Patient sign monitoring device and monitoring method thereof

Abstract

The invention relates to a patient physical sign monitoring device and a monitoring method thereof, which can collect, process and transmit key physical sign data of a patient in real time, including electrocardiosignals, pulse signals, oxyhemoglobin saturation and body temperature signals. The device comprises a sensor unit, a data processing unit, a wireless communication module, a display and control unit, a battery management system and a remote monitoring platform. The sensor unit is used for collecting physical sign data in a high-precision mode, the data processing unit processes and analyzes the data through algorithms such as fast Fourier transform, peak detection and moving average filtering, and the accuracy and reliability of the data are ensured. The wireless communication module adopts an AES encryption algorithm to guarantee data transmission safety, the display and control unit provides real-time data visualization and alarm threshold setting functions, and the remote monitoring platform analyzes the health state of a patient through an intelligent algorithm and generates a health assessment report.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and in particular to a patient vital sign monitoring device and a monitoring method thereof. Background Art

[0002] With the rapid development of modern medical technology, the application of patient vital sign monitoring devices in the medical field has become more and more extensive. Vital sign monitoring devices can provide doctors with timely and accurate reference data through real-time collection and analysis of patients' ECG signals, pulse signals, blood oxygen saturation, body temperature and other physiological parameters, helping medical institutions to conduct a comprehensive assessment of patients' health status. However, existing vital sign monitoring technologies and equipment still have some technical bottlenecks and application limitations, which are mainly reflected in the following aspects:

[0003] Many vital sign monitoring devices currently on the market are greatly affected by environmental factors during the signal acquisition process. For example, ECG signals are easily affected by electromagnetic interference and baseline drift, resulting in reduced signal quality; pulse signals are limited by the acquisition location and the degree of skin contact, and data loss or abnormal fluctuations often occur. In addition, the body temperature sensor has a slow response speed and large measurement errors, making it difficult to provide accurate readings under rapidly changing body temperature conditions. These problems make existing monitoring equipment unable to meet the needs of high-precision medical scenarios, especially in critical care or emergency conditions, where higher requirements are placed on the real-time and accuracy of data, and the performance of existing equipment has not yet reached the ideal level.

[0004] Currently, many monitoring devices can only collect a single or a few vital sign parameters and lack the ability to integrate multi-parameter data. For example, there is a certain correlation between ECG signals, blood oxygen saturation and pulse signals. If these data can be analyzed jointly, the patient's health status can be assessed more accurately. However, existing equipment usually only provides analysis results for a single parameter and cannot perform correlation analysis between multiple parameters. This single analysis method requires doctors to rely on multiple devices to obtain different parameter data separately during the diagnosis process, which increases the complexity of data integration and may also affect the final diagnosis results due to the asynchrony of data sources.

[0005] Although existing vital sign monitoring equipment can collect patients' vital sign data, there are great limitations in data processing. For example, some devices only process ECG signals at the level of simple filtering and feature extraction, lacking in-depth analysis in the frequency and time domains; the calculation methods for blood oxygen saturation mostly use simple empirical formulas, and do not fully utilize advanced signal processing technology to optimize the data. The shortcomings of these processing methods have limited the reliability and accuracy of the data output by the equipment, and cannot provide in-depth support for complex diseases. In addition, existing equipment generally lacks the ability to conduct in-depth analysis of historical data, such as trend prediction and risk assessment, which is crucial in the long-term management of chronic diseases.

[0006] Wireless transmission of vital sign data is an important part of remote monitoring, but existing equipment has obvious deficiencies in data transmission security. Some devices use relatively simple wireless communication protocols and lack encryption protection, making them vulnerable to cyber attacks and data leaks. At the same time, patients' vital sign data are highly sensitive information, and how to ensure data privacy during remote transmission and data storage is still an unresolved issue.

[0007] The display and control interface of existing vital sign monitoring equipment is usually relatively simple, and the user experience is poor. For example, the display unit of some equipment can only provide basic numerical display, lacks data visualization capabilities, and cannot intuitively display the changing trends of vital sign parameters in a graphical form. At the same time, the alarm function of existing equipment usually uses fixed thresholds, lacks personalized setting capabilities, and cannot dynamically adjust the threshold according to the specific situation of the patient, which is easy to cause false alarms or missed alarms.

[0008] Most vital sign monitoring devices require complex hardware support and high energy consumption, are large in size and heavy in weight, and are not suitable for mobile applications. In addition, insufficient battery life is also a major problem with existing devices. For example, during long-term monitoring, the device's battery may run out at a critical moment, thus interrupting the monitoring process and affecting the continuity and integrity of the data.

[0009] Many high-end vital sign monitoring devices are expensive and are mainly used in hospitals and other medical institutions. For home health monitoring or primary medical institutions, the purchase cost is high, which limits its popularization and application. In addition, the high maintenance cost and complex operation of the equipment have also affected the widespread application of the equipment to a certain extent.

[0010] Although the existing patient vital signs monitoring technology has achieved real-time monitoring of patient vital signs to a certain extent, there is still much room for improvement in terms of data collection accuracy, multi-parameter comprehensive analysis capabilities, data processing technology, wireless communication security, human-computer interaction design, portability and cost. These shortcomings provide a clear direction for the research and development of the next generation of vital signs monitoring equipment, and also reflect the necessity and importance of developing a high-precision, intelligent and multifunctional patient vital signs monitoring device. Summary of the invention

[0011] On the one hand, a patient vital sign monitoring device comprises the following modules:

[0012] Sensor unit: used to collect the patient's vital sign data, including ECG signal E(t), pulse signal P(t), blood oxygen saturation signal S(t) and body temperature signal T(t);

[0013] Data processing unit: used to pre-process and analyze the vital sign data collected by the sensor unit. The processed data are defined as E′(t), P′(t), S′(t), and T′(t), respectively, where E′(t) is the filtered ECG signal, P′(t) is the extracted pulse cycle, S′(t) is the calculated blood oxygen saturation, and T′(t) is the denoised body temperature signal;

[0014] Wireless communication module: used to transmit the vital sign parameters V(t)=[E′(t), P′(t), S′(t), T′(t)] output by the data processing unit to the remote monitoring platform in real time via wireless mode;

[0015] Display and control unit: used to display the real-time parameter V(t) and provide alarm threshold setting function, where the threshold is defined as V th =[E th ,P th ,S th ,T th ];

[0016] Battery management system: provides power to the device, and the battery status monitoring parameters are B(t), including the power B cap (t), charging voltage B vol (t) and current B cur (t);

[0017] Remote monitoring platform: used to receive vital sign data V(t), analyze patient health status and record historical data H(t), where

[0018] The present invention also proposes a monitoring method of a patient vital sign monitoring device, comprising the following steps:

[0019] The sensor unit is used to collect the patient's electrocardiogram signal E(t), pulse signal P(t), blood oxygen signal S(t) and body temperature signal T(t);

[0020] The data processing unit pre-processes E(t), P(t), S(t) and T(t) to obtain processed data E′(t), P′(t), S′(t), T′(t);

[0021] The processed data V(t) = [E′(t), P′(t), S′(t), T′(t)] is transmitted to the remote monitoring platform in real time through the wireless communication module;

[0022] The display and control unit displays the vital sign data V(t) and generates an alarm according to the set alarm threshold V th Make abnormal alarm;

[0023] The remote monitoring platform records the historical curve of vital signs data And generate an assessment report on the patient's health status.

[0024] As a preferred technical solution of the present invention, the data processing unit uses fast Fourier transform (FFT) to perform frequency domain analysis on the electrocardiogram signal E(t), and the specific steps include:

[0025] The collected ECG signal E(t) is discretized to obtain a discrete sequence E[n]; the frequency domain signal E[k] is obtained by applying fast Fourier transform to E[n]:

[0026]

[0027] Where N is the number of sampling points;

[0028] The non-target frequency band signal is filtered out by the frequency domain filter H[k], and the filtered ECG signal E′(k) is output:

[0029] E′(k)=E[k]·H[k];

[0030] Apply inverse Fourier transform to restore the time domain signal E′(t):

[0031] E′(t)=IFFT(E′(k)).

[0032] As a preferred technical solution of the present invention, the period P of the pulse signal P(t) c Calculated by peak detection algorithm, the specific method includes: normalizing the signal of P(t):

[0033]

[0034] Detect the local maximum point P of the signal peak, calculate the time interval Δt between adjacent peak points i ; Calculate the pulse period P by the following formula c :

[0035] P c =max(Δt i ).

[0036] As a preferred technical solution of the present invention, the method for calculating the blood oxygen saturation S(t) is: collecting red light signal R(t) and infrared signal IR(t); calculating the light absorption ratio Ratio(t):

[0037]

[0038] Wherein AC and DC are the alternating current component and the direct current component respectively; the blood oxygen saturation S(t) is calculated by the empirical formula: S(t)=110-25·Ratio(t).

[0039] As a preferred technical solution of the present invention, the device detects the blood oxygen saturation S(t) th When an alarm occurs, the alarm signal is sent to the remote monitoring platform through the wireless communication module.

[0040] 7. The patient vital sign monitoring method according to claim 2, characterized in that the filtering process of the body temperature signal T(t) adopts a sliding average filtering algorithm, and its calculation formula is:

[0041]

[0042] Where N is the sliding window size and T′(t) is the filtered body temperature signal.

[0043] As a preferred technical solution of the present invention, the remote monitoring platform calculates the patient's health index H according to the received vital sign data V(t). i (t), which is calculated as:

[0044] H i (t) = w 1 ·E′(t)+w 2 ·P′(t)+w 3 ·S′(t)+w 4 ·(T)′(t),

[0045] Where W 1 ,w 2 ,w 3 ,w 4 is the weight parameter, satisfying w 1 +w 2 +w 3 +w 4 =1. ​

[0046] As a preferred technical solution of the present invention, the health index H i The weight parameter w of (t) i Dynamically adjust according to the patient's real-time status, and the adjustment formula is:

[0047]

[0048] where x i is the current vital sign data, μ i is the normal value, and α is the adjustment coefficient.

[0049] As a preferred technical solution of the present invention, the wireless communication module supports encrypted transmission and uses the AES encryption algorithm to encrypt the vital sign data V(t), and the encryption formula is:

[0050] V enc (t) = AES K (V(t)),

[0051] Where K is the key;

[0052] The display and control unit supports a variety of vital sign data visualization functions, including a line graph to display real-time data V(t), a bar graph to display data statistical results mean(V(t)), and a trend analysis graph to display historical data H(t).

[0053] Beneficial effects:

[0054] The patient vital signs monitoring device of the present invention can simultaneously collect four key vital signs parameters, namely, ECG signals, pulse signals, blood oxygen saturation and body temperature, and process the data with high precision through advanced sensor design and optimized signal processing algorithms. Compared with the prior art, the present device significantly improves the accuracy and stability of signal acquisition. It uses fast Fourier transform (FFT) to perform frequency domain analysis on ECG signals, combined with peak detection algorithm and sliding average filtering method, which can effectively remove signal noise and accurately extract key characteristic parameters. In addition, the multi-parameter integration function of the device realizes the joint analysis of ECG, blood oxygen, pulse and body temperature data, providing more reliable data support for the comprehensive assessment of the patient's health status.

[0055] The present invention realizes the real-time transmission of patient vital sign data through the wireless communication module, and combines the AES encryption algorithm to ensure the security of data during remote transmission. The remote monitoring platform can perform intelligent analysis on the received vital sign data and trigger the alarm mechanism in real time according to the personalized alarm threshold. This design not only improves the reliability and security of data transmission, but also effectively reduces the occurrence of false alarms or missed alarms, so that medical staff can respond to potential health risks in a timely manner. Especially in scenarios such as ICU or home remote monitoring, this device can greatly shorten the response time of critical situations and improve the timeliness of medical intervention.

[0056] The display and control unit of the present invention provides a wealth of visualization functions, including real-time data line graphs, bar charts and trend analysis graphs, so that users can intuitively understand the real-time changes and historical trends of patients' vital signs. In addition, the device is lightweight in design, has excellent portability and low power consumption, and can be used in a variety of scenarios such as hospital monitoring, community health screening, family health management and sports health monitoring. By combining high-precision vital sign monitoring with a remote monitoring platform, the device achieves long-term and continuous health management of patients, effectively filling the gaps in existing technologies in multi-scenario applications and data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 The present invention is a structural diagram of a patient vital sign monitoring device;

[0059] Figure 2 The present invention is a flowchart of a method for monitoring patient vital signs. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The following is combined with Figure 1 and Figure 2 , the specific implementation methods of the present invention are described in detail.

[0062] Example 1: Real-time monitoring of vital signs of patients in the ICU ward;

[0063] Working principle of sensor unit and data collection: In the ICU ward, the patient's vital signs need to be monitored in real time to prevent emergencies. This embodiment is based on the vital sign monitoring device in the claims and is deployed on a patient with severe cardiovascular disease. The detailed implementation is as follows:

[0064] Electrocardiogram (ECG): The sensor collects ECG data at a sampling frequency of 500 Hz, and the signal is collected through a three-electrode lead attached to the patient's chest.

[0065] Pulse signal (PPG): Red and infrared light emitters and photodetectors are embedded in the finger clip sensor to measure the pulse waveform at the end of the finger. Blood oxygen saturation (SpO2): Combined with the pulse signal, it is obtained by calculating the ratio of red light to infrared light absorption.

[0066] Body temperature signal: The thermistor sensor is attached to the patient's armpit and records the body temperature at a sampling frequency of 1 Hz.

[0067] Detailed implementation of the data processing unit: ECG signal processing: The collected signal is preprocessed to remove baseline drift and high-frequency noise, and the frequency domain characteristics are analyzed using fast Fourier transform (FFT):

[0068]

[0069] The filtered signal is regressed into the time domain through inverse Fourier transform to detect the heart rate and R wave amplitude in real time.

[0070] Pulse signal processing: The pulse waveform is normalized and the peak detection algorithm is applied to calculate the pulse cycle. Blood oxygen saturation calculation: Collect red light (AC / DC) and infrared light (AC / DC) signals and calculate the ratio:

[0071] Through the empirical formula:

[0072] Realize real-time calculation of blood oxygen saturation.

[0073] Temperature signal filtering: Use sliding average filtering algorithm to smooth temperature fluctuations:

[0074]

[0075] Wireless communication module transmission: The output data (heart rate, pulse cycle, blood oxygen saturation, body temperature) of the data processing unit is transmitted to the remote monitoring platform via the Bluetooth low energy (BLE) module. The data is encrypted by AES:

[0076] C=E(K,P)

[0077] Among them, K is the key, P is the original data, and the transmission ensures the security of the data.

[0078] Remote monitoring platform and alarm mechanism:

[0079] The monitoring platform updates data every 5 seconds, displaying the patient's real-time vital signs data chart. If the heart rate exceeds 150bpm, blood oxygen is less than 90%, and the body temperature exceeds 38.5℃, the system triggers an alarm and sends the data to the terminal device of the ICU nurse station, prompting medical staff to check the patient's status.

[0080] Platform generated health index:

[0081] HI=0.4·HeartRate+0.3·SpO 2 +0.2·T f

[0082] When the index is below 60, the system automatically issues a "dangerous" level alarm.

[0083] Example 2: Remote health monitoring of the elderly at home;

[0084] This embodiment is applied in home scenarios and mainly serves elderly people living alone.

[0085] Equipment configuration and installation: The sensor unit is fixed in the wristband of the elderly and can continuously record the ECG signal, pulse waveform and body temperature.

[0086] The data processing unit is integrated into the wristband device, which is small in size and low in power consumption. The wireless communication module uploads the data to the cloud server via Wi-Fi.

[0087] Monitoring and data processing: The frequency of data collection is reduced to once per minute to save power.

[0088] The system monitors blood oxygen saturation in real time. If it is lower than 92%, the device will immediately vibrate and sound an alarm, and at the same time send an alarm message to the mobile phone of the family member who is remotely monitoring.

[0089] Cloud platform analysis and data visualization: Data is stored long-term through a remote platform and trend analysis curves are drawn.

[0090] Family members can view data in real time through the mobile app and set custom alarm thresholds, such as body temperature exceeding 37.8℃ or pulse cycle being too short.

[0091] Health assessment and reporting: The platform generates a health assessment report every week, including average heart rate, body temperature fluctuations, and blood oxygen changes, and recommends whether further medical examinations are needed.

[0092] Example 3: Athlete training load monitoring;

[0093] This embodiment is used to monitor the physical condition of athletes in real time during high-intensity sports training to avoid injuries caused by excessive training.

[0094] Sports scene deployment: ECG sensors and pulse sensors are embedded in sportswear and made of flexible materials to adapt to strenuous exercise.

[0095] The data is transmitted to the coach’s tablet computer via Bluetooth to monitor the athlete’s status in real time.

[0096] Calculate the exercise intensity index: Calculate the exercise intensity index (SI) based on the ratio of heart rate to maximum heart rate:

[0097]

[0098] If the index is higher than 85%, the platform will automatically remind the coach to adjust the training intensity.

[0099] Heat stress assessment: Calculate heat stress status using sliding mean body temperature:

[0100] HeatStress=T f -37.5

[0101] If the heat stress value exceeds 1.0, a high-risk warning will be displayed.

[0102] Example 4: Health screening at community medical centers

[0103] Cardiovascular abnormality detection: In community medical centers, this device is used for resident health screening to achieve rapid vital sign data collection and analysis.

[0104] Screening process: Each resident wears the device for 3 minutes to collect electrocardiogram signals, pulse signals and body temperature.

[0105] Vital sign data is uploaded to the screening system platform via Wi-Fi to generate a health risk assessment report.

[0106] The system calculates the arrhythmia index by analyzing the ECG signal spectrum:

[0107]

[0108] If the HRV value is greater than 10%, it indicates the risk of arrhythmia.

[0109] Large-scale data management: The platform records the historical data of each resident, supports health trend analysis, and schedules further medical examinations for residents with suspected abnormalities.

[0110] Example 5: Big data integration in smart hospitals;

[0111] Connecting the device to the intelligent hospital information system (HIS) enables comprehensive integration and management of vital sign data within the hospital.

[0112] Deployment method: Each hospitalized patient is equipped with a device that monitors vital sign data in real time.

[0113] The data is transmitted to the hospital’s central data platform via a secure network.

[0114] Real-time analysis on the doctor side: Doctors can view the vital signs data of all patients in the ward through the HIS system and make real-time comparisons.

[0115] The platform automatically generates personalized health monitoring plans based on patient data.

[0116] Long-term health assessment: The system generates a trend report of physical sign data for up to a week or a month to assist doctors in adjusting treatment plans.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A patient vital sign monitoring device, characterized in that: Includes the following modules: Sensor unit: used to collect the patient's vital sign data, including ECG signal E(t), pulse signal P(t), blood oxygen saturation signal S(t) and body temperature signal T(t); Data processing unit: used to pre-process and analyze the vital sign data collected by the sensor unit. The processed data are defined as E′(t), P′(t), S′(t), and T′(t), respectively, where E′(t) is the filtered ECG signal, P′(t) is the extracted pulse cycle, S′(t) is the calculated blood oxygen saturation, and T′(t) is the denoised body temperature signal; Wireless communication module: used to transmit the vital sign parameters V(t)=[E′(t), P′(t), s′(t), T′(t)] output by the data processing unit to the remote monitoring platform in real time by wireless means; Display and control unit: used to display the real-time parameter V(t) and provide alarm threshold setting function, where the threshold is defined as V th =[E th ,P th ,S th ,T th ]; Battery management system: provides power to the device, and the battery status monitoring parameters are B(t), including the power B cap (t), charging voltage B vol (t) and current B cur (t); Remote monitoring platform: used to receive vital sign data V(t), analyze patient health status and record historical data H(t), where 2. A monitoring method using the patient vital sign monitoring device according to claim 1, characterized in that: The following steps are involved: The sensor unit is used to collect the patient's electrocardiogram signal E(t), pulse signal P(t), blood oxygen signal S(T) and body temperature signal T(t); The data processing unit pre-processes E(t), P(t), S(t) and T(t) to obtain processed data E′(t), P′(t), S′(t), T′(t); The processed data V(t) = [E′(t), P′(t), S′(t), T′(t)] is transmitted to the remote monitoring platform in real time through the wireless communication module; The display and control unit displays the vital sign data V(t) and generates an alarm according to the set alarm threshold V th Make abnormal alarm; The remote monitoring platform records the historical curve of vital signs data And generate an assessment report on the patient's health status.

3. The patient vital sign monitoring method according to claim 2, characterized in that: The data processing unit performs frequency domain analysis on the electrocardiogram signal E(t) using fast Fourier transform (FFT), and the specific steps include: The collected ECG signal E(t) is discretized to obtain a discrete sequence E[n]; the frequency domain signal E[k] is obtained by applying fast Fourier transform to E[n]: Where N is the number of sampling points; The non-target frequency band signal is filtered out by the frequency domain filter H[k], and the filtered ECG signal E′(k) is output: E′(k)=E[k]·H[k]; Apply inverse Fourier transform to restore the time domain signal E′(t): E′(t)=IFFT(E′(k)).

4. The patient vital sign monitoring method according to claim 2, characterized in that: The period P of the pulse signal P(t) c Calculated by peak detection algorithm, the specific method includes: normalizing the signal of P(t): Detect the local maximum point P of the signal peak , calculate the time interval Δt between adjacent peak points i ; Calculate the pulse period P by the following formula c : P c =max(Δt i )。 5. The patient vital sign monitoring method according to claim 2, characterized in that: The blood oxygen saturation S(t) is calculated by collecting the red light signal R(t) and the infrared signal IR(t); and calculating the light absorption ratio Ratio(t): Wherein AC and DC are the alternating current component and the direct current component respectively; the blood oxygen saturation S(t) is calculated by the empirical formula: S(t)=110-25·Ratio(t).

6. The patient vital sign monitoring method according to claim 5, characterized in that: The device detects the blood oxygen saturation S(t) th When an alarm occurs, the alarm signal is sent to the remote monitoring platform through the wireless communication module.​ 7. The patient vital sign monitoring method according to claim 2, characterized in that: The filtering process of the body temperature signal T(t) adopts a sliding average filtering algorithm, and its calculation formula is: Where N is the sliding window size and T′(t) is the filtered body temperature signal.

8. The patient vital sign monitoring method according to claim 2, characterized in that: The remote monitoring platform calculates the patient's health index H based on the received vital sign data V(t) i (t), which is calculated as: H i (t)=w1·E′(t)+w2·P′(t)+w3·S′(t)+w4·T′(t), Among them, w1, w2, w3, w4 are weight parameters, satisfying w1+w2+w3+w4=1.

9. The patient vital sign monitoring method according to claim 8, characterized in that: The health index H i The weight parameter w of (t) i Dynamically adjust according to the patient's real-time status, and the adjustment formula is: where x i is the current vital sign data, μ i is the normal value, and α is the adjustment coefficient.

10. The patient vital sign monitoring method according to claim 1, characterized in that: The wireless communication module supports encrypted transmission and uses the AES encryption algorithm to encrypt the vital sign data V(t). The encryption formula is: V enc (t)=AES K (V(t)), Where K is the key; The display and control unit supports a variety of vital sign data visualization functions, including a line graph to display real-time data V(t), a bar graph to display data statistical results mean(V(t)), and a trend analysis graph to display historical data H(t).

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