collected in real time use the same time axis, and the heart rate of the user is obtained from the electrocardiograph; identifying electrocardio features in an electrocardiogram, positioning a heart sound chart by the electrocardio features, and searching heart sound features; and when the R wave does not correspond to the first heart sound S1, or the T wave does not correspond to the second heart sound S2, or the first heart sound S1 is abnormal, or the second heart sound S2 is abnormal, sending out heart abnormality alarm. The invention has the advantages of simultaneously monitoring the electrocardio and heart sound signals, positioning heart sound characteristics by the electrocardio characteristics, and being convenient for a user to monitor the heart state at home.
However, the prior art has the following problems,
in the prior art, the heart sound data are changed due to the change of the heart rhythm and the blood circulation rate when the tested person is in different degrees of movement, and in the actual situation, the movement load is larger, larger feedback can be generated on the heart sound data, so that the data characterization is enhanced, and partial hidden features are conveniently identified, for example, the partial abnormal hidden features can be displayed when the heart sound data are stronger.
Therefore, if the above features are not considered, hidden features may be omitted in actual cardiac rehabilitation data detection, and the data detection accuracy is not high.
Disclosure of Invention
In order to solve the problems that in the prior art, heart sound data are changed due to the change of heart rhythm and blood circulation rate when a tested person is in different degrees of motion, hidden features can be omitted in actual heart rehabilitation data detection, and data detection accuracy is low, the invention provides an analysis management system based on heart rehabilitation data, which comprises the following components:
the data acquisition module comprises an adhesive sheet, a sound receiving unit and an inertial sensor unit, wherein the sound receiving unit is arranged on one side of the adhesive sheet and used for acquiring heart sound data of the adhesive part of the adhesive sheet, and the inertial sensor unit is used for acquiring displacement parameters, and the displacement parameters comprise displacement quantity and displacement speed;
the data storage module comprises a plurality of sub-databases, each sub-database is used for storing a plurality of motion parameters of patients with different heart disease types and a plurality of heart sound data in different motion parameter intervals, the motion parameters are calculated based on displacement parameters acquired by the data acquisition module at a preset part of the patient, and the heart sound data in the motion parameter intervals are acquired in the process of acquiring the motion parameters, wherein the motion parameters are in the corresponding time periods of the motion parameter intervals;
the data analysis module is connected with the data acquisition module and the data storage module and comprises a data integration unit and a data analysis unit,
the data integration unit is used for integrating a plurality of heart sound data in the same motion parameter interval in each sub-database to obtain characterization heart sound data, and judging the characteristic motion parameter interval of each sub-database based on the fitting degree of the characterization heart sound data in each motion parameter interval in the sub-database and the characterization heart sound data in the rest sub-databases;
the data analysis unit responds to a preset input instruction, judges a sub-database to be called based on information contained in the preset input instruction, and determines a characteristic motion parameter interval corresponding to the sub-database;
the data acquisition module is used for acquiring the data acquired by the data acquisition module in real time, correspondingly calculating motion parameters based on displacement parameters, extracting heart sound data belonging to the characteristic motion parameter interval from the acquired data, comparing the heart sound data with heart sound data in the characteristic motion parameter interval in the called sub-database, and judging whether the heart sound data is abnormal or not based on a comparison result;
the predetermined input instructions include the type of heart disease to be compared.
Further, the data integration unit integrates a plurality of heart sound data in different motion parameter intervals in each sub-database to obtain characterization heart sound data, wherein,
the data integration unit calculates and sorts the fitting degree average value of each heart sound data and the rest heart sound data, and determines the heart sound data corresponding to the maximum fitting degree average value as the representation heart sound data.
Further, the data integration unit calculates fitting parameters corresponding to each motion parameter interval according to the formula (1) based on the fitting degree of the representing heart sound data in each motion parameter interval in the sub-database and the representing heart sound data in the rest sub-databases,
（1）
in the formula (1), E represents a fitting parameter, ki represents the fitting degree of the heart sound data represented in the motion parameter interval in the sub-database and the heart sound data represented in the same motion parameter interval in the rest i sub-database, and n represents the total amount of the sub-databases.
Further, the data integration unit judges the characteristic motion parameter interval of each sub-database based on the fitting degree of the characteristic heart sound data in each motion parameter interval in the sub-database and the characteristic heart sound data in the rest sub-databases, wherein,
and the data integration unit calculates fitting parameters corresponding to all the motion parameter intervals of the sub-database, sorts the fitting parameters, screens out the motion parameter interval corresponding to the minimum fitting parameter, and determines the motion parameter interval as a characteristic motion parameter interval.
Further, the data analysis unit determines a sub-database to be called based on information contained in the predetermined input instruction, wherein,
the data analysis unit determines heart disease categories to be compared contained in the preset input instruction, and invokes a sub-database for storing a plurality of motion parameters of a patient in the heart disease categories and a plurality of heart sound data in different motion parameter intervals.
Further, the data analysis unit correspondingly calculates motion parameters according to a formula (2) based on the displacement parameters,
（2）
in the formula (2), P represents a motion parameter, L represents a total displacement amount, V represents a displacement velocity average value, and V0 represents a velocity reference value.
Further, the data analysis unit extracts heart sound data belonging to the characteristic motion parameter interval from the acquired data, wherein,
the data analysis unit calculates motion parameters in real time based on the acquired displacement parameters, records that the motion parameters belong to corresponding time periods in the characteristic motion parameter interval, and extracts heart sound data acquired by the data storage module in the time periods.
Further, the data analysis unit compares the heart sound data with heart sound data in a characteristic motion parameter interval in the called sub-database, judges whether the heart sound data has abnormality based on a comparison result, wherein,
the data analysis unit solves the average value of the fitting degree of the heart sound data and each heart sound data in the characteristic motion parameter interval in the called sub-database, compares the average value of the fitting degree with a preset threshold value of the fitting degree,
under a preset comparison condition, the data analysis unit judges that the heart sound data is abnormal;
the preset comparison condition is that the average fitting degree is larger than a preset fitting degree threshold value.
Further, a vibration unit is further arranged on the adhesive sheet of the data acquisition module, and is used for vibrating when the data analysis unit judges that the heart sound data is abnormal.
Further, the data analysis module is connected with an external touch display screen so as to input a preset input instruction through the touch display screen.
Compared with the prior art, the heart sound data processing system is provided with the data acquisition module, the data storage module and the data analysis module, wherein the data storage module comprises a plurality of sub-databases for storing a plurality of motion parameters of patients with different heart disease types and a plurality of heart sound data in different motion parameter intervals, the data analysis module can integrate the heart sound data in each sub-database and judge the characteristic motion parameter interval of the sub-database, the data analysis module extracts heart sound data which belongs to the corresponding heart sound data in the characteristic motion parameter interval in the data acquired by the data acquisition module, compares the heart sound data with heart sound data in the characteristic motion parameter interval in the called sub-database, judges whether the heart sound data is abnormal or not based on the comparison result, considers the characterization of the heart sound data of different disease types under different motion parameters through the scheme, and improves the judgment precision when the heart sound data is abnormally judged.
In particular, the characteristic motion parameter interval of each sub-database is judged, in the actual situation, the motion parameter is calculated based on the displacement parameter, the motion intensity of a tested person is represented, further, heart sound data characterizations are different under different motion intensities, heart sound characteristics of partial disease seeds are required to be distinguished after certain motion intensity is ensured, heart sound data association changes, therefore, the characteristic motion parameter interval of each sub-database is determined based on the fitting degree of the characteristic heart sound data in each motion parameter interval in each sub-database and the characteristic heart sound data in the rest sub-databases, the characteristic motion parameters of the sub-databases characterize that heart sound characteristics of patients corresponding to the disease seeds have stronger data characterizations compared with other disease seeds when the motion parameter belongs to the characteristic motion interval, further, the data identification redundancy is reduced, the characteristic omission is avoided, and the judgment precision when the heart sound data is abnormally judged is improved.
In particular, when the heart sound data of a patient is evaluated to be abnormal in rehabilitation training of the patient, the data analysis unit responds to a preset input instruction to input the heart disease category corresponding to the detected patient, the data analysis unit can screen heart sound data belonging to a characteristic motion parameter interval from the acquired heart sound data based on the judging result of the data integration unit, the screened heart sound data has stronger data characterization and is convenient to fit and compare, in actual conditions, as the similarity among the heart sound data is higher, the heart sound data of a long period is compared, distinguishing characteristics can be covered, therefore, only one section of heart sound data with stronger data markedness is selected to be compared with the corresponding heart sound data in the sub-database, the data operation amount is reduced, and the judging precision of abnormal judgment of the heart sound data is improved.
In particular, the invention also provides the vibration unit on the adhesive sheet of the data acquisition module, and the data analysis unit vibrates when judging that the heart sound data is abnormal, so as to prompt a tested person to know the detection result and improve the use experience of the system.
Drawings
FIG. 1 is a schematic diagram of an analysis management system based on cardiac rehabilitation data according to an embodiment of the invention;
fig. 2 is a schematic diagram of a data analysis module according to an embodiment of the invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, unless explicitly specified and defined otherwise, the term "connected" should be construed broadly, and may be, for example, mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 and fig. 2, a schematic structural diagram of an analysis management system based on cardiac rehabilitation data and a schematic structural diagram of a data analysis module according to an embodiment of the invention are shown, and the analysis management system based on cardiac rehabilitation data of the invention includes:
the data acquisition module comprises an adhesive sheet, a sound receiving unit and an inertial sensor unit, wherein the sound receiving unit is arranged on one side of the adhesive sheet and used for acquiring heart sound data of the adhesive part of the adhesive sheet, and the inertial sensor unit is used for acquiring displacement parameters, and the displacement parameters comprise displacement quantity and displacement speed;
the data storage module comprises a plurality of sub-databases, each sub-database is used for storing a plurality of motion parameters of patients with different heart disease types and a plurality of heart sound data in different motion parameter intervals, the motion parameters are calculated based on displacement parameters acquired by the data acquisition module at a preset part of the patient, and the heart sound data in the motion parameter intervals are acquired in the process of acquiring the motion parameters, wherein the motion parameters are in the corresponding time periods of the motion parameter intervals;
the data analysis module is connected with the data acquisition module and the data storage module and comprises a data integration unit and a data analysis unit,
the data integration unit is used for integrating a plurality of heart sound data in the same motion parameter interval in each sub-database to obtain characterization heart sound data, and judging the characteristic motion parameter interval of each sub-database based on the fitting degree of the characterization heart sound data in each motion parameter interval in the sub-database and the characterization heart sound data in the rest sub-databases;
the data analysis unit responds to a preset input instruction, judges a sub-database to be called based on information contained in the preset input instruction, and determines a characteristic motion parameter interval corresponding to the sub-database;
the data acquisition module is used for acquiring the data acquired by the data acquisition module in real time, correspondingly calculating motion parameters based on displacement parameters, extracting heart sound data belonging to the characteristic motion parameter interval from the acquired data, comparing the heart sound data with heart sound data in the characteristic motion parameter interval in the called sub-database, and judging whether the heart sound data is abnormal or not based on a comparison result;
the predetermined input instructions include the type of heart disease to be compared.
Specifically, the invention does not limit the specific form of the sub-database in the data storage module, and the sub-database only needs to meet the storage, receiving and transmitting functions of the data, which is the prior art and is not repeated.
In particular, the specific structure of the data analysis module is not limited, and it may be composed of logic components or a combination of logic components, and the logic components include a field programmable part, a computer, and a microprocessor.
Specifically, when the data acquisition module is configured at a predetermined position of the patient in this embodiment, the predetermined position is a chest portion of the patient, and is preferably located at a position where the heart is located.
Specifically, the specific structure of the sound receiving unit is not limited, the sound receiving unit can be a combination of a sound receiver and a communication module so as to transmit data detected by the sound receiver to the data analysis module, the specific structure of the inertial sensor unit is also not limited, the specific structure of the inertial sensor unit can be a combination of an inertial sensor and the communication module, and the inertial sensor is commonly used for detecting displacement, speed, acceleration and speed direction, which is not repeated in the prior art.
Specifically, for the motion parameters and the sources of heart sound data stored in the sub-databases, in this embodiment, the data acquisition module may be used to acquire in advance, and acquire and store the corresponding motion parameters and heart sound data in the process of moving patients with different heart disease types in the corresponding sub-databases.
Specifically, the method for calculating the fitting degree of the heart sound data is not specifically limited, and in this embodiment, the fitting degree, such as frequency, intensity, time course, etc., may be considered from multiple dimensions of the heart sound data by using a voice analysis tool in a MATLAB tool box, which is not described in detail in this embodiment.
Specifically, the data integration unit integrates a plurality of heart sound data in different motion parameter intervals in each sub-database to obtain characterization heart sound data, wherein,
the data integration unit calculates and sorts the fitting degree average value of each heart sound data and the rest heart sound data, and determines the heart sound data corresponding to the maximum fitting degree average value as the representation heart sound data.
Specifically, the data integration unit calculates fitting parameters corresponding to each motion parameter interval according to the formula (1) based on the fitting degree of the representing heart sound data in each motion parameter interval in the sub-database and the representing heart sound data in the rest sub-databases,
（1）
in the formula (1), E represents a fitting parameter, ki represents the fitting degree of the heart sound data represented in the motion parameter interval in the sub-database and the heart sound data represented in the same motion parameter interval in the rest i sub-database, and n represents the total amount of the sub-databases.
Specifically, the data integration unit determines characteristic motion parameter intervals of each sub-database based on fitting degree of the characteristic heart sound data in each motion parameter interval in the sub-database and the characteristic heart sound data in the rest sub-databases, wherein,
and the data integration unit calculates fitting parameters corresponding to all the motion parameter intervals of the sub-database, sorts the fitting parameters, screens out the motion parameter interval corresponding to the minimum fitting parameter, and determines the motion parameter interval as a characteristic motion parameter interval.
According to the method, characteristic motion parameter intervals of all the sub-databases are judged, in an actual situation, motion parameters are calculated based on displacement parameters, motion intensity of a measured person is represented, and further, heart sound characteristics of partial disease seeds are different in heart sound data characterizations under different motion intensities, certain motion intensity is guaranteed, heart sound data is distinguished after association changes, therefore, characteristic motion parameter intervals of all the sub-databases are determined based on fitting degrees of the characteristic heart sound data in all the motion parameter intervals in the sub-databases and the characteristic heart sound data in the rest sub-databases, characteristic motion parameters of the sub-databases characterize heart sound characteristics of patients corresponding disease seeds when the motion parameters belong to the characteristic motion intervals, the heart sound characteristics of the patients have stronger data characterizations compared with other disease seeds, and further, the heart sound characteristics are easy to identify during fitting comparison, so that data identification redundancy is reduced, characteristic omission is avoided, and judgment accuracy during abnormal judgment of the heart sound data is improved.
Specifically, the data analysis unit determines a sub-database to be called based on information contained in the predetermined input instruction, wherein,
the data analysis unit determines heart disease categories to be compared contained in the preset input instruction, and invokes a sub-database for storing a plurality of motion parameters of a patient in the heart disease categories and a plurality of heart sound data in different motion parameter intervals.
Specifically, the data analysis unit correspondingly calculates motion parameters according to a formula (2) based on displacement parameters,
（2）
in the formula (2), P represents a motion parameter, L represents a total displacement amount, V represents a displacement velocity average value, and V0 represents a velocity reference value.
V0 is obtained by pre-recording, the speed values detected by the inertial sensor units when a plurality of patients perform rehabilitation exercises are counted, the average value of the speed values is solved, and the average value of the speed values is determined to be a speed reference value.
In particular, the data analysis unit extracts heart sound data belonging to the characteristic motion parameter interval from the acquired data, wherein,
the data analysis unit calculates motion parameters in real time based on the acquired displacement parameters, records that the motion parameters belong to corresponding time periods in the characteristic motion parameter interval, and extracts heart sound data acquired by the data storage module in the time periods.
Specifically, the data analysis unit compares the heart sound data with heart sound data in a characteristic motion parameter interval in the called sub-database, judges whether the heart sound data has abnormality based on a comparison result, wherein,
the data analysis unit solves the average value of the fitting degree of the heart sound data and each heart sound data in the characteristic motion parameter interval in the called sub-database, compares the average value of the fitting degree with a preset threshold value of the fitting degree,
under a preset comparison condition, the data analysis unit judges that the heart sound data is abnormal;
the preset comparison condition is that the average fitting degree is larger than a preset fitting degree threshold value.
Specifically, in this embodiment, the fitness threshold Ne is obtained by measuring heart sound data obtained when a plurality of patients of the same heart disease type are active, solving the fitness among the heart sound data, and solving the average value N0 of the fitness, and setting ne=n0×β, where β represents the precision coefficient, and 1.05 < β < 1.1.
In the invention, when the heart sound data of a patient is evaluated to be abnormal in rehabilitation training of the patient, the data analysis unit responds to a preset input instruction to input the heart disease category corresponding to the detected patient, the data analysis unit can screen the heart sound data belonging to the characteristic motion parameter interval from the acquired heart sound data based on the judging result of the data integration unit, the screened heart sound data has stronger data characterization property and is convenient for fitting and comparison, in the practical situation, as the similarity between the heart sound data is higher, the heart sound data in a long period is adopted for comparison, distinguishing characteristics can be covered, therefore, only one section of heart sound data with stronger data markedness is selected for comparison with the corresponding heart sound data in the sub-database, the data operand is reduced, and the judging precision when the heart sound data is abnormally judged is improved.
Specifically, the sticking sheet of the data acquisition module is also provided with a vibration unit used for vibrating when the data analysis unit judges that the heart sound data is abnormal.
Further, the data analysis module is connected with an external touch display screen so as to input a preset input instruction through the touch display screen.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (10)
1. An analytical management system based on cardiac rehabilitation data, comprising:
the data acquisition module comprises an adhesive sheet, a sound receiving unit and an inertial sensor unit, wherein the sound receiving unit is arranged on one side of the adhesive sheet and used for acquiring heart sound data of the adhesive part of the adhesive sheet, and the inertial sensor unit is used for acquiring displacement parameters, and the displacement parameters comprise displacement quantity and displacement speed;
the data storage module comprises a plurality of sub-databases, each sub-database is used for storing a plurality of motion parameters of patients with different heart disease types and a plurality of heart sound data in different motion parameter intervals, the motion parameters are calculated based on displacement parameters acquired by the data acquisition module at a preset part of the patient, and the heart sound data in the motion parameter intervals are acquired in the process of acquiring the motion parameters, wherein the motion parameters are in the corresponding time periods of the motion parameter intervals;
the data analysis module is connected with the data acquisition module and the data storage module and comprises a data integration unit and a data analysis unit,
the data integration unit is used for integrating a plurality of heart sound data in the same motion parameter interval in each sub-database to obtain characterization heart sound data, and judging the characteristic motion parameter interval of each sub-database based on the fitting degree of the characterization heart sound data in each motion parameter interval in the sub-database and the characterization heart sound data in the rest sub-databases;
the data analysis unit responds to a preset input instruction, judges a sub-database to be called based on information contained in the preset input instruction, and determines a characteristic motion parameter interval corresponding to the sub-database;
the data acquisition module is used for acquiring the data acquired by the data acquisition module in real time, correspondingly calculating motion parameters based on displacement parameters, extracting heart sound data belonging to the characteristic motion parameter interval from the acquired data, comparing the heart sound data with heart sound data in the characteristic motion parameter interval in the called sub-database, and judging whether the heart sound data is abnormal or not based on a comparison result;
the predetermined input instructions include the type of heart disease to be compared.
2. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the data integration unit integrates a plurality of heart sound data in different motion parameter intervals in each sub-database to obtain characterization heart sound data, wherein,
the data integration unit calculates and sorts the fitting degree average value of each heart sound data and the rest heart sound data, and determines the heart sound data corresponding to the maximum fitting degree average value as the representation heart sound data.
3. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the data integration unit calculates fitting parameters corresponding to each motion parameter interval according to formula (1) based on fitting degrees of the characteristic heart sound data in each motion parameter interval in the sub-databases and the characteristic heart sound data in the remaining sub-databases,
in the formula (1), E represents a fitting parameter, ki represents the fitting degree of the heart sound data represented in the motion parameter interval in the sub-database and the heart sound data represented in the same motion parameter interval in the rest i sub-database, and n represents the total amount of the sub-databases.
4. The analysis management system based on cardiac rehabilitation data according to claim 3, wherein the data integration unit determines a characteristic motion parameter interval of each sub-database based on a fitting degree of the characteristic heart sound data in each motion parameter interval in the sub-database to the characteristic heart sound data in the remaining sub-databases,
and the data integration unit calculates fitting parameters corresponding to all the motion parameter intervals of the sub-database, sorts the fitting parameters, screens out the motion parameter interval corresponding to the minimum fitting parameter, and determines the motion parameter interval as a characteristic motion parameter interval.
5. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the data analysis unit determines a sub-database to be called based on information contained in the predetermined input instruction, wherein,
the data analysis unit determines heart disease categories to be compared contained in the preset input instruction, and invokes a sub-database for storing a plurality of motion parameters of a patient in the heart disease categories and a plurality of heart sound data in different motion parameter intervals.
6. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the data analysis unit correspondingly calculates the motion parameter according to formula (2) based on the displacement parameter,
in the formula (2), P represents a motion parameter, L represents a total displacement amount, V represents a displacement velocity average value, and V0 represents a velocity reference value.
7. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the data analysis unit extracts heart sound data belonging to the characteristic motion parameter interval from the acquired data, wherein,
the data analysis unit calculates motion parameters in real time based on the acquired displacement parameters, records that the motion parameters belong to corresponding time periods in the characteristic motion parameter interval, and extracts heart sound data acquired by the data storage module in the time periods.
8. The analysis management system based on cardiac rehabilitation data according to claim 7, wherein the data analysis unit compares the heart sound data with heart sound data in a characteristic motion parameter interval in the called sub-database, determines whether there is an abnormality in the heart sound data based on the comparison result, wherein,
the data analysis unit solves the average value of the fitting degree of the heart sound data and each heart sound data in the characteristic motion parameter interval in the called sub-database, compares the average value of the fitting degree with a preset threshold value of the fitting degree,
under a preset comparison condition, the data analysis unit judges that the heart sound data is abnormal;
the preset comparison condition is that the average fitting degree is larger than a preset fitting degree threshold value.
9. The analysis management system based on cardiac rehabilitation data according to claim 1, wherein the adhesive sheet of the data acquisition module is further provided with a vibration unit for vibrating when the data analysis unit determines that there is an abnormality in the heart sound data.
10. The cardiac rehabilitation data-based analysis management system according to claim 1, wherein the data analysis module is connected to an external touch display screen to input a predetermined input instruction through the touch display screen.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311034707.XA CN116759102B (en) | 2023-08-17 | 2023-08-17 | Analysis management system based on heart rehabilitation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311034707.XA CN116759102B (en) | 2023-08-17 | 2023-08-17 | Analysis management system based on heart rehabilitation data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116759102A true CN116759102A (en) | 2023-09-15 |
| CN116759102B CN116759102B (en) | 2023-12-05 |
Family
ID=87948169
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311034707.XA Active CN116759102B (en) | 2023-08-17 | 2023-08-17 | Analysis management system based on heart rehabilitation data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116759102B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020043997A (en) * | 2000-12-05 | 2002-06-14 | 윤종용 | Heart sound classification method by principal components analysis |
| CN101518439A (en) * | 2009-03-24 | 2009-09-02 | 重庆大学 | Cardiac function detecting system based on cardiac sound and electrocardiogram |
| CN101919704A (en) * | 2009-08-26 | 2010-12-22 | 济南大学 | Heart sound signal positioning and segmenting method |
| KR20130048099A (en) * | 2011-11-01 | 2013-05-09 | 경북대학교 산학협력단 | An apparatus of heart sound analysis base on simplicity and the method |
| US20140343446A1 (en) * | 2013-05-15 | 2014-11-20 | Sharp Laboratories Of America, Inc. | Model-Based Method for Assessing Acoustic Signal Quality in Heart Monitoring Device |
| JP2014233598A (en) * | 2013-06-05 | 2014-12-15 | 国立大学法人山口大学 | Auscultatory cardiac sound signal processing method, auscultatory cardiac sound signal processing apparatus, and auscultatory cardiac sound signal processing program |
| CN107049357A (en) * | 2017-05-25 | 2017-08-18 | 西华大学 | A kind of cardiechema signals analytic method, apparatus and system |
| CN111370120A (en) * | 2020-02-17 | 2020-07-03 | 深圳大学 | Method for detecting diastolic dysfunction based on heart sound signals |
| US20210090734A1 (en) * | 2019-09-20 | 2021-03-25 | Kaushik Kunal SINGH | System, device and method for detection of valvular heart disorders |
| CN112971839A (en) * | 2021-02-05 | 2021-06-18 | 云南大学 | Heart sound classification method based on feedforward convolutional neural network |
| KR20210072866A (en) * | 2019-12-09 | 2021-06-18 | (의료)길의료재단 | Heart Disease Diagnosis System and Method Using Cardiac Sound Data |
| CN114912521A (en) * | 2022-05-10 | 2022-08-16 | 平安科技（深圳）有限公司 | Heart data processing method and device, computer equipment and storage medium |
| CN115762578A (en) * | 2022-12-02 | 2023-03-07 | 北京理工大学 | Interpretable heart sound abnormity identification method and system based on fractional domain Fourier transform |
| CN115828168A (en) * | 2022-12-09 | 2023-03-21 | 杭州电子科技大学 | Heart sound segmentation method and device based on hidden Markov cardiac cycle |
-
2023
- 2023-08-17 CN CN202311034707.XA patent/CN116759102B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020043997A (en) * | 2000-12-05 | 2002-06-14 | 윤종용 | Heart sound classification method by principal components analysis |
| CN101518439A (en) * | 2009-03-24 | 2009-09-02 | 重庆大学 | Cardiac function detecting system based on cardiac sound and electrocardiogram |
| CN101919704A (en) * | 2009-08-26 | 2010-12-22 | 济南大学 | Heart sound signal positioning and segmenting method |
| KR20130048099A (en) * | 2011-11-01 | 2013-05-09 | 경북대학교 산학협력단 | An apparatus of heart sound analysis base on simplicity and the method |
| US20140343446A1 (en) * | 2013-05-15 | 2014-11-20 | Sharp Laboratories Of America, Inc. | Model-Based Method for Assessing Acoustic Signal Quality in Heart Monitoring Device |
| JP2014233598A (en) * | 2013-06-05 | 2014-12-15 | 国立大学法人山口大学 | Auscultatory cardiac sound signal processing method, auscultatory cardiac sound signal processing apparatus, and auscultatory cardiac sound signal processing program |
| CN107049357A (en) * | 2017-05-25 | 2017-08-18 | 西华大学 | A kind of cardiechema signals analytic method, apparatus and system |
| US20210090734A1 (en) * | 2019-09-20 | 2021-03-25 | Kaushik Kunal SINGH | System, device and method for detection of valvular heart disorders |
| KR20210072866A (en) * | 2019-12-09 | 2021-06-18 | (의료)길의료재단 | Heart Disease Diagnosis System and Method Using Cardiac Sound Data |
| CN111370120A (en) * | 2020-02-17 | 2020-07-03 | 深圳大学 | Method for detecting diastolic dysfunction based on heart sound signals |
| CN112971839A (en) * | 2021-02-05 | 2021-06-18 | 云南大学 | Heart sound classification method based on feedforward convolutional neural network |
| CN114912521A (en) * | 2022-05-10 | 2022-08-16 | 平安科技（深圳）有限公司 | Heart data processing method and device, computer equipment and storage medium |
| CN115762578A (en) * | 2022-12-02 | 2023-03-07 | 北京理工大学 | Interpretable heart sound abnormity identification method and system based on fractional domain Fourier transform |
| CN115828168A (en) * | 2022-12-09 | 2023-03-21 | 杭州电子科技大学 | Heart sound segmentation method and device based on hidden Markov cardiac cycle |
Non-Patent Citations (1)
| Title |
|---|
| 成谢锋 等: "运动与年龄对心音混沌特性影响规律的研究", 振动与冲击, no. 01 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116759102B (en) | 2023-12-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103997955B (en) | The method of the atrial fibrillation in detection electrocardiogram | |
| CN100553555C (en) | Adaptive physiological monitoring system and using method thereof | |
| EP1284645B1 (en) | System and device for multi-scale analysis and representation of electrocardiographic data | |
| JP3980969B2 (en) | Heart rate measurement system, heart rate measurement method, heart rate measurement program, and recording medium recording the program | |
| AU2004285457B2 (en) | Analysis of auscultatory sounds using single value decomposition | |
| US20110263994A1 (en) | Method and Apparatus for Acquiring and Analyzing Data Relating to a Physiological Condition of a Subject | |
| CN105943021A (en) | Wearable heart rhythm monitoring device and heart rhythm monitoring system | |
| US11903724B2 (en) | Method and a system for detecting a respiratory event of a subject and a method for forming a model for detecting a respiratory event | |
| CN103313662A (en) | System, stethoscope and method for indicating risk of coronary artery disease | |
| CN101911083A (en) | System, method and device for predicting sudden cardiac death risk | |
| CN104661703A (en) | Corrective prompting system for appropriate chest compressions | |
| JP2006510447A (en) | Advanced patient management using synthetic parameter indices | |
| CN102186416A (en) | Apnea detection program, apnea detector, and apnea detection method | |
| CN105852851A (en) | Atrial fibrillation detection system | |
| US11730424B2 (en) | Methods and systems to detect eating | |
| WO2004041086A1 (en) | Accelerometer with an automatic calibration | |
| CN116759102B (en) | Analysis management system based on heart rehabilitation data | |
| CN206007242U (en) | A kind of wearable cardiac rhythm monitoring device | |
| CN114652288A (en) | Non-cuff type dynamic blood pressure measuring system | |
| CN113208573A (en) | Support wearable equipment of PPG + ECG function | |
| CN109984743A (en) | A kind of health indicator analysis method based on resonance cell | |
| RU2779697C1 (en) | Method for registering a person's post-covid state according to a cardiorhythmogram | |
| AU2021103601A4 (en) | System and method for monitoring post covid patient using machine learning and block chain | |
| US20240105344A1 (en) | System for constructing hyperkalemia prediction algorithm through electrocardiogram, method for constructing hyperkalemia prediction algorithm through electrocardiogram by using same, and hyperkalemia prediction system using electrocardiogram | |
| CN210354670U (en) | Physiological monitoring and analysis system based on hybrid sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |