CN210354670U - Physiological monitoring and analysis system based on hybrid sensing - Google Patents
Physiological monitoring and analysis system based on hybrid sensing Download PDFInfo
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- CN210354670U CN210354670U CN201820097024.7U CN201820097024U CN210354670U CN 210354670 U CN210354670 U CN 210354670U CN 201820097024 U CN201820097024 U CN 201820097024U CN 210354670 U CN210354670 U CN 210354670U
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The utility model relates to a physiology detects and analytic system based on mix sensing, including sensor, the data record unit that is used for collecting data, be used for data analysis's data analysis unit and be used for accepting the report receiving element of analysis report. The utility model discloses a collect the physiological data of the biological each side of target, realized the omnidirectional analysis to the biological physiological information of target for the result of analysis is more accurate reliable, and convenient and fast, has improved the efficiency of physiology monitoring and disease detection.
Description
Technical Field
The utility model relates to a disease diagnosis technique especially relates to a physiology detects and analytic system based on mix sensing.
Background
The judgment of the disease or health state in the prior art is generally judged by a detection machine. However, the detection method is not accurate due to interference of external factors and incomplete physiological data acquired due to condition limitation, and is prone to cause misdiagnosis.
SUMMERY OF THE UTILITY MODEL
To the defect or not enough that exist among the prior art, the utility model discloses the technical problem that will solve is: the technical scheme can solve the problem that misdiagnosis is easily caused in health state judgment.
In order to achieve the above object, the technical solution of the present invention is a physiological detection and analysis system based on hybrid sensing, which comprises a plurality of sensors, a data recording unit, a data analysis unit and a report receiving unit; the data analysis unit: the data recording unit is used for processing the physiological information data of the target organism collected by the sensor and sending an analysis report to the report receiving unit.
As a further improvement of the present invention, the sensor includes an electrocardiogram sensor, an accelerometer, a motion sensor, and a pressure sensor; the data recording unit comprises a central processor for measuring, recording or registering physiological information data collected by the sensor, the central processor: and is also used for sending the physiological information data to the data analysis unit.
As a further improvement of the present invention, the data analysis unit includes a past database, a real-time collection database, and an analysis platform capable of establishing and training an algorithm statistical model by a machine learning method;
the past database includes: past physiological information data of the target living being and past physiological information group data of a living being of the same or different race or breed as the target living being;
the real-time acquisition database includes physiological information data of the target living being.
As a further improvement of the present invention, the data analysis platform is further configured to: improving the signal-to-noise ratio of the physiological information data, and extracting time domain and/or frequency domain characteristics of different physiological information data by a characteristic extraction method.
Also provides a physiological detection and analysis method based on mixed sensing, which comprises the following steps:
s1, establishing an algorithm statistical model through an experiment;
s2, collecting physiological information data of a target organism; wherein the physiological information data comprises electrophysiological information, mechanical physiological information and physical movement activity data of the target organism; the comprehensiveness of the information is ensured by collecting the electrophysiological information, the mechanical physiological information, the body movement activity data and the like of the target organism.
S3, performing noise reduction processing on the physiological information data through a signal processing method, and extracting time domain characteristics and/or frequency domain characteristics of different physiological information data through a characteristic extraction method; the feature extraction method is one or more of Fourier transform, frequency band power calculation, time-frequency analysis, wavelet decomposition and waveform detection, and can also be one or more of other feature extraction methods capable of extracting time domain features or frequency domain features of physiological information data; the signal to noise ratio of the physiological information data is improved, and distorted or abnormal data information caused by external interference or other uncontrollable factors is eliminated.
S4, inputting the time domain characteristics and/or the frequency domain characteristics extracted from the electrophysiological information, the mechanical physiological information and the body movement activity data into an algorithm statistical model for operation to obtain an output target; the algorithm statistical model comprises a heart rate checking algorithm statistical model, a blood pressure checking algorithm statistical model, a heart rate variation checking algorithm statistical model, a respiratory rate checking algorithm statistical model, a blood pressure checking algorithm statistical model, an emotion checking algorithm statistical model, a cardiac output quantity checking algorithm statistical model and a physical exercise checking algorithm statistical model. The output target comprises heart rate analysis, blood pressure analysis, heart rate variation analysis, respiration rate analysis, blood pressure analysis, emotion analysis, cardiac output analysis and body movement analysis which correspond to the algorithm statistical model, and can also comprise other algorithm statistical models of physiological information. And establishing an algorithm statistical model corresponding to the physiological information data through different experimentally established physiological information data, inputting the collected physiological information data of the target organism into the corresponding algorithm statistical model, and performing comparison calculation analysis to obtain a corresponding output target, wherein the output target comprises heart rate analysis, blood pressure analysis, heart rate variation analysis and the like corresponding to the algorithm statistical model.
S5, the output target is used as an analysis report and reported back to a report receiving unit, or the output target is compared with past databases respectively to obtain an analysis report and reported back to a report receiving unit, wherein the past databases include: past physiological information data of the target living body and past physiological information group data of living bodies of the same or different race and breed with the target living body. Physiological data information of the target organism or an organism having the same or similar race, family, order, age and size with the target organism is generally stored in a past database, an analysis report of the target organism is obtained by comparing an output target with the data, and the data analysis unit sends the analysis report to a report receiving unit for a professional to give suggestions according to the report.
As a further improvement of the present invention, the step S1 further includes the following steps:
s11, collecting experimental physiological information data of an experimental object through a sensor;
s12, improving the signal-to-noise ratio of experimental physiological information data through a signal processing method;
s13, extracting time domain features and/or frequency domain features of different experimental physiological information data through a feature extraction method, wherein the feature extraction method comprises the following steps: fourier transform, frequency band power calculation, time-frequency analysis, wavelet decomposition and waveform detection;
s14, inputting the time domain characteristics and/or the frequency domain characteristics of the experimental physiological information data into a machine learning system to establish a statistical model, and training the statistical model to obtain an algorithm statistical model.
As a further improvement of the present invention, step S14 further includes the following steps:
s141, the machine learning system presets standard statistical test parameters and acceptable deviation degree of a preset algorithm result;
s142, the machine learning system selects a subset of relevant time domain features and/or frequency domain features of the experimental physiological information data through a feature selection method to construct models of different combinations, compares the operation result of the statistical model with the physiological result obtained through a standard measurement method, and checks whether the operation result meets preset statistical test parameters and the acceptable result deviation degree;
s143, if the statistical model does not meet the requirements, the tested time domain features and/or frequency domain features are removed from the statistical model;
and S144, constructing an algorithm statistical model by selecting the feature subset with the highest accuracy and the highest statistical parameter value.
As a further improvement of the present invention, the electrophysiological information includes an electrocardiogram and an electrical respiration measuring chart.
As a further improvement of the present invention, the mechanical physiological information includes a heart vibration diagram, a ballistocardiogram and a mechanical respiration measurement diagram.
As a further improvement of the present invention, the output target includes body movement, respiration rate, heart rate variation, blood pressure, emotion, cardiac output and body movement.
The utility model has the advantages that: the utility model discloses a collect the physiological data of the biological each side of target, realized the omnidirectional analysis to the biological physiological information of target for the result of analysis is more accurate reliable, and convenient and fast, has improved the efficiency of physiology monitoring and disease detection.
Drawings
Fig. 1 is a flow chart of the detection provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention;
FIG. 3 is a time domain data image extracted from electrophysiological signals provided by the present invention;
FIG. 4 is a frequency domain data image extracted from electrophysiological signals provided by the present invention;
fig. 5 is a time domain data image extracted from a mechanical physiological signal provided by the present invention;
fig. 6 is a frequency domain data image extracted from the mechanical physiological signal provided by the present invention;
FIG. 7 is a cross-data image between the time domain features of electrophysiological and mechanical signal extraction provided by the present invention;
FIG. 8 is a diagram of the data distribution of the correlation of the time domain and/or frequency domain features extracted from the electrophysiological signals and the mechanical physiological signals;
FIG. 9 is an image of correlation data between time domain features extracted from electrophysiological and mechanical signals to analyze cardiac output and related parameter images provided by the present invention;
fig. 10 is a side view of a data collection structure of an analysis system provided by the present invention;
FIG. 11 is a process diagram of folding or unfolding of a data collection structure of an analysis system provided by the present invention;
FIG. 12 is an embodiment of the present invention providing for collecting target biological data;
wherein the numbers indicate: 11-ecg sensor 12-accelerator 13-pressure sensor 2-data registering unit 3-data analyzing unit 4-report receiving unit 51-mechanical activity sensor.
Detailed Description
The present invention will be further described with reference to the following description and embodiments.
As shown in fig. 1, the utility model provides a method for monitoring and analyzing of physiological monitoring and analyzing system based on hybrid sensing, which comprises:
s1, establishing an algorithm statistical model through an experiment;
specifically, the step S1 further includes the following steps:
s11, collecting experimental physiological information data of an experimental object through a sensor;
large amounts of data (human and/or animal, same and different race/breed, healthy and unhealthy) were collected at the time of the experiment: the comprehensive data collection is beneficial to establishing a comprehensive algorithm statistical model, so that the structure of a subsequent application for analyzing the physiological state of a target organism is more accurate.
S12, improving the signal-to-noise ratio of experimental physiological information data through a signal processing method; generally, when data is collected, there are interference data which may disturb the analysis result or even cause misdiagnosis, and it is necessary to improve the signal-to-noise ratio of the data collected from the sensor, such as electrophysiological signals and mechanical physiological signals, by a signal processing (signal processing) method suitable for the data before inputting the data into the machine learning system.
S13, extracting time domain features and/or frequency domain features of different experimental physiological information data through a feature extraction method, wherein the feature extraction method comprises the following steps: one or more of Fourier transform, frequency band power calculation, time-frequency analysis, wavelet decomposition, waveform detection and the like, and performing feature extraction on different experimental physiological information data through one or more of the feature extraction methods; the time domain features and/or frequency domain features of the extracted experimental physiological information data are typically different time domain and/or frequency domain features. The signal processing method applied to "feature extraction" includes, but is not limited to, Fourier Transform (Fourier Transform), frequency band power calculation (frequency band power calculation), time frequency analysis (time frequency analysis), wavelet decomposition (wavelet decomposition), and waveform detection (amplitude change and time position), and the system can extract relevant information by itself.
S14, inputting the time domain characteristics and/or the frequency domain characteristics of the experimental physiological information data into a machine learning system to establish a statistical model, and training the statistical model to obtain an algorithm statistical model.
Further, step S14 includes the following steps:
s141, the machine learning system presets standard statistical test parameters and acceptable deviation degree of a preset algorithm result; for example, the standard statistical test parameters are preset to be greater than 95% (i.e. p-value < 0.05), the significance level is a value that is determined by the subject to be statistically studied, and the acceptable deviation from blood pressure is set to be <1 mmHg.
S142, the machine learning system selects the subsets of the relevant time domain features and/or frequency domain features of the experimental physiological information data through a feature selection method to construct models with different combinations, and compares the operation results of the statistical models with the physiological results obtained through a standard measurement method, wherein the operation algorithms are respectively trained and possibly have different preset values and parameters; after the statistical model is established, the statistical model needs to be trained to be more representative, wherein, the relevant machine learning system can automatically eliminate the time domain/frequency domain characteristics with insufficient influence by a characteristic selection operation method, and the characteristic selection can select and utilize relevant data to calculate the required target value to compare with the experimental data so as to check whether the requirements of the significance level and the prediction error are met.
S143, if the statistical model does not meet the requirements, the tested time domain features and/or frequency domain features are removed from the statistical model;
calculating by circulating operation on all data until a statistical model which can generate requirements of meeting the reservation level and the prediction error for all the comprehensive data is generated; it should be noted that: different output target data have different algorithms, and an algorithm statistical model can be formed by different time domain/frequency domain characteristics and has different parameters.
And S144, constructing an algorithm statistical model by selecting the feature subset with the highest accuracy and the highest statistical parameter value.
The physiological data of the human or animal being collected in the experiment may be body movement, respiration rate, heart rate variability, blood pressure, mood, cardiac output, body movement, etc., as desired.
After the model is established, the following steps are carried out:
s2, collecting physiological information data of a target organism; wherein the physiological information data comprises electrophysiological information, mechanical physiological information and physical movement activity data of the target organism; the comprehensiveness of the information is ensured by collecting the electrophysiological information, the mechanical physiological information, the body movement activity data and the like of the target organism.
S3, performing noise reduction processing on the physiological information data through a signal processing method, and extracting time domain characteristics and/or frequency domain characteristics of different physiological information data through a characteristic extraction method; the characteristic extraction method comprises Fourier transform, frequency band power calculation, time-frequency analysis, wavelet decomposition or waveform detection; the signal to noise ratio of the physiological information data is improved, and distorted or abnormal data information caused by external interference or other uncontrollable factors is eliminated.
S4, inputting the time domain characteristics and/or the frequency domain characteristics extracted from the electrophysiological information, the mechanical physiological information and the body movement activity data into an algorithm statistical model for operation to obtain an output target; the method comprises the steps of establishing an algorithm statistical model corresponding to physiological information data through different physiological information data established by experiments, inputting the collected physiological information data of a target organism into the corresponding algorithm statistical model for comparison calculation analysis to obtain a corresponding output target, wherein the output target comprises heart rate analysis, blood pressure analysis, heart rate variation analysis and the like corresponding to the algorithm statistical model.
For example, the time domain features and/or frequency domain features extracted from electrophysiological information, mechanical physiological information, physical movement activity data, and the like are input into an algorithmic statistical model established for heart rate examination, and only if machine learning is selected, features related to heart rate are selected, and the output result of entering the statistical model is the output target of heart rate, and the output target is analyzed according to a plurality of physiological information data of a target organism.
S5, the output target is used as an analysis report and reported back to the report receiving unit 4, or the output target is compared with the past databases respectively to obtain an analysis report and reported back to the report receiving unit 4, wherein the past databases include: past physiological information data of the target living body and past physiological information group data of living bodies of the same or different race and breed with the target living body. Physiological data information of the target organism or an organism having the same or similar race, family, order, age and size as the target organism is generally stored in a past database, an analysis report of the target organism is obtained by comparing an output target with the data, and the data analysis unit 3 sends the analysis report to a report receiving unit 4 for a professional to give advice according to the report.
As shown in fig. 2, the present invention further provides a physiological detection and analysis system based on hybrid sensing, which comprises a data collector, a data recording unit, a data analysis unit capable of analyzing the physiological information data of the target organism collected by the data collector after being processed by the data recording unit, and a report receiving unit; the data collector comprises a rotating body and a sensor, the sensor is arranged on the rotating body, the rotating body comprises a first rotating part and a second rotating part, and the first rotating part and the second rotating part are rotationally fixed through a rotating shaft. Wherein the sensors include, but are not limited to:
an electrocardiogram sensor 11, an accelerometer 12, a motion sensor and a pressure sensor 13 for collecting electrophysiological information, mechanical physiological activity, respiration and body motion related activity; the utility model records the electrophysiological and mechanical activities of the cardiovascular system in a synchronous and time-locking manner, and synchronously measures the cardio-pulmonary activities and the body movements.
As shown in fig. 3-9, the system can collect real-time electrophysiological messages from animals and humans via the sensors, including, but not limited to: electrocardiogram (ECG) and respiratory measurements of electrical properties; real-time mechanical physiological information is collected from animals and humans, including, but not limited to: cardiac vibrograms (SCG; sesamocordiography), ballistocardiograms (BCG; ballistocardiography) and mechanical respiration measurements; real-time physical motion activity data is collected. And analyzing the collected time domain characteristics and frequency domain characteristics of different physiological information data extractors by combining the electrophysiological signals and the mechanical physiological signals again to obtain mutual data images between the electrophysiological signals and the time domain characteristics extracted by the mechanical physiological signals.
In particular, physiological measurements of cardiac conditions, hemodynamic status, respiratory and physical activity status include, but are not limited to: physical exercise, heart rate variability, electrocardiogram peak composition structure detection, heart vibration map peak composition structure detection, ballistocardiogram peak composition structure detection, blood pressure, emotion detection, and the like.
The data recording unit 2 comprises a central processor for measuring, recording or registering physiological information data collected by the sensors, the central processor: and is further configured to send the physiological information data to the data analysis unit 3.
The data analysis unit 3 comprises a past database, a real-time acquisition database and an analysis platform capable of establishing and training an algorithm statistical model by a machine learning method; the past database includes: past physiological information data of the target living being and past physiological information group data of a living being of the same or different race or breed as the target living being; the real-time acquisition database includes physiological information data of the target living being.
The data analysis platform can also be used for improving the signal to noise ratio of the physiological information data and extracting time domain and/or frequency domain characteristics of different physiological information data through a characteristic extraction method.
As shown in fig. 10-12, the measuring part of the measuring system can be designed to be foldable, which is convenient for storage and carrying.
Specifically, the mechanical physiological activity sensor 51 is embedded in a data collection structure, and the data collection structure is used as a carrier of the sensor, so that a user can directly act on the body of a target organism through the data collection structure to collect physiological data information of the target organism.
For example, physical movement activity data of the target living being is acquired by an accelerometer.
The utility model discloses when being applied to the monitoring and the analysis of heart condition, blood flow dynamic state, breathing and physical activity, carry out the analysis back with the data of collecting to give user and/or medical expert analysis feedback, doctor or other professionals diagnose under the guide of analysis report again, treat and prescription suggestion. The automatic rapid clinical interpretation and diagnosis of the electrocardiogram greatly improve the specificity and efficiency of diagnosis.
The utility model has the advantages that: the utility model discloses can carry out heart health aassessment to the target organism, for example:
judging the cardiovascular health and emotional state according to the heart rate data and the heart blood flow data;
scouting abnormal heart activity, for example: arrhythmia;
detecting the blood pressure;
or for measurement of lung activity:
detecting the respiration rate;
detecting abnormal respiratory activity;
or performing a physical activity measurement: such as physical fitness status; or, the respiration data is used for judging the whole physical ability level, the body movement data is used for judging the whole physical ability level, and the physiological data collection platform is used for synchronously recording the electrophysiological and mechanical data of the heart, the respiration and the body movement in real time; the data collected from the sensors may be recorded on the data recording unit 2, remotely or at other servers or devices.
The foregoing is a more detailed description of the present invention, taken in conjunction with the specific preferred embodiments thereof, and it is not intended that the invention be limited to the specific embodiments shown and described. To the utility model belongs to the technical field of ordinary technical personnel, do not deviate from the utility model discloses under the prerequisite of design, can also make a plurality of simple deductions or replacement, all should regard as belonging to the utility model discloses a protection scope.
Claims (1)
1. A physiological monitoring and analysis system based on hybrid sensing is characterized by comprising a data collector, a data recording unit and a report receiving unit;
the data collector comprises a rotating body and a sensor, the sensor is arranged on the rotating body, the sensor is an electrocardiogram sensor, an accelerometer, a motion sensor or a pressure sensor, the rotating body comprises a first rotating part and a second rotating part, and the first rotating part and the second rotating part are rotationally fixed through a rotating shaft.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201820097024.7U CN210354670U (en) | 2018-01-19 | 2018-01-19 | Physiological monitoring and analysis system based on hybrid sensing |
| TW108200641U TWM585420U (en) | 2018-01-19 | 2019-01-14 | Physiological monitoring and analyzing system based on hybrid sensing |
| PCT/IB2019/050372 WO2019142120A1 (en) | 2018-01-19 | 2019-01-17 | Hybrid sensing-based physiological monitoring and analyzing system |
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| CN201820097024.7U CN210354670U (en) | 2018-01-19 | 2018-01-19 | Physiological monitoring and analysis system based on hybrid sensing |
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| CN202288251U (en) * | 2011-09-29 | 2012-07-04 | 博奥生物有限公司 | Portable vital sign monitoring device |
| US20150025329A1 (en) * | 2013-07-18 | 2015-01-22 | Parkland Center For Clinical Innovation | Patient care surveillance system and method |
| WO2015089484A1 (en) * | 2013-12-12 | 2015-06-18 | Alivecor, Inc. | Methods and systems for arrhythmia tracking and scoring |
| CN104605841A (en) * | 2014-12-09 | 2015-05-13 | 电子科技大学 | Wearable electrocardiosignal monitoring device and method |
| CN105078445B (en) * | 2015-08-24 | 2018-11-02 | 华南理工大学 | Senior health and fitness's service system based on health service robot |
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| WO2019142120A1 (en) | 2019-07-25 |
| TWM585420U (en) | 2019-10-21 |
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