CN115137324A - Human heart health monitoring system based on cloud-edge-end architecture - Google Patents
Human heart health monitoring system based on cloud-edge-end architecture Download PDFInfo
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- CN115137324A CN115137324A CN202210552116.0A CN202210552116A CN115137324A CN 115137324 A CN115137324 A CN 115137324A CN 202210552116 A CN202210552116 A CN 202210552116A CN 115137324 A CN115137324 A CN 115137324A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1101—Detecting tremor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1102—Ballistocardiography
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
- A61B5/747—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network in case of emergency, i.e. alerting emergency services
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/02—Stethoscopes
- A61B7/04—Electric stethoscopes
Abstract
The invention discloses a human heart health monitoring system based on a cloud-edge-end architecture, and belongs to the technical field of crossing of an Internet of things technology and a medical information processing technology. The human heart health monitoring system based on the cloud-edge-end architecture provides a mature and stable cloud-edge cooperative framework, supports large-scale user access, realizes early screening of heart diseases and tracking follow-up of patients, and relieves medical resource shortage; the user side equipment subsystem can adopt a non-contact wearable design with low power consumption, the equipment size is small, and the use comfort is high; the medical information abundance and the heart health abnormity identification accuracy adopted by the edge terminal subsystem and the cloud terminal subsystem are high, and the heart health information of the user can be fed back continuously and immediately for 24 hours; the cloud-side information interaction system is used for updating the heart physiological signal data of the patient at the cloud, a data island between the patient and a hospital is opened, the training model can be updated and iterated, and the accuracy of heart health abnormity detection is guaranteed.
Description
Technical Field
The invention belongs to the technical field of crossing of an Internet of things technology and a medical information processing technology, and particularly relates to a human heart health monitoring system based on a cloud-edge-end architecture.
Background
Early diagnosis and follow-up of cardiovascular diseases are of great significance to treatment and control of the diseases, however, an effective early diagnosis and follow-up means is lacking at present, and the disease occupies a large amount of medical resources but the control effect is not ideal.
In order to solve the problems, the Internet of things technology is adopted, various wearable or portable physiological signal acquisition devices can be used for acquiring the heart activity information of the patient in real time for a long time, and the artificial intelligence technology is used for analyzing the heart health condition of the patient, so that the purpose of early diagnosis and tracking follow-up visit can be achieved by realizing uninterrupted heart health monitoring.
However, although some internet of things monitoring schemes for medical use exist at present, the schemes only adopt edge end or cloud data for data processing, and the system architecture is unstable and difficult to deploy in a large scale; in addition, the computing resources of the edge end and the cloud end are very limited, the updating speed is slow, and the requirements of training and updating iteration of a big data processing algorithm cannot be met.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a human heart health monitoring system based on a cloud-edge-end architecture, which is helpful for realizing early screening of heart diseases and tracking and follow-up visits of patients, alleviating practical problems of shortage of medical resources, uneven distribution, serious shortage of professional doctors, uneven levels, and the like, and promoting development of novel medical modes such as smart medical, distributed medical, and the like.
The invention is realized by the following technical scheme:
the invention discloses a human heart health monitoring system based on a cloud-edge-end architecture, which comprises a user end equipment subsystem, an edge end subsystem and a cloud end subsystem; the user side equipment subsystem comprises a plurality of heart physiological signal acquisition equipment deployed at a patient; the edge terminal system comprises a communication interface, a local file storage module, a data analysis module and a health abnormity early warning module; the cloud subsystem comprises a cloud computing server, a cloud database, a cloud file storage system, a cloud health anomaly early warning module and an MQTT communication server; the user side equipment subsystem is in communication interconnection with the edge side subsystem through a communication interface, the communication interface is connected with a data analysis module, the data analysis module is connected with a health abnormity early warning module, and the data analysis module and the health abnormity early warning module are both connected with a local file storage module; the client side equipment subsystem and the edge side subsystem are respectively in communication interconnection with the MQTT communication server through an MQTT communication protocol, the MQTT communication server is respectively connected with the cloud computing server and the cloud end database, the cloud computing server is connected with the cloud end health abnormity early warning module, and the cloud computing server, the cloud end database and the cloud end health abnormity early warning module are respectively connected with the cloud end file storage system.
Preferably, the cardiac physiological signal acquisition device includes an electrocardiogram acquisition device, an ballistocardiogram acquisition device, a phonocardiogram acquisition device, an oxygen saturation acquisition device, and a blood pressure acquisition device.
Preferably, the edge terminal system is integrated into the patient's monitoring equipment or deployed within the primary care facility and regional center hospital.
Preferably, the data analysis module comprises a signal filtering module and a calculation module, the signal filtering module is used for filtering noise signals and artifact interference signals in the raw data, and the calculation module is used for calculating the cardiac physiological signal data of the patient.
Further preferably, the calculation module continuously monitors the heart health condition of the patient for 24h, specifically: the calculation module extracts characteristic wavelets or characteristic vectors of physiological signals, multi-dimensional information spaces under different dimensions of a wavelet level and an integer wave time-frequency domain characteristic level are constructed, and the real-time heart health state of a patient is obtained through a fuzzy algorithm in combination with an intelligent classification algorithm of machine learning and historical health data of the patient.
Preferably, the cloud computing server is used for heart health abnormality detection and identification of big data heart physiological signal fusion; the cloud file storage system is used for storing a detection model, a plurality of physical physiological signal data sets and data mirror images; the cloud database is used for storing addressing catalogs, user personal information, user side equipment numbers, edge side equipment numbers and names of medical institutions; the MQTT communication server is used for providing an information interaction function of the cloud subsystem and the edge subsystem, receiving the update information in a remote wireless communication mode and transmitting the update information to the cloud computing server center for processing.
Further preferably, the cloud computing server realizes the heart health anomaly detection and identification based on a deep learning algorithm of a convolutional neural network.
Preferably, the local file storage module is used for storing historical physiological signal data, historical detection results and latest version heart health anomaly detection algorithms of the patient, and the local file storage module performs information interaction with the cloud subsystem.
Preferably, the health abnormity early warning module is used for sending a health abnormity warning to the user side equipment subsystem, communicating with the cloud subsystem and sending the health abnormity warning to the family members and hospitals of the patients through the cloud subsystem.
Preferably, the cloud health abnormity early warning module is used for sending health abnormity alarms to the user side equipment subsystem, the family members of the patient and the hospital.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention discloses a human heart health monitoring system based on a cloud-edge-end architecture, which provides a mature and stable cloud-edge cooperative frame, supports large-scale user access, can realize early screening of heart diseases and tracking and follow-up of patients, and is beneficial to relieving the practical problems of shortage of medical resources, uneven distribution, serious shortage of professional doctors, uneven levels and the like; the user side equipment subsystem of the system can adopt a non-contact wearable design with low power consumption, the equipment volume is small, the use comfort is high, the compliance and the use viscosity of patients are improved, and the system is suitable for being used by families of patients and medical institutions in remote areas; according to the system, an intelligent recognition algorithm of multi-physical heart signal fusion is adopted in the edge terminal subsystem and the cloud terminal subsystem, the medical information enrichment degree and the recognition accuracy of heart health abnormity are high, and the heart health information of a user can be fed back continuously and immediately for 24 hours; the system updates the cardiac physiological signal data of the patient at the cloud end by using the cloud-side information interaction system, a data island between the patient and a hospital is opened, the training model at the cloud end can be updated and iterated, and the accuracy of the abnormal cardiac health detection is ensured. The invention is helpful to relieve the shortage and uneven distribution of medical resources, the number of professional doctors is seriously deficient, the level is uneven and the like, the development of novel medical treatment modes such as intelligent medical treatment and distributed medical treatment is promoted.
Drawings
Fig. 1 is a schematic diagram of the system structure and principle of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the following drawings and specific examples, which are intended to be illustrative and not limiting:
as shown in fig. 1, the present embodiment provides a human heart health monitoring system based on a cloud-edge-end architecture, which includes a client device subsystem, an edge terminal subsystem, and a cloud end subsystem; the user terminal equipment subsystem comprises a plurality of heart physiological signal acquisition equipment deployed at a patient; the edge terminal system comprises a communication interface, a local file storage module, a data analysis module and a health abnormity early warning module; the cloud subsystem comprises a cloud computing server, a cloud database, a cloud file storage system, a cloud health anomaly early warning module and an MQTT communication server; the user side equipment subsystem is in communication interconnection with the edge side subsystem through a communication interface, the communication interface is connected with a data analysis module, the data analysis module is connected with a health abnormity early warning module, and the data analysis module and the health abnormity early warning module are both connected with a local file storage module; the client side equipment subsystem and the edge terminal subsystem are respectively communicated and interconnected with the MQTT communication server through an MQTT communication protocol, the MQTT communication server is respectively connected with the cloud computing server and the cloud side database, the cloud computing server is connected with the cloud side health abnormity early warning module, and the cloud computing server, the cloud side database and the cloud side health abnormity early warning module are respectively connected with the cloud side file storage system.
The client device subsystem is deployed at the patient, and includes six kinds of acquisition devices of main physiological signals of the human heart, namely, low-power-consumption contactless wearable Electrocardiogram (ECG), seismogram (scimocardiogram, SCG), ballistocardiogram (BCG), phonocardiogram (PCG), oxyhemoglobin Saturation (SaO 2), and Blood Pressure (Blood Pressure, BP).
The edge terminal system can be integrated in the monitoring equipment of the patient or deployed in the primary medical institutions of communities and villages and regional central hospitals, and comprises a communication interface, a local file storage module, a data analysis module and a health abnormity early warning module.
The data analysis module firstly filters noise signals and artifact interference signals in original data, improves the purity of the signals, and then calculates the basic data of the heart rate, the heart rate variability, the heart shrinkage rate, the ventricular pump blood rate and the like of a patient.
The local file storage module is used for storing historical physiological signal data, historical detection results and latest version heart health abnormity detection algorithm of the patient, and the local file storage module can be in information interaction with the cloud system.
The data analysis module can analyze human multi-physical heart physiological signals collected by the user side, and continuously monitor the heart health condition of a patient for 24h, and specifically comprises the following steps:
the data analysis module extracts characteristic wavelets or characteristic vectors of physiological signals, multi-dimensional information spaces under different dimensions of a wavelet layer and an integral wave time-frequency domain characteristic layer are constructed, interference of actions (such as standing, walking, running, jumping, riding, sleeping and the like) of a patient is further eliminated through comparison of the multi-dimensional information, and the characteristic information of different dimensions is identified and detected through a fuzzy algorithm in combination with an intelligent classification algorithm of machine learning and historical health data of the patient, so that the real-time heart health state of the patient is obtained.
If the detection result of the heart health state of the patient is abnormal, the data analysis module sends the detection result to the heart health abnormal early warning module, the heart health abnormal early warning module sends a health abnormal alarm to user end equipment, and the health abnormal alarm is sent to family members of the patient, hospitals and doctors through remote wireless communication and a cloud system.
The cloud subsystem comprises a cloud computing server, a cloud database, a cloud file storage system, a cloud health anomaly early warning module and an MQTT communication server.
The cloud computing server is used for heart health abnormity detection and identification of big data multi-physical heart signal fusion; the cloud file storage system is used for storing a detection model, a multi-physical physiological signal data set, a data mirror image and a heart health abnormity detection result, and the cloud database is used for storing an addressing directory, user personal information, a user side equipment number, an edge side equipment number and a name of a medical institution; the MQTT communication server provides an information interaction function of the cloud subsystem and the edge subsystem, receives update information in a remote wireless communication mode and transmits the update information to the cloud computing server center for processing; the method for detecting and identifying the fusion abnormality of the big data multi-physical heart physiological signals is a deep learning algorithm based on a Convolutional Neural Network (CNN); if the CNN convolutional neural network detects that the heart health condition of the patient is abnormal and identifies the corresponding disease type, the health abnormal early warning module immediately sends the health abnormal information and the diagnosis result to the patient, family members of the patient and a doctor in a mode of user end equipment, short message service, mobile equipment APP or e-mail.
The above description is only a part of the embodiments of the present invention, and although some terms are used in the present invention, the possibility of using other terms is not excluded. These terms are used merely for convenience in describing and explaining the nature of the invention and are to be construed as any additional limitation which is not in accordance with the spirit of the invention. The foregoing is merely an illustration of the present invention for the purpose of providing an easy understanding and is not intended to limit the present invention to the particular embodiments disclosed herein, and any technical extensions or innovations made herein are protected by the present invention.
Claims (10)
1. A human heart health monitoring system based on a cloud-edge-end architecture is characterized by comprising a user end equipment subsystem, an edge end subsystem and a cloud end subsystem; the user side equipment subsystem comprises a plurality of heart physiological signal acquisition equipment deployed at a patient; the edge terminal system comprises a communication interface, a local file storage module, a data analysis module and a health abnormity early warning module; the cloud subsystem comprises a cloud computing server, a cloud database, a cloud file storage system, a cloud health abnormity early warning module and an MQTT communication server; the user side equipment subsystem is in communication interconnection with the edge side subsystem through a communication interface, the communication interface is connected with a data analysis module, the data analysis module is connected with a health abnormity early warning module, and the data analysis module and the health abnormity early warning module are both connected with a local file storage module; the client side equipment subsystem and the edge terminal subsystem are respectively communicated and interconnected with the MQTT communication server through an MQTT communication protocol, the MQTT communication server is respectively connected with the cloud computing server and the cloud side database, the cloud computing server is connected with the cloud side health abnormity early warning module, and the cloud computing server, the cloud side database and the cloud side health abnormity early warning module are respectively connected with the cloud side file storage system.
2. The system for monitoring the health of the human heart based on the cloud-edge-end architecture as claimed in claim 1, wherein the cardiac physiological signal collecting device comprises an electrocardiogram collecting device, an seismogram collecting device, an ballistocardiogram collecting device, a phonocardiogram collecting device, an oxygen saturation collecting device and a blood pressure collecting device.
3. The cloud-edge-end architecture based human heart health monitoring system of claim 1, wherein the edge-end subsystem is integrated into a patient's monitoring device or deployed within a primary care facility and regional center hospital.
4. The human heart health monitoring system based on the cloud-side-end architecture as claimed in claim 1, wherein the data analysis module comprises a signal filtering module and a calculation module, the signal filtering module is used for filtering noise signals and artifact interference signals in the raw data, and the calculation module is used for calculating the cardiac physiological signal data of the patient.
5. The system for monitoring the health of the human heart based on the cloud-edge-end architecture as claimed in claim 4, wherein the computing module continuously monitors the health condition of the heart of the patient for 24h, specifically: the calculation module extracts characteristic wavelets or characteristic vectors of physiological signals, multi-dimensional information spaces under different dimensions of a wavelet level and an integer wave time-frequency domain characteristic level are constructed, and the real-time heart health state of a patient is obtained through a fuzzy algorithm in combination with an intelligent classification algorithm of machine learning and historical health data of the patient.
6. The human heart health monitoring system based on the cloud-edge-end architecture as claimed in claim 1, wherein the cloud computing server is used for heart health anomaly detection and identification of big data heart physiological signal fusion; the cloud file storage system is used for storing a detection model, a plurality of physical physiological signal data sets and data mirror images; the cloud database is used for storing addressing catalogs, user personal information, user side equipment numbers, edge side equipment numbers and names of medical institutions; the MQTT communication server is used for providing an information interaction function of the cloud subsystem and the edge subsystem, receiving update information in a remote wireless communication mode and transmitting the update information to the cloud computing server center for processing.
7. The human heart health monitoring system based on the cloud-edge-end architecture as claimed in claim 6, wherein the cloud computing server implements heart health anomaly detection and identification based on a deep learning algorithm of a convolutional neural network.
8. The human heart health monitoring system based on the cloud-side-end architecture as claimed in claim 1, wherein the local file storage module is configured to store historical physiological signal data, historical detection results, and latest version of heart health anomaly detection algorithm of the patient, and performs information interaction with the cloud subsystem.
9. The system according to claim 1, wherein the health anomaly early warning module is configured to send a health anomaly alarm to the client device subsystem, communicate with the cloud subsystem, and send a health anomaly alarm to the patient's family members and the hospital through the cloud subsystem.
10. The system according to claim 1, wherein the cloud-side-end architecture based human heart health monitoring system is configured to send out health anomaly alerts to the client device subsystem, the patient's family members, and the hospital.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115736938A (en) * | 2022-11-17 | 2023-03-07 | 东南大学 | Multi-mode physiological signal acquisition device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115736938A (en) * | 2022-11-17 | 2023-03-07 | 东南大学 | Multi-mode physiological signal acquisition device |
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