Electrocardiosignal monitoring system based on Internet of Things

Abstract

The invention discloses an electrocardiosignal monitoring system based on the Internet of Things. The electrocardiosignal monitoring system comprises an electrocardiosignal acquisition module and an electrocardiosignal analysis cloud server, a central electric signal collection module collects and preprocesses original electrocardiosignals, and then the electrocardiosignals are sent to a cloud platform through an Internet of Things communication module; the electrocardiosignal analysis cloud server accesses the cloud platform, obtains an electrocardiosignal updating state, obtains updated data, transfers the updated data to a local storage area, carries out deep analysis on the updated electrocardiosignal data, and pushes a deep analysis result to the user end application and / or the doctor end application; the electrocardiosignal acquisition module performs preliminary analysis on the preprocessed electrocardiosignal, and when the transmission rate of the Internet of Things communication module is lower than a preset threshold value, a preliminary analysis result is sent to the cloud platform, and sending of the preprocessed electrocardiosignal is stopped. According to the electrocardiosignal monitoring system, a user can still carry out basic monitoring and analysis under the condition that the network environment is poor, and the stability and reliability of the system are ensured.

Description

Technical Field

[0001] The present invention relates to an electrocardiogram (ECG) signal monitoring system based on the Internet of Things, belonging to the technical field of remote monitoring. Background Art

[0002] ECG monitoring is a crucial part of medical diagnosis, especially for the early detection and continuous monitoring of cardiovascular diseases. With the development of communication technologies, there are currently various remote ECG signal monitoring solutions. The Chinese invention patent application with the application number 201911263238.2 discloses a wearable ECG monitoring and arrhythmia remote real-time diagnosis device. This device uses a wearable front-end acquisition module to collect ECG data, sends it to the MCU via Bluetooth, the MCU filters the ECG data, and sends the filtered ECG signal to a remote cloud server via a communication module. The cloud server stores and performs neural network diagnosis on the ECG signal. The realization of remote diagnosis by this device depends on a stable remote communication network. When the network is poor, the MCU may experience delays, packet loss, etc. when sending the ECG signal to the remote cloud server, which affects the diagnosis of the cloud server and may even result in diagnostic errors. Summary of the Invention

[0003] Object of the Invention: The technical problem to be solved by the present invention is to provide an ECG signal monitoring system based on the Internet of Things in view of the deficiencies of the prior art, enabling users to still perform basic monitoring and analysis in a poor network environment, ensuring the stability and reliability of the system.

[0004] To solve the above technical problem, the present invention discloses an ECG signal monitoring system based on the Internet of Things, which includes an ECG signal acquisition module and an ECG signal analysis cloud server; the ECG signal acquisition module collects the original ECG signal and performs preprocessing, and then sends it to the cloud platform via an Internet of Things communication module; the ECG signal analysis cloud server accesses the cloud platform to obtain the update status of the ECG signal. If there is data update, the ECG signal analysis cloud server obtains the updated data, transfers it to the local storage area, and performs in-depth analysis on the updated ECG data, and pushes the in-depth analysis results to the user-side application and / or the doctor-side application;

[0005] The ECG signal acquisition module performs preliminary analysis on the preprocessed ECG signal. When the transmission rate of the Internet of Things communication module is lower than a preset threshold, the preliminary analysis results are sent to the cloud platform, and the transmission of the preprocessed ECG signal is terminated.

[0006] Further, the preliminary analysis includes heart rate calculation and ECG baseline correction.

[0007] Further, the electrocardiogram (ECG) signal acquisition module includes a lead interface, an amplification and filtering circuit, an MCU, and an Internet of Things (IoT) communication module; the original ECG signal is input from the lead interface, and the amplification and filtering circuit suppresses the common-mode signal and amplifies the differential-mode signal of the original ECG signal, filters out the power frequency interference signal, and provides a bias voltage to raise the ECG signal above 0V; the MCU samples the front-end amplified ECG signal and performs digital filtering, and then sends it to the cloud platform through the IoT communication module.

[0008] Further, the ECG signal monitoring system further includes a display screen; the preliminary analysis result is displayed through the display screen.

[0009] Further, the IoT communication module is a Guanghetong L610 wireless communication module, and the MCU transmits data to the IoT communication module through a serial port.

[0010] Further, when the IoT communication module sends data to the cloud platform and the ECG signal analysis cloud server obtains the ECG signal from the cloud platform, encrypted communication is used.

[0011] Further, the ECG signal analysis cloud server uses Nginx as a reverse proxy server.

[0012] Further, the ECG signal analysis cloud server constructs an application framework based on Flask.

[0013] Further, the ECG signal analysis cloud server performs in-depth analysis on the ECG data, including shallow analysis and deep analysis. The shallow analysis includes generating an ECG waveform diagram; the deep analysis includes generating an ECG waveform diagram and processing the ECG data in combination with an intelligent diagnosis algorithm to obtain a diagnosis suggestion.

[0014] Further, the user-side application includes a WeChat mini-program and an online diagnosis website, and the doctor-side application includes a PC-side application and an online diagnosis website.

[0015] Beneficial effects: Compared with the prior art, the ECG signal monitoring system disclosed in the present invention uses the ECG signal acquisition module with lower computing performance as an edge device and deploys a simple algorithm for preliminary analysis; uses the ECG signal analysis cloud server with higher computing performance as a cloud device and deploys a complex algorithm for in-depth analysis; this hierarchical algorithm deployment gives full play to the computing capabilities of the edge device and the cloud device, reduces the amount of communication transmission data in a weak network situation, enables users to still perform basic monitoring and analysis, and ensures the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The following further specifically describes the present invention in conjunction with the drawings and specific embodiments, and the above and / or other advantages of the present invention will become clearer.

[0017] Figure 1 Schematic diagram of the composition of the electrocardiogram signal monitoring system based on the Internet of Things disclosed by the present invention;

[0018] Figure 2 Schematic diagram of the composition of the electrocardiogram signal acquisition module;

[0019] Figure 3 Schematic diagram of the connection of the electrocardiogram signal analysis cloud server. Specific implementation manner

[0020] The present invention discloses an electrocardiogram signal monitoring system based on the Internet of Things. As Figure 1 shown, it includes an electrocardiogram signal acquisition module 10 and an electrocardiogram signal analysis cloud server 20. The electrocardiogram signal acquisition module 10 acquires the original electrocardiogram signal and performs preprocessing, and then sends it to the cloud platform through the Internet of Things communication module. The electrocardiogram signal analysis cloud server accesses the cloud platform to obtain the update status of the electrocardiogram signal. If there is data update, the electrocardiogram signal analysis cloud server acquires the updated data and transfers it to the local storage area, and deeply analyzes the updated electrocardiogram data, and pushes the deep analysis result to the user-side application and / or the doctor-side application.

[0021] As Figure 2 shown, the electrocardiogram signal acquisition module 10 includes a lead interface 11, an amplification and filtering circuit 12, an MCU 13, and an Internet of Things communication module 14; the original electrocardiogram signal is input from the lead interface 11, and the amplification and filtering circuit 12 is used to suppress the common-mode signal and amplify the differential-mode signal of the original electrocardiogram signal, filter out the power-frequency interference signal, and provide a bias voltage to lift the electrocardiogram signal above 0V; in this embodiment, the amplification and filtering circuit 12 is composed of a differential amplifier and a band-stop filter connected in series. The differential amplifier can effectively suppress the common-mode interference, enhance the effective signal, and amplify the voltage signal, amplifying the original electrocardiogram signal from the microvolt level to a voltage range convenient for subsequent processing; the band-stop filter filters out the main power-frequency interference signal, improves the signal-to-noise ratio of the signal; and provides a bias voltage to lift the signal above 0V to ensure that the electrocardiogram signal is within the range of 0 to 3.3V, which is convenient for the signal acquisition of the subsequent MCU module.

[0022] The MCU samples the amplified electrocardiogram (ECG) signals at the front end and performs digital filtering, and then sends them to the cloud platform through the Internet of Things (IoT) communication module. In this embodiment, the MCU uses the STM32F429 single-chip microcomputer. The MCU uses the ADC combined with DMA to collect the ECG signals output by the amplification and filtering circuit 12. In order to further improve the quality of the ECG signals, the digital filtering function is implemented on the STM32F429 single-chip microcomputer to further filter out the 50Hz power frequency interference and significantly improve the signal-to-noise ratio. The IoT communication module uses the Fibocom L610 wireless communication module, and the MCU transmits data to the IoT communication module through the serial port. Using the Fibocom L610 module to achieve data transmission enables the system to perform real-time communication using the 4G network, significantly expanding the usage scenarios of the system. In remote areas, outdoors, during travel or in case of emergencies such as natural disasters, there is no need to worry about Wi-Fi coverage. The ECG signals can be continuously monitored through the mobile network, and contact with medical service providers can be quickly made when needed to ensure health and safety.

[0023] The ECG signals sampled and filtered by the MCU are uploaded to the cloud platform through the L610 module. In this embodiment, the Tencent Cloud platform is used. First, device registration is performed for the ECG signal acquisition module 10, and a unique ID and key are added to ensure the uniqueness and security of the device identity, and appropriate device attributes are configured. These attributes will be used as temporary storage fields after the data is uploaded, facilitating subsequent data management and processing.

[0024] After the registration of the ECG signal acquisition module 10 is completed, the ECG signal analysis cloud server accesses the cloud platform to obtain the ECG signal update status. The ECG signal analysis cloud server can access the cloud platform at a preset frequency or on demand through control instructions. If there is data update, the ECG signal analysis cloud server obtains the updated data and transfers it to the local storage area, and performs in-depth analysis on the updated ECG data, and pushes the in-depth analysis results to the user-side application and / or the doctor-side application. To protect user privacy and data security, both the transmission of data from the ECG signal acquisition module 10 to the cloud platform and the acquisition of ECG signals by the ECG signal analysis cloud server 20 from the cloud platform use encrypted communication.

[0025] The present invention constructs an ECG signal analysis cloud server program based on the Python language. Using the standard API access library provided by Tencent, the ECG data is downloaded from Tencent Cloud and saved in the.json format. This format selection facilitates the structured management and subsequent processing of the data. The ECG signal analysis cloud server uses Nginx as a reverse proxy server to improve the stability and performance of the system. Nginx can handle a large number of concurrent requests and ensure good response speed under high load conditions. At the same time, the application framework is built based on Flask, providing a flexible interface and simple routing management for data reception and processing, making the development and maintenance work more efficient.

[0026] After receiving the electrocardiogram (ECG) data, the ECG signal analysis cloud server program will perform in-depth analysis and processing on the ECG data. Other modules and algorithms can call these data to achieve the drawing and analysis of ECG waveforms. Through the built-in graphical tool, the ECG signal analysis cloud server can generate intuitive ECG waveform diagrams to help users and doctors quickly understand the ECG status. In addition, intelligent diagnostic algorithms for common heart diseases are integrated, which can analyze the received ECG data and automatically identify common cardiovascular diseases such as arrhythmia and atrial fibrillation. Once the diagnosis is completed, the ECG signal analysis cloud server will generate corresponding diagnostic opinions and visualization results, and then push this information to users or doctors through the application. This real-time feedback mechanism not only improves the user experience but also enhances the doctor's decision-making support ability, ensuring timely adoption of appropriate medical measures.

[0027] To optimize the data processing flow, the ECG signal analysis cloud server also supports concurrent access and multi-threaded processing, enabling efficient data reception and processing even in high-concurrency situations. In addition, the ECG signal analysis cloud regularly performs data backup and archiving to ensure data integrity and security. This efficient data reception, processing, and forwarding mechanism provides solid technical support for the popularization and implementation of intelligent medical applications, helping to improve the quality and efficiency of medical services. The ECG signal analysis cloud server uses a processor with good performance, and complex intelligent diagnostic algorithms are deployed on it, which can be executed quickly, enabling doctors to obtain accurate diagnostic opinions promptly and thus be more calm when facing patients. The intelligent diagnostic algorithms can be continuously updated and optimized to continuously improve the system's diagnostic ability, and users and doctors can also continuously improve and adjust the health monitoring plan through feedback, forming a positive interaction.

[0028] If it is necessary to frequently or continuously monitor the user's electrocardiogram (ECG) status, the ECG signal acquisition module 10 will continuously send a large amount of data to the cloud platform. If the network condition is poor, with delays, error codes, or packet loss occurring, the ECG data obtained by the ECG signal analysis cloud server 20 will be incorrect, and analyzing the incorrect data may lead to incorrect results. To avoid this situation, in this embodiment, the ECG signal acquisition module performs a preliminary analysis on the preprocessed ECG signals, that is, a preliminary analysis on the ECG signals after MCU sampling and filtering. When the transmission rate of the Internet of Things communication module L610 is lower than the preset threshold, the preliminary analysis results are sent to the cloud platform; and the transmission of the preprocessed ECG signals is terminated and resumed when the transmission rate meets the standard. The ECG signal acquisition module 10 also includes a display screen; the preliminary analysis results are displayed through the display screen. In the present invention, the preliminary analysis includes simple algorithms such as heart rate calculation and ECG baseline correction. These algorithms are simple to calculate and have low requirements for the computing performance of the processor. The MCU performing the preliminary analysis can ensure the real-time nature of signal processing, ensure that users obtain real-time feedback during health monitoring, and improve the processing speed of ECG data. The amount of data of the preliminary analysis results is small, reducing the data transmission requirements. Users can still perform basic monitoring and analysis in a poor network environment, ensuring the stability and reliability of the system.

[0029] As an edge device, the ECG signal acquisition module deploys simple algorithms such as heart rate calculation and ECG baseline correction, while the cloud server is responsible for deploying intelligent diagnosis algorithms for common heart diseases. This hierarchical algorithm deployment gives full play to the computing capabilities of the edge-side devices and cloud devices, effectively improving the user experience and the doctor's diagnosis efficiency.

[0030] After the ECG signal analysis cloud server performs in-depth analysis or obtains the preliminary analysis results from the cloud platform, the analysis results are pushed to the user-side application and the doctor-side application. As Figure 3 shown, in the present invention, the user-side application includes a WeChat mini-program and an online diagnosis website, and the doctor-side application includes a PC-side application and an online diagnosis website. In the WeChat mini-program, users can conveniently view the heart rate measurement results, ECG waveform records, and intelligent diagnosis reports. Through the intuitive interface design, users can quickly obtain their own health data and timely understand their heart health status. In addition, the mini-program also supports the real-time push function, and users can receive feedback and suggestions from doctors in the first place, enhancing the interaction between users and medical services. The design of the mini-program fully considers the user experience and supports various interaction methods, such as swiping and clicking, making the operation simple and easy to understand and suitable for users of all ages. Relying on the huge user base of the WeChat platform, this mini-program can be quickly promoted to more users, providing them with convenient ECG monitoring and health management services.

[0031] The PC - side application of the present invention aims to provide a professional health monitoring and diagnosis platform for doctors. The user - friendly interface on the PC side enables doctors to conveniently access patients' electrocardiogram (ECG) data and historical records. Doctors can view ECG waveforms and heart rate data in real time and generate detailed diagnostic reports to ensure accurate medical decisions. In addition, the PC - side application supports multitasking, allowing doctors to monitor multiple patients simultaneously, thus improving work efficiency. It conducts data interaction with the mini - program side for users. Through a secure online communication function, doctors can communicate with patients in real time, answer their questions, and provide personalized health advice for patients.

[0032] The online diagnosis website aims to provide convenient health services for doctors and users. The website is divided into a doctor side and a user side. The doctor side allows doctors to log in and access patients' ECG data and diagnostic reports, while the user side enables patients to view their own health information. Through the website, users can obtain real - time ECG monitoring results, diagnostic opinions, and professional advice, and it also supports online communication with doctors. This platform ensures data security and privacy protection, promoting the efficiency and reliability of medical services. By combining the PC - side application with the online website, the present invention realizes multi - channel medical services, enhances the interaction and communication between doctors and patients, and promotes the development of intelligent healthcare.

[0033] Multiple types of user - side and doctor - side applications can enable users to conduct self - detection and family member detection, and also enable doctors to remotely monitor. In addition, it can centrally manage the ECG data of multiple users.

[0034] The present invention provides an idea for electrocardiogram signal monitoring based on the Internet of Things. There are many methods and ways to specifically implement this technical solution. The above - mentioned is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be realized by existing technologies.

Claims

1. An electrocardiogram signal monitoring system based on the Internet of Things, characterized in that: It includes an ECG signal acquisition module and an ECG signal analysis cloud server; the ECG signal acquisition module acquires raw ECG signals and performs preprocessing, and then sends them to the cloud platform through the Internet of Things communication module; the ECG signal analysis cloud server accesses the cloud platform to obtain the ECG signal update status, and if there is data update, the ECG signal analysis cloud server obtains the updated data and transfers it to the local storage area, and performs in-depth analysis on the updated ECG data, and pushes the in-depth analysis results to the user-side application and / or the doctor-side application; The ECG signal acquisition module performs preliminary analysis on the preprocessed ECG signal. When the transmission rate of the Internet of Things communication module is lower than a preset threshold, the preliminary analysis result is sent to the cloud platform and the sending of the preprocessed ECG signal is stopped.

2. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The preliminary analysis includes heart rate calculation and ECG baseline correction.

3. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The ECG signal acquisition module includes a lead interface, an amplifying and filtering circuit, an MCU and an Internet of Things communication module; the original ECG signal is input from the lead interface, the amplifying and filtering circuit suppresses the common-mode signal of the original ECG signal and amplifies the differential-mode signal, filters out the power frequency interference signal, and provides a bias voltage to raise the ECG signal to above 0V; the MCU samples the ECG signal after front-end amplification and performs digital filtering, and then sends it to the cloud platform through the Internet of Things communication module.

4. The electrocardiogram signal monitoring system according to claim 1, characterized in that: It also includes a display screen; the preliminary analysis results are displayed through the display screen.

5. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The IoT communication module is the Fibocom L610 wireless communication module, and the MCU transmits data with the IoT communication module via the serial port.

6. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The IoT communication module sends data to the cloud platform, and the ECG signal analysis cloud server obtains ECG signals from the cloud platform, both using encrypted communication.

7. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The ECG signal analysis cloud server uses Nginx as a reverse proxy cloud server.

8. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The ECG signal analysis cloud server builds an application framework based on Flask.

9. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The ECG signal analysis cloud server performs in-depth analysis on the ECG data, and the in-depth analysis includes generating an ECG waveform diagram, processing the ECG data in combination with an intelligent diagnosis algorithm, and obtaining diagnosis suggestions.

10. The electrocardiogram signal monitoring system according to claim 1, characterized in that: The user-side application includes a WeChat applet and an online diagnosis website, and the doctor-side application includes a PC-side application and an online diagnosis website.

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