CN112037916A - Shared multifunctional sudden death prevention physiological information detection system and method thereof - Google Patents

Shared multifunctional sudden death prevention physiological information detection system and method thereof Download PDF

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CN112037916A
CN112037916A CN202010933532.6A CN202010933532A CN112037916A CN 112037916 A CN112037916 A CN 112037916A CN 202010933532 A CN202010933532 A CN 202010933532A CN 112037916 A CN112037916 A CN 112037916A
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陈世雄
黄为民
朱明星
黄保发
方贤权
黄�俊
贾进滢
覃诚
皮瑶
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Shenzhen Fengsheng Biotechnology Co ltd
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Abstract

The invention discloses a shared multifunctional sudden death prevention physiological information detection system and a method thereof, wherein the shared multifunctional sudden death prevention physiological information detection system comprises the following steps: the system comprises a human-computer interaction module, a cloud computing module and a 5G remote diagnosis module; the human-computer interaction module comprises a physiological information acquisition module, a main processor and a human-computer interaction module. The system utilizes an intelligent algorithm to process multiple physiological information in a fusing manner at the cloud, reduces the dependence on the judgment experience of doctors, utilizes the low time delay of the 5G technology to match with the shared physiological information inspection equipment to carry out remote diagnosis on the user, has higher specialty, is more convenient, more efficient and lower in cost, detects multiple physiological parameters such as electrocardio, heart rate and blood pressure for office workers in a short time, extracts the multiple physiological parameters of the user according to the judgment emphasis of the doctors, judges and classifies the multiple physiological parameters by combining a multiple physiological information data fusion analysis model, provides suggestions for the user according to the classification result, and achieves the purpose of preventing sudden death.

Description

Shared multifunctional sudden death prevention physiological information detection system and method thereof
Technical Field
The invention relates to the technical field of medical instruments, in particular to a shared multifunctional sudden death prevention physiological information detection system and a method thereof.
Background
In China, 1 person has sudden cardiac death per minute on average, and the number of the people who occur in the year is 54.4 thousands, which is the first in the world. Office workers, as important income-saving personnel of families, are under family stress and working stress, lack rest time and exercise time, and are generally poor in physical quality. The regular examination of multiple physiological information (such as electrocardiogram, electroencephalogram, blood pressure, etc.) is of great significance for the prevention of sudden death of office workers and the stability of family society.
With the rapid social rhythm, the frequency of the office worker sudden death events is higher and higher, and the source sudden death accounts for 75% of all the sudden death events. The main physiological information detection methods at present include hospital physical examination, heart rate bracelet and the like. The hospital physical examination has accurate and professional detection results, but annual examination once a year is not enough for office workers to prevent sudden cardiac death events, and the office workers do not have enough time and economic capacity to undertake frequent hospital physical examination. Portable physiological information check out test set such as rhythm of the heart bracelet has the convenience, but physiological information detects too singly, often can only detect one or several simple physiological parameters in rhythm of the heart, breathing, the blood oxygen, consequently can't provide accurate, authoritative testing result for the user, often can only accomplish the accident and report to the police, and can't reach the effect of preventing diseases such as sudden death in advance.
Therefore, how to effectively prevent sudden death through physiological information detection is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above problems, the present invention aims to solve the problems that the time for detecting physiological information in hospital physical examination is long, the cost is high, frequent detection is inconvenient, the detection of physiological information by a heart rate bracelet is too single, and an accurate and authoritative detection result cannot be provided, so as to realize effective prevention of sudden death through physiological information detection.
The embodiment of the invention provides a shared multifunctional sudden death prevention physiological information detection system, which comprises: the system comprises a human-computer interaction end, a cloud computing module and a 5G remote diagnosis module; the human-computer interaction terminal comprises a physiological information acquisition module, a main processor and a human-computer interaction module;
the physiological information acquisition module is connected with the main processor and is used for acquiring various physiological information data of a user and transmitting the various physiological information data of the user to the main processor;
the cloud computing module is connected with the main processor and is used for inputting the various physiological information data into a multi-physiological information data fusion analysis model, generating comprehensive analysis result data and transmitting the comprehensive analysis result data to the main processor;
the main processor is connected with the physiological information acquisition module, the human-computer interaction module, the cloud computing module and the 5G remote diagnosis module, and is used for transmitting the various physiological information data to the cloud computing module, judging whether the user is in an unhealthy state according to the comprehensive analysis result data, if the user is in the unhealthy state, sending the comprehensive analysis result data to the 5G remote diagnosis module, establishing video connection with the 5G remote diagnosis module, sending warning information and remote guidance audio and video data received from the 5G remote diagnosis module through the human-computer interaction module, and if the user is in the healthy state, calling the comprehensive analysis result data according to the user instruction and sending the comprehensive analysis result data to the human-computer interaction module;
the 5G remote diagnosis module is connected with the main processor and used for carrying out real-time data analysis according to the comprehensive analysis result data and sending the audio and video data to the main processor through the video connection for remote guidance;
the man-machine interaction module is connected with the main processor and used for transmitting a user instruction to the main processor and receiving and displaying the comprehensive analysis result data, the warning information and the audio and video data.
In one embodiment, the plurality of physiological information data includes:
cardiac functional examination data, pulmonary functional examination data, routine examination data, and brain functional examination data.
In one embodiment, the physiological information acquisition module includes: the system comprises a heart function data acquisition unit, a lung function data acquisition unit, a conventional data acquisition unit and a brain function data acquisition unit;
the cardiac function data acquisition unit is connected with the main processor and is used for acquiring electrocardiogram data and non-invasive cardiac drainage detection data, preprocessing the electrocardiogram data, combining the preprocessed electrocardiogram data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report and transmitting the cardiac function detection report to the main processor;
the lung function data acquisition unit is connected with the main processor and is used for acquiring respiratory data and blood oxygen concentration, combining the respiratory data with the blood oxygen concentration to generate a lung function detection report and transmitting the lung function detection report to the main processor;
the regular data acquisition unit is connected with the main processor and is used for acquiring blood pressure and body temperature, combining the blood pressure and the body temperature to generate a regular inspection report and transmitting the regular inspection report to the main processor;
the brain function data acquisition unit is connected with the main processor and used for acquiring electroencephalogram data, generating a brain function examination report according to the received electroencephalogram data and transmitting the brain function examination report to the main processor.
In one embodiment, the cloud computing module comprises: the device comprises a preprocessing unit, a data set generating unit, a constructing unit and a comprehensive analysis result data generating unit;
the preprocessing unit is connected with the data set generating unit and is used for filtering the electrocardiosignals in the various physiological information data, extracting characteristic values and generating processed electrocardiosignals;
the data set generating unit is connected with the preprocessing unit and the constructing unit and is used for fusing the processed electrocardiosignals with other physiological information data to generate a data set;
the construction unit is connected with the data set generation unit and the comprehensive analysis result data generation unit and is used for constructing a multi-physiological information data fusion analysis preliminary model according to the data set, and pruning the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model;
the comprehensive analysis result data generation unit is connected with the construction unit and used for inputting the various physiological information data into the multi-physiological information data fusion analysis model, outputting diagnosis results and generating comprehensive analysis result data.
In one embodiment, the human-computer interaction terminal further includes: an emergency help module;
the emergency help seeking module is connected with the man-machine interaction module and the main processor and used for shooting a user picture when an emergency occurs, transmitting the user picture and the user position to the main processor, starting broadcasting, seeking help for the user and carrying out remote emergency help according to the audio and video data sent by the main processor.
In one embodiment, the main processor is further configured to receive the user picture and the user position, send the user picture and the user position to a medical institution, establish a video connection with the 5G remote diagnosis module, and send the audio/video data to the emergency help module.
In view of the above, in a second aspect of the present application, a detection method of a shared multifunctional sudden death prevention physiological information detection system is further provided, including:
the physiological information acquisition module acquires various physiological information data of a user and transmits the various physiological information data of the user to the main processor, and the main processor transmits the various physiological information data to the cloud computing module;
the cloud computing module inputs the various physiological information data into a multi-physiological information data fusion analysis model to generate comprehensive analysis result data, and transmits the comprehensive analysis result data to the main processor;
the main processor judges whether the user is in an unhealthy state or not according to the comprehensive analysis result data, and if the user is in the unhealthy state, the comprehensive analysis result data is sent to a 5G remote diagnosis module, and video connection with the 5G remote diagnosis module is established;
the 5G remote diagnosis module carries out real-time data analysis according to the comprehensive analysis result data and sends the audio and video data to the main processor through the video connection for remote guidance;
the main processor receives the audio and video data and sends warning information and the audio and video data to a man-machine interaction module;
and if the user is in a healthy state, the main processor calls the comprehensive analysis result data according to a user instruction and sends the comprehensive analysis result data to the human-computer interaction module.
In one embodiment, the physiological information collecting module collects a plurality of physiological information data of a user and transmits the plurality of physiological information data of the user to the main processor, and the method includes:
the cardiac function data acquisition unit acquires electrocardiogram data and non-invasive cardiac drainage detection data, preprocesses the electrocardiogram data, combines the preprocessed electrocardiogram data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report, and transmits the cardiac function detection report to the main processor;
a lung function data acquisition unit acquires respiratory data and blood oxygen concentration, combines the respiratory data with the blood oxygen concentration to generate a lung function detection report, and transmits the lung function detection report to the main processor;
the routine data acquisition unit acquires blood pressure and body temperature, combines the blood pressure and the body temperature to generate a routine examination report, and transmits the routine examination report to the main processor;
the brain function data acquisition unit acquires electroencephalogram data, generates a brain function examination report according to the received electroencephalogram data, and transmits the brain function examination report to the main processor.
In one embodiment, the cloud computing module inputs the multiple physiological information data into a multiple physiological information data fusion analysis model, generates comprehensive analysis result data, and transmits the comprehensive analysis result data to the main processor, and the cloud computing module includes:
the preprocessing unit filters the electrocardiosignals in the various physiological information data, extracts characteristic values and generates processed electrocardiosignals;
the data set generating unit fuses the processed electrocardiosignals and other physiological information data to generate a data set;
the construction unit constructs a multi-physiological information data fusion analysis preliminary model according to the data set, and performs pruning processing on the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model;
the comprehensive analysis result data generation unit inputs the multiple physiological information data into the multiple physiological information data fusion analysis model, outputs a diagnosis result and generates comprehensive analysis result data.
In one embodiment, further comprising:
when an emergency occurs, the emergency help module shoots a user photo, transmits the user photo and the user position to the main processor, starts broadcasting and seeks help for the user;
the main processor receives the user picture and the user position, sends the user picture and the user position to a medical institution, establishes video connection with the 5G remote diagnosis module, and sends the audio and video data to the main processor through the video connection for remote guidance by the 5G remote diagnosis module;
the main processor sends the audio and video data to the emergency help module, and the emergency help module carries out remote emergency help according to the audio and video data sent by the main processor.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the shared multifunctional sudden death prevention physiological information detection system and the method thereof provided by the embodiment of the invention have the advantages that the intelligent algorithm is utilized to fuse and process a plurality of physiological information at the cloud end, the dependence on the judgment experience of doctors is reduced, the low time delay of the 5G technology is utilized to match with the shared physiological information inspection equipment to carry out remote diagnosis on the user, the system has higher specialty and is more convenient, efficient and low in cost, in addition, a plurality of physiological parameters such as electrocardio, heart rate, blood pressure and the like are detected for office workers in a short time, the plurality of physiological parameters of the user are extracted according to the judgment emphasis of the doctors, the judgment and classification are carried out on the plurality of physiological parameters by combining the multi-physiological information data fusion analysis model, and the suggestion is provided for the user according to the classification result, so that the purpose of preventing sudden death.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a shared multifunctional sudden death prevention physiological information detection system provided by an embodiment of the invention;
FIG. 2 is a block diagram of a physiological information collection module according to an embodiment of the present invention;
fig. 3 is a flowchart of a computing method of a cloud computing module according to an embodiment of the present invention;
FIG. 4 is a flowchart of a detection method of the shared multifunctional sudden death prevention physiological information detection system according to an embodiment of the present invention;
fig. 5 is a flowchart of step S401 provided in the embodiment of the present invention;
fig. 6 is a flowchart of step S402 according to an embodiment of the present invention;
fig. 7 is a flowchart illustrating an emergency help module according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, a shared multifunctional sudden death prevention physiological information detection system provided by an embodiment of the present invention includes: the system comprises a human-computer interaction end 1, a cloud computing module 2 and a 5G remote diagnosis module 3; the human-computer interaction terminal 1 comprises a physiological information acquisition module 4, a main processor 5 and a human-computer interaction module 6;
the physiological information acquisition module 4 is connected with the main processor 5 and is used for acquiring various physiological information data of a user and transmitting the various physiological information data of the user to the main processor 5.
Specifically, the plurality of types of physiological information data include: cardiac functional examination data, pulmonary functional examination data, routine examination data, and brain functional examination data.
Further, referring to fig. 2, the cardiac function examination data includes an electrocardiogram, a non-invasive cardiac row and a heart color ultrasound; the lung function examination data comprises respiratory data and blood oxygen information; the routine examination data comprises blood pressure and body temperature; the brain function test data includes an electroencephalogram.
Further, the electrocardiographic detection device, the respiration detection device, the electroencephalogram detection device and the noninvasive electrical drainage detection device are respectively connected with the physiological information acquisition module 4 through electrode sensors, and the physiological information acquisition module 4 is composed of an analog signal acquisition unit (for example, an ADS1299 chip), a power supply unit (for example, an LP5907 chip, or an LM2664 chip, or a TPS72325 chip), an analog signal amplification unit (for example, an INA141 chip, or a 188 chip), an audio DAC unit (for example, a PCM5102 chip), and a wireless transmission unit (for example, a GY-C320 chip).
The cloud computing module 2 is connected with the main processor 5, and is configured to input the multiple physiological information data into a multiple physiological information data fusion analysis model, generate comprehensive analysis result data, and transmit the comprehensive analysis result data to the main processor 5.
The main processor 5 is connected with the physiological information acquisition module 4, the human-computer interaction module 6, the cloud computing module 2 and the 5G remote diagnosis module 3 for connection, is used for transmitting the various physiological information data to the cloud computing module 2, judging whether the user is in an unhealthy state according to the comprehensive analysis result data, if the user is in the unhealthy state, the comprehensive analysis result data is sent to the 5G remote diagnosis module 3, a video connection with the 5G remote diagnosis module 3 is established, and sends out warning information and remote guidance audio and video data received from the 5G remote diagnosis module 3 through the man-machine interaction module 6, if the user is in a healthy state, and calling the comprehensive analysis result data according to the user instruction and sending the comprehensive analysis result data to the human-computer interaction module 6.
The 5G remote diagnosis module 3 is connected with the main processor 5 and is used for carrying out real-time data analysis according to the comprehensive analysis result data and sending the audio and video data to the main processor 5 through the video connection for remote guidance.
The human-computer interaction module 6 is connected with the main processor 5 and is used for transmitting a user instruction to the main processor 5 and receiving and displaying the comprehensive analysis result data, the warning information and the audio and video data.
Specifically, the human-computer interaction module 6 can apply for 5G expert remote diagnosis on a human-computer exchange interface to form a user instruction, and the comprehensive analysis result data, the warning information and the remote guidance video are displayed on the human-computer interaction module.
It should be noted that the human-computer interaction terminal 1 and the cloud computing modules 2 and 5G remote diagnosis module 3 perform data transmission through a communication device (for example, wireless or bluetooth).
In the embodiment, the intelligent algorithm is used for fusion processing of multiple physiological information at the cloud, dependence on doctor judgment experience is reduced, remote diagnosis is performed on a user by using the low-delay matching of the 5G technology and the shared physiological information inspection equipment, the method has higher specialty, is more convenient, more efficient and lower in cost, in addition, multiple physiological parameters such as electrocardio, heart rate and blood pressure are detected for office workers in a short time, the multiple physiological parameters of the user are extracted according to the judgment key points of the doctor, the multiple physiological parameters are judged and classified by combining the multiple physiological information data fusion analysis model, suggestions are provided for the user according to classification results, and the purpose of preventing sudden death is achieved.
In one embodiment, the physiological information acquisition module 4 includes: a cardiac function data acquisition unit 7, a pulmonary function data acquisition unit 8, a conventional data acquisition unit 9 and a brain function data acquisition unit 10;
the cardiac function data acquisition unit 7 is connected to the main processor 5, and is configured to acquire electrocardiographic data and non-invasive cardiac drainage detection data, pre-process the electrocardiographic data, combine the pre-processed electrocardiographic data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report, and transmit the cardiac function detection report to the main processor 5.
Specifically, the preprocessing of the electrocardiogram data includes noise reduction and baseline drift elimination.
Further, the electrocardiographic function detection report includes: the normal range of the electrocardiogram data and the detection data of the non-invasive cardiac row, whether the detection result is in the normal range and related suggestions.
The lung function data acquisition unit 8 is connected to the main processor 5, and is configured to acquire respiratory data and blood oxygen concentration, combine the respiratory data and the blood oxygen concentration to generate a lung function detection report, and transmit the lung function detection report to the main processor 5.
Specifically, the lung function test report includes: respiratory rate, normal range of blood oxygen concentration, whether the detection result is in the normal range and related suggestions.
The regular data acquisition unit 9 is connected to the main processor 5, and is configured to acquire blood pressure and body temperature, combine the blood pressure and the body temperature, generate a regular examination report, and transmit the regular examination report to the main processor 5.
Specifically, the general inspection report includes: normal ranges of blood pressure and body temperature, whether the detection result is in the normal range and related suggestions.
The brain function data acquisition unit 10 is connected to the main processor 5, and is configured to acquire electroencephalogram data, generate a brain function examination report according to the received electroencephalogram data, and transmit the brain function examination report to the main processor 5.
Specifically, the brain function test report includes: normal range of electroencephalogram data, whether the test result is within the normal range, and related advice.
It should be noted that the cardiac function data acquisition unit 7, the pulmonary function data acquisition unit 8, the conventional data acquisition unit 9, and the brain function data acquisition unit 10 are all disposed in the analog signal acquisition unit.
In one embodiment, referring to fig. 3, the cloud computing module 2 includes: the system comprises a preprocessing unit 11, a data set generating unit 12, a constructing unit 13 and a comprehensive analysis result data generating unit 14;
the preprocessing unit 11 is connected to the data set generating unit 12, and is configured to filter the electrocardiographic signals in the multiple kinds of physiological information data, extract a feature value, and generate processed electrocardiographic signals.
Specifically, an FIR filter is used for filtering power frequency interference in the electrocardiosignals to enable the electrocardiosignals to be smoother, a Butterworth filter is used for filtering myoelectric interference in the electrocardiosignals, a median filter is used for filtering baseline drift in the electrocardiosignals, a threshold method is used for detecting P-QRS-T waves in the electrocardiosignals, and 7 characteristic values such as RR intervals (ventricular cycles) and QRS wave intervals (ventricular muscle depolarization time) are extracted.
The data set generating unit 12 is connected to the preprocessing unit 11 and the constructing unit 13, and is configured to fuse the processed electrocardiographic signal with other physiological information data to generate a data set.
Specifically, the data set comprises 5 types of suspected coronary heart disease, suspected cardiomyopathy, suspected cardiac electrical conduction system abnormality, suspected cardiac concussion and health status.
For example, labels corresponding to suspected coronary heart disease, suspected cardiomyopathy, suspected cardiac electrical conduction system abnormality, suspected cardiac concussion and health status are 0, 1, 2, 3 and 4 respectively, the whole data set is composed of 60 parts of the above 5 types of physiological characteristic data, each part of the physiological characteristic data is composed of 7 characteristic values (RR interval, QRS wave interval, PR wave interval (unit: s), P wave, Q wave, R wave and T wave amplitude (unit: mV)) of electrocardiosignals, a cardiac index CI (unit: L/min) of non-invasive cardiac output, an index SI (unit: mL), systolic pressure, diastolic pressure (unit: mmHg), gender (0 for female, 1 for male), age, body temperature (unit: DEG C)) and a total of 14 physiological characteristics, and one label is added, the data style is shown in the following table 1:
table 1:
Figure BDA0002671111570000111
the constructing unit 13 is connected to the data set generating unit 12 and the comprehensive analysis result data generating unit 14, and is configured to construct a multi-physiological information data fusion analysis preliminary model according to the data set, and perform pruning processing on the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model.
Specifically, the steps of constructing the multi-physiological information data fusion analysis model are as follows:
A. constructing a first node: and calculating the information entropy of the data set D, wherein the calculation formula is as follows:
Figure BDA0002671111570000112
where | D | represents the total number of samples, | ck| represents the amount of data belonging to a certain class of the class 5 (k ═ 5) classification results;
and calculating the empirical condition entropy of each feature, wherein the calculation formula is as follows:
Figure BDA0002671111570000113
where H (D | A) represents the uncertainty of the random variable D given the random variable A, the conditional probability of D given A is represented by the maximum likelihood estimation, i.e. by
Figure BDA0002671111570000114
To represent P (D)i| A), i represents a specific case of the corresponding feature, | DiI represents the number of samples of the corresponding case, | cikIf is, then the corresponding situation belongs to a certain class in the classification resultData quantity of (e.g., when A represents gender, D)0Representing a condition characterised by sex as female, D1Representing a gender characterised by men, | D1| c represents the number of samples with gender as the male characteristic | c11And | represents the number of suspected coronary heart diseases in men).
B. And calculating the information gain of each feature according to the following calculation formula:
g(D,A)＝H(D)-H(D|A)
calculating the information entropy change before and after selecting a certain characteristic to obtain information gain, and selecting the characteristic value with the maximum information gain as a root node;
C. establishing a second node according to the result of the root node characteristics, calculating information gain on all characteristics of the second node, selecting the best characteristic as the second node, continuously and downwards establishing a corresponding node until no proper characteristics can be selected or all training subsets can be correctly classified, and establishing a multi-physiological information data fusion analysis preliminary model;
D. in order to prevent overfitting of a multi-physiological information data fusion analysis model and improve generalization performance of multi-physiological information data fusion analysis, pruning is required to be performed on a constructed sudden death prevention fusion algorithm, wherein a loss function is as follows:
Figure BDA0002671111570000121
wherein t represents a second node, Nt represents the number of samples of the second node, ht (t) represents the entropy of information corresponding to the second node, and α is an adjustable parameter.
And judging whether to prune the second node according to the value of the loss function, wherein the size of the loss function can be changed by adjusting the size of alpha and the threshold value of each physiological parameter so as to influence the pruning result, improve the generalization of the model and finally generate a complete multi-physiological information data fusion analysis model.
The comprehensive analysis result data generating unit 14 is connected to the constructing unit 13, and is configured to input the multiple physiological information data into the multiple physiological information data fusion analysis model, output a diagnosis result, and generate comprehensive analysis result data.
Specifically, the diagnosis results include: suspected coronary heart disease, suspected cardiomyopathy, suspected cardiac electrical conduction system abnormality, suspected cardiac concussion, and health status.
In the embodiment, the multi-physiological information data fusion analysis is an algorithm easy to understand and realize, can directly reflect the characteristics of physiological information data, simulates the intuitive decision rule of a human, considers the interaction between the characteristics, and can simultaneously process the data type and the conventional type attributes.
In one embodiment, the human-computer interaction terminal 1 further includes: an emergency call-for-help module 15;
the emergency help seeking module 15 is connected with the human-computer interaction module 6 and the main processor 5, and is configured to take a user picture when an emergency occurs, transmit the user picture and a user position to the main processor 5, start a broadcast, seek help for the user (for example, a passer seeking emergency experience helps, notifies passers to make a lifesaving channel, and the like), and send the audio and video data according to the main processor 5 to perform remote emergency help.
Specifically, whether the user has self-rescue ability is judged according to the user picture, and if the user has the self-rescue ability, the user carries out self-rescue through remote guidance of a medical specialist; if the user does not have the self-rescue ability, the broadcasting seeks the help of passers-by, and when the head picture of the passers-by appears on the camera, the real-time guidance is given to the passers-by, so that the user can be rescued in time.
In one embodiment, the main processor 5 is further configured to receive the user picture and the user location, send the user picture and the user location to a medical institution, establish a video connection with the 5G remote diagnosis module, and send the audio/video data to the emergency call-for-help module 15.
Specifically, the main processor 5 preliminarily determines whether the user is in an emergency rescue situation according to the user picture, so as to prevent the user from occupying medical resources by malicious use.
Referring to fig. 4, the detection method of the shared multifunctional sudden death prevention physiological information detection system comprises the following steps:
s401, a physiological information acquisition module acquires various physiological information data of a user and transmits the various physiological information data of the user to a main processor, and the main processor transmits the various physiological information data to a cloud computing module;
specifically, the plurality of types of physiological information data include: cardiac functional examination data, pulmonary functional examination data, routine examination data, and brain functional examination data.
Further, referring to fig. 2, the cardiac function examination data includes an electrocardiogram, a non-invasive cardiac row and a heart color ultrasound; the lung function examination data comprises respiratory data and blood oxygen information; the routine examination data comprises blood pressure and body temperature; the brain function test data includes an electroencephalogram.
Furthermore, the electrocardio detection device, the respiration detection device, the electroencephalogram detection device and the noninvasive electric drainage detection device are respectively connected with the physiological information acquisition module through electrode sensors, and the physiological information acquisition module consists of an analog signal acquisition unit, a power supply unit, an analog signal amplification unit, an audio DAC unit and a wireless transmission unit.
S402, the cloud computing module inputs the multiple physiological information data into a multiple physiological information data fusion analysis model to generate comprehensive analysis result data, and the comprehensive analysis result data is transmitted to the main processor;
s403, the main processor judges whether the user is in an unhealthy state or not according to the comprehensive analysis result data, and if the user is in the unhealthy state, the comprehensive analysis result data is sent to a 5G remote diagnosis module, and video connection with the 5G remote diagnosis module is established;
s404, the 5G remote diagnosis module performs real-time data analysis according to the comprehensive analysis result data and sends the audio and video data to the main processor through the video connection for remote guidance;
s405, the main processor receives the audio and video data and sends warning information and the audio and video data to a human-computer interaction module;
s406, if the user is in a healthy state, the main processor calls the comprehensive analysis result data according to a user instruction and sends the comprehensive analysis result data to the human-computer interaction module.
Specifically, the human-computer interaction module can apply for 5G expert remote diagnosis on a human-computer interaction interface to form a user instruction, and the comprehensive analysis result data, the warning information and the remote guidance video are displayed on the human-computer interaction module.
In one embodiment, as shown in fig. 5, in step S401, the acquiring a plurality of physiological information data of a user by the physiological information acquiring module, and transmitting the plurality of physiological information data of the user to the main processor, includes:
s4011, a cardiac function data acquisition unit acquires electrocardiogram data and non-invasive cardiac drainage detection data, preprocesses the electrocardiogram data, combines the preprocessed electrocardiogram data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report, and transmits the cardiac function detection report to the main processor.
Specifically, the preprocessing of the electrocardiogram data includes noise reduction and baseline drift elimination.
Further, the electrocardiographic function detection report includes: the normal range of the electrocardiogram data and the detection data of the non-invasive cardiac row, whether the detection result is in the normal range and related suggestions.
S4012, the lung function data acquisition unit acquires respiratory data and blood oxygen concentration, combines the respiratory data and the blood oxygen concentration to generate a lung function detection report, and transmits the lung function detection report to the main processor.
Specifically, the lung function test report includes: respiratory rate, normal range of blood oxygen concentration, whether the detection result is in the normal range and related suggestions.
S4013, the conventional data acquisition unit acquires blood pressure and body temperature, combines the blood pressure and the body temperature to generate a conventional examination report, and transmits the conventional examination report to the main processor.
Specifically, the general inspection report includes: normal ranges of blood pressure and body temperature, whether the detection result is in the normal range and related suggestions.
S4014, the brain function data acquisition unit acquires electroencephalogram data, generates a brain function examination report according to the received electroencephalogram data, and transmits the brain function examination report to the main processor.
Specifically, the brain function test report includes: normal range of electroencephalogram data, whether the test result is within the normal range, and related advice.
In one embodiment, as shown in fig. 6, the step S402 of inputting the multiple physiological information data into a multiple physiological information data fusion analysis model by the cloud computing module, generating comprehensive analysis result data, and transmitting the comprehensive analysis result data to the main processor includes:
s4021, filtering the electrocardiosignals in the various physiological information data by the preprocessing unit, extracting characteristic values and generating processed electrocardiosignals.
Specifically, an FIR filter is used for filtering power frequency interference in the electrocardiosignals to enable the electrocardiosignals to be smoother, a Butterworth filter is used for filtering myoelectric interference in the electrocardiosignals, a median filter is used for filtering baseline drift in the electrocardiosignals, a threshold method is used for detecting P-QRS-T waves in the electrocardiosignals, and 7 characteristic values such as RR intervals (ventricular cycles) and QRS wave intervals (ventricular muscle depolarization time) are extracted.
S4022, a data set generating unit fuses the processed electrocardiosignals and other physiological information data to generate a data set.
Specifically, the data set comprises 5 types of suspected coronary heart disease, suspected cardiomyopathy, suspected cardiac electrical conduction system abnormality, suspected cardiac concussion and health status.
S4023, a construction unit constructs a multi-physiological information data fusion analysis preliminary model according to the data set, and performs pruning processing on the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model.
S4024, the comprehensive analysis result data generation unit inputs the multiple physiological information data into the multiple physiological information data fusion analysis model, outputs a diagnosis result and generates comprehensive analysis result data.
In one embodiment, as shown in fig. 7, further includes:
s501, when an emergency occurs, the emergency help module shoots a user photo, transmits the user photo and the user position to the main processor, starts broadcasting and seeks help for the user.
S502, the main processor receives the user picture and the user position, sends the user picture and the user position to a medical institution, establishes video connection with the 5G remote diagnosis module, and sends the audio and video data to the main processor through the video connection for remote guidance.
Specifically, the main processor preliminarily judges whether the user is in an emergency rescue situation according to the user picture so as to prevent the malicious use from occupying medical resources.
S503, the main processor sends the audio and video data to the emergency help module, and the emergency help module carries out remote emergency help according to the audio and video data sent by the main processor.
Specifically, whether the user has self-rescue ability is judged according to the user picture, and if the user has the self-rescue ability, the user carries out self-rescue through remote guidance of a medical specialist; if the user does not have the self-rescue ability, the broadcasting seeks the help of passers-by, and when the head picture of the passers-by appears on the camera, the real-time guidance is given to the passers-by, so that the user can be rescued in time.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. Shared multifunctional sudden death prevention physiological information detection system is characterized by comprising: the system comprises a human-computer interaction end, a cloud computing module and a 5G remote diagnosis module; the human-computer interaction terminal comprises a physiological information acquisition module, a main processor and a human-computer interaction module;
the physiological information acquisition module is connected with the main processor and is used for acquiring various physiological information data of a user and transmitting the various physiological information data of the user to the main processor;
the cloud computing module is connected with the main processor and is used for inputting the various physiological information data into a multi-physiological information data fusion analysis model, generating comprehensive analysis result data and transmitting the comprehensive analysis result data to the main processor;
the main processor is connected with the physiological information acquisition module, the human-computer interaction module, the cloud computing module and the 5G remote diagnosis module, and is used for transmitting the various physiological information data to the cloud computing module, judging whether the user is in an unhealthy state according to the comprehensive analysis result data, if the user is in the unhealthy state, sending the comprehensive analysis result data to the 5G remote diagnosis module, establishing video connection with the 5G remote diagnosis module, sending warning information and remote guidance audio and video data received from the 5G remote diagnosis module through the human-computer interaction module, and if the user is in the healthy state, calling the comprehensive analysis result data according to the user instruction and sending the comprehensive analysis result data to the human-computer interaction module;
the 5G remote diagnosis module is connected with the main processor and used for carrying out real-time data analysis according to the comprehensive analysis result data and sending the audio and video data to the main processor through the video connection for remote guidance;
the man-machine interaction module is connected with the main processor and used for transmitting a user instruction to the main processor and receiving and displaying the comprehensive analysis result data, the warning information and the audio and video data.
2. The system as claimed in claim 1, wherein the plurality of physiological information data comprises:
cardiac functional examination data, pulmonary functional examination data, routine examination data, and brain functional examination data.
3. The shared multifunctional sudden death prevention physiological information detection system of claim 1, wherein the physiological information collection module comprises: the system comprises a heart function data acquisition unit, a lung function data acquisition unit, a conventional data acquisition unit and a brain function data acquisition unit;
the cardiac function data acquisition unit is connected with the main processor and is used for acquiring electrocardiogram data and non-invasive cardiac drainage detection data, preprocessing the electrocardiogram data, combining the preprocessed electrocardiogram data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report and transmitting the cardiac function detection report to the main processor;
the lung function data acquisition unit is connected with the main processor and is used for acquiring respiratory data and blood oxygen concentration, combining the respiratory data with the blood oxygen concentration to generate a lung function detection report and transmitting the lung function detection report to the main processor;
the regular data acquisition unit is connected with the main processor and is used for acquiring blood pressure and body temperature, combining the blood pressure and the body temperature to generate a regular inspection report and transmitting the regular inspection report to the main processor;
the brain function data acquisition unit is connected with the main processor and used for acquiring electroencephalogram data, generating a brain function examination report according to the received electroencephalogram data and transmitting the brain function examination report to the main processor.
4. The shared multifunctional sudden death prevention physiological information detection system of claim 1, wherein said cloud computing module comprises: the device comprises a preprocessing unit, a data set generating unit, a constructing unit and a comprehensive analysis result data generating unit;
the preprocessing unit is connected with the data set generating unit and is used for filtering the electrocardiosignals in the various physiological information data, extracting characteristic values and generating processed electrocardiosignals;
the data set generating unit is connected with the preprocessing unit and the constructing unit and is used for fusing the processed electrocardiosignals with other physiological information data to generate a data set;
the construction unit is connected with the data set generation unit and the comprehensive analysis result data generation unit and is used for constructing a multi-physiological information data fusion analysis preliminary model according to the data set, and pruning the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model;
the comprehensive analysis result data generation unit is connected with the construction unit and used for inputting the various physiological information data into the multi-physiological information data fusion analysis model, outputting diagnosis results and generating comprehensive analysis result data.
5. The system as claimed in claim 1, wherein the human-computer interaction terminal further comprises: an emergency help module;
the emergency help seeking module is connected with the man-machine interaction module and the main processor and used for shooting a user picture when an emergency occurs, transmitting the user picture and the user position to the main processor, starting broadcasting, seeking help for the user and carrying out remote emergency help according to the audio and video data sent by the main processor.
6. The system of claim 5, wherein the main processor is further configured to receive the user picture and the user location, send the user picture and the user location to a medical institution, establish a video connection with the 5G remote diagnosis module, and send the audio/video data to the emergency call module.
7. The detection method of the shared multifunctional sudden death prevention physiological information detection system is characterized by comprising the following steps:
the physiological information acquisition module acquires various physiological information data of a user and transmits the various physiological information data of the user to the main processor, and the main processor transmits the various physiological information data to the cloud computing module;
the cloud computing module inputs the various physiological information data into a multi-physiological information data fusion analysis model to generate comprehensive analysis result data, and transmits the comprehensive analysis result data to the main processor;
the main processor judges whether the user is in an unhealthy state or not according to the comprehensive analysis result data, and if the user is in the unhealthy state, the comprehensive analysis result data is sent to a 5G remote diagnosis module, and video connection with the 5G remote diagnosis module is established;
the 5G remote diagnosis module carries out real-time data analysis according to the comprehensive analysis result data and sends the audio and video data to the main processor through the video connection for remote guidance;
the main processor receives the audio and video data and sends warning information and the audio and video data to a man-machine interaction module;
and if the user is in a healthy state, the main processor calls the comprehensive analysis result data according to a user instruction and sends the comprehensive analysis result data to the human-computer interaction module.
8. The method as claimed in claim 7, wherein the step of collecting the physiological information data of the user and transmitting the physiological information data of the user to the host processor comprises:
the cardiac function data acquisition unit acquires electrocardiogram data and non-invasive cardiac drainage detection data, preprocesses the electrocardiogram data, combines the preprocessed electrocardiogram data with the non-invasive cardiac drainage detection data to generate a cardiac function detection report, and transmits the cardiac function detection report to the main processor;
a lung function data acquisition unit acquires respiratory data and blood oxygen concentration, combines the respiratory data with the blood oxygen concentration to generate a lung function detection report, and transmits the lung function detection report to the main processor;
the routine data acquisition unit acquires blood pressure and body temperature, combines the blood pressure and the body temperature to generate a routine examination report, and transmits the routine examination report to the main processor;
the brain function data acquisition unit acquires electroencephalogram data, generates a brain function examination report according to the received electroencephalogram data, and transmits the brain function examination report to the main processor.
9. The method as claimed in claim 7, wherein the step of inputting the physiological information data into a multi-physiological information data fusion analysis model by the cloud computing module to generate comprehensive analysis result data, and transmitting the comprehensive analysis result data to the main processor comprises:
the preprocessing unit filters the electrocardiosignals in the various physiological information data, extracts characteristic values and generates processed electrocardiosignals;
the data set generating unit fuses the processed electrocardiosignals and other physiological information data to generate a data set;
the construction unit constructs a multi-physiological information data fusion analysis preliminary model according to the data set, and performs pruning processing on the multi-physiological information data fusion analysis preliminary model to generate a multi-physiological information data fusion analysis model;
the comprehensive analysis result data generation unit inputs the multiple physiological information data into the multiple physiological information data fusion analysis model, outputs a diagnosis result and generates comprehensive analysis result data.
10. The method for detecting a shared multifunctional sudden death prevention physiological information detection system as claimed in claim 7, further comprising:
when an emergency occurs, the emergency help module shoots a user photo, transmits the user photo and the user position to the main processor, starts broadcasting and seeks help for the user;
the main processor receives the user picture and the user position, sends the user picture and the user position to a medical institution, establishes video connection with the 5G remote diagnosis module, and sends the audio and video data to the main processor through the video connection for remote guidance by the 5G remote diagnosis module;
the main processor sends the audio and video data to the emergency help module, and the emergency help module carries out remote emergency help according to the audio and video data sent by the main processor.
CN202010933532.6A 2020-09-08 2020-09-08 Shared multifunctional sudden death prevention physiological information detection system and method thereof Pending CN112037916A (en)

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