Wearable wireless transmission electrocardiogram monitoring and early warning vest

Abstract

The invention discloses a wearable wireless transmission electrocardiogram monitoring and early warning vest, and relates to the technical field of medical equipment. The problems that in the prior art, the local storage space of equipment is limited, a large amount of electrocardiogram data cannot be stored for a long time, the electrocardiogram data cannot be transmitted to medical professionals or guardians of users in real time based on remote analysis and management, and timely treatment is affected are solved. Through comprehensive and accurate electrocardiosignal acquisition, a user is notified in time through the buzzer when abnormality is detected, the wrist strap type active alarm can also actively give an alarm when the user is uncomfortable, alarm information is sent to a preset emergency contact through a wireless network, and the user can conveniently and rapidly carry out emergency treatment. The main control chip performs data interaction with the cloud monitoring platform based on a wireless network, so that a doctor or a family member can remotely monitor the electrocardiogram condition of a user and provide timely medical intervention suggestions, the standby power supply effectively ensures that the equipment can still work continuously under the condition that the main power supply is exhausted, and the life safety of the user is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a wearable wireless transmission electrocardiogram monitoring and early warning vest. Background Art

[0002] With the development of wearable technology, a variety of portable ECG monitoring devices have appeared on the market. For example, the Chinese patent with the announcement number CN214804773U discloses a wearable neck ECG monitoring vest, which includes a neck protector body and a vest body. The neck protector body and the vest body are connected by a first wire and a second wire respectively. The vest body is provided with an ECG monitor, which is arranged on the inner side of the left front chest of the vest body. The vest body is also provided with a controller, and the vest body is provided with an indicator light, a power battery, a display screen and a switch button respectively. The controller is connected to the indicator light, the power battery, the ECG monitor, the display screen and the switch button respectively. A jack is provided at the bottom of one side of the vest body, and the power battery is connected to the jack through a wire. The vest realizes the integration of ECG monitoring, drug prevention, massage and neck protection in one body, which can facilitate people to perform ECG monitoring, massage, drug prevention and neck protection, so that patients can detect and prevent diseases early and increase the effect of disease cure.

[0003] Although the above patents can provide users with comprehensive health monitoring and health care services, there are still the following problems:

[0004] In the existing technology, the local storage space of the device is limited, and a large amount of ECG data cannot be stored for a long time. It is also impossible to transmit the data to medical professionals or the user's guardian in real time based on remote analysis and management, which affects timely treatment. When ECG abnormalities are detected, it is impossible to automatically issue an early warning to the user or medical institution. Summary of the invention

[0005] The purpose of the present invention is to provide a wearable wireless transmission ECG monitoring and early warning vest, which realizes real-time, accurate monitoring and intelligent early warning of the user's ECG data. At the same time, the data processing capability and intelligent analysis capability of the cloud monitoring platform further improve the accuracy of monitoring and the timeliness of early warning, providing strong protection for the user's health, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A wearable wireless transmission ECG monitoring and early warning vest includes a wireless monitoring vest, a sensor module is installed in the wireless monitoring vest, the sensor module is electrically connected to a main control chip, one side of the main control chip is electrically connected to a buzzer and an active alarm, the main control chip exchanges data with a cloud monitoring platform based on a wireless network, and a backup power supply is installed on one side of the wireless monitoring vest.

[0008] Furthermore, the main control chip includes:

[0009] The signal acquisition module is used to obtain the ECG monitoring data collected by the sensor module and pre-process the ECG monitoring data;

[0010] A signal amplification module, used to amplify the pre-processed ECG monitoring data to generate an analog signal of the ECG monitoring data;

[0011] The analog-to-digital conversion module is used to convert the amplified analog signal into a digital signal and perform data verification on the converted digital signal;

[0012] A wireless transmission module is used to transmit the verified complete digital signal to the cloud monitoring platform in real time based on a wireless network;

[0013] The power module is used to provide a stable power supply for the main control chip based on the main power supply, and monitor the main power status data. When the main power supply power is lower than the preset threshold, it switches to the backup power charging mode.

[0014] Furthermore, the main control chip also includes:

[0015] The user information collection module is used to actively collect basic user information and perform identity authentication. At the same time, a unique account identifier is assigned to each user;

[0016] User information management module, used to review and modify user information and generate the user's personal profile based on the user's basic information and unique account identifier;

[0017] The user data binding module is used to bind the wireless monitoring vest to the user's account through the identity authentication process before the user starts ECG monitoring.

[0018] Furthermore, the cloud monitoring platform includes:

[0019] A data processing unit, used for performing redundant processing on the acquired complete digital signal to obtain processed monitoring data;

[0020] The abnormality monitoring unit is used to identify abnormalities in the monitoring data output by the data processing unit, monitor abnormal waveforms and abnormal parameter change data, and determine the abnormal type and abnormal level according to the abnormal monitoring results.

[0021] The anomaly monitoring unit is also used to compare the current data with the user's historical data, analyze the abnormal change trend, and at the same time, obtain the user's real-time positioning data when an abnormality is detected;

[0022] An early warning unit is used to match corresponding early warning instructions from a preset early warning strategy library according to the abnormality type and abnormality level, and send the early warning instructions to the main control chip based on the wireless network;

[0023] At the same time, for serious anomalies that exceed the preset risk level, the direct alarm process will be automatically triggered, sending a distress signal to medical institutions or emergency rescue centers, and providing users with real-time positioning data.

[0024] Furthermore, the data processing unit performs redundancy processing, specifically:

[0025] The complete digital signal is divided into multiple data segments according to a fixed time window, and the transient timing verification is performed on the sub-data in each data segment, and the modal characteristic change value and sequence feedback coefficient of the sub-data are determined according to the verification result;

[0026] Determine the modal characteristic attenuation index of the sub-data according to the attenuation rate of the modal characteristic change value of the sub-data to the signal energy;

[0027] The target hash value of the sub-data is calculated according to the modal characteristic attenuation index and the sequence feedback coefficient of the sub-data, and the normal ECG data obtained based on the big data are statistically analyzed to determine the preset hash value;

[0028] Confirm whether the target hash value of each sub-data is greater than or equal to the preset hash value. If so, confirm that the sub-data is not redundant data. If not, confirm that the sub-data is redundant data.

[0029] Extract redundant data from the complete digital signal, determine the specific location and attributes of the redundant data, and eliminate the redundancy to obtain the processed monitoring data.

[0030] Furthermore, the time length setting of the time window is obtained through the following process, including:

[0031] Set the heart rate collection time period;

[0032] Collect the user's heart rate data in each unit time during the heart rate collection time period, wherein the value of the unit time is 1 minute;

[0033] Retrieve the reference time window length stored in the database;

[0034] Obtaining a heart rate characterization coefficient using the user's heart rate data per unit time;

[0035] The heart rate characterization coefficient is obtained by the following formula:

[0036]

[0037] Where S represents the heart rate characterization coefficient; T represents the length of the reference time window; t represents the length of the unit time; n represents the number of unit times experienced by the heart rate data collection, and nt is not less than 5T; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR max and HR min represents the maximum heart rate value and the minimum heart rate value corresponding to n unit time; m represents the number of unit time intervals between the maximum heart rate value and the minimum heart rate value; λ represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0038]

[0039] Among them, λ represents the adjustment coefficient; n represents the number of unit time experienced by heart rate data collection; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR z represents the median heart rate corresponding to n unit time; k i represents the number of unit times between the heart rate value of the i-th unit time and the unit time to which the middle value of the heart rate belongs; t represents the duration of the unit time;

[0040] Comparing the heart rate characterization coefficient with a preset heart rate characterization coefficient threshold;

[0041] The time length corresponding to the fixed time window is determined according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold.

[0042] Further, determining the time length corresponding to the fixed time window according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold includes:

[0043] When the heart rate characterization coefficient is lower than a preset heart rate characterization coefficient threshold, maintaining the time length of the time window as a reference time window length, that is, taking the reference time window length as a fixed time window;

[0044] When the heart rate characterization coefficient exceeds a preset heart rate characterization coefficient threshold, the heart rate characterization coefficient is retrieved;

[0045] Using the heart rate characterization coefficient in combination with the reference time window length, obtaining the time length corresponding to the fixed time window;

[0046] The time length corresponding to the fixed time window is obtained by the following formula:

[0047]

[0048] Among them, T g represents the time length corresponding to the fixed time window; S represents the heart rate characterization coefficient; S y represents the preset heart rate characterization coefficient threshold; n represents the number of unit time experienced by heart rate data collection; k i It represents the number of unit times between the heart rate value of the ith unit time and the unit time to which the middle heart rate value belongs; m represents the number of unit times between the maximum heart rate value and the minimum heart rate value; T represents the length of the reference time window.

[0049] Furthermore, the data processing unit further includes:

[0050] Data analysis module, used to analyze long-term and short-term trends in the processed monitoring data, identify potential ECG activity patterns in the processed monitoring data, and present the analysis results to users in the form of charts, reports and other visual forms;

[0051] The feature extraction module is used to extract the ECG signal from the processed monitoring data, determine the key features of the ECG signal, extract the frequency domain features of the ECG signal based on the key features, and identify slight changes and potential risks in the ECG signal based on the frequency domain features.

[0052] Furthermore, the abnormality monitoring unit includes:

[0053] An abnormality identification module is used to identify abnormal waveforms and parameter change data of the ECG activity pattern of the monitoring data that are inconsistent with the normal pattern based on the identified ECG activity pattern and feature extraction results, determine the abnormal type of the monitoring data according to the abnormal characteristics of the abnormal waveform and parameter change data, and determine a preset abnormal threshold based on the classification result;

[0054] The risk assessment module is used to compare the output of the anomaly identification module with the preset anomaly threshold, assess the anomaly level of the monitoring data, analyze the changing trend of the abnormal monitoring data in combination with the user's historical data, predict possible future impacts, and match the corresponding emergency response plan according to the risk assessment results;

[0055] The positioning data processing module is used to automatically trigger the data instruction of collecting the user's real-time positioning when an anomaly is detected, associate the monitoring data with the abnormality with the positioning data, and identify the specific location and scene where the anomaly occurs.

[0056] Furthermore, the early warning unit includes:

[0057] The warning instruction generation module is used to match the corresponding warning instructions according to the abnormal type and abnormal level assessment results of the monitoring data, and formulate a warning strategy based on the user's personal information and ECG monitoring history records;

[0058] The warning notification module is used to send the generated warning instructions to the main control chip through the wireless network, trigger the buzzer to sound and light alarm, receive the user's confirmation feedback on the warning notification, and determine the receipt and processing of the warning information;

[0059] The rescue dispatch module is used to conduct risk assessment on serious anomalies that exceed the preset risk level, and decide whether to trigger the direct alarm process based on the assessment results, send a distress signal to nearby medical institutions or emergency rescue centers, provide users with real-time positioning data, and update the rescue status in real time based on rescue feedback data.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] Through comprehensive and accurate ECG signal collection, the vest can monitor ECG data in real time and promptly notify the user through a buzzer when an abnormality is detected. The wristband active alarm reminds the user through vibration or sound. The wristband active alarm can also actively alarm when the user feels unwell, and send alarm information to the preset emergency contact through the wireless network. The wireless monitoring vest is made of soft and breathable elastic material, so that the user can wear it for a long time without feeling uncomfortable, which improves the user's wearing experience. The main control chip interacts with the cloud monitoring platform based on the wireless network, so that doctors or family members can remotely monitor the user's ECG condition and provide timely medical intervention suggestions. The backup power supply effectively ensures that the equipment can continue to work when the main power is exhausted, thereby protecting the user's life safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic diagram of the wireless monitoring vest of the present invention;

[0063] Figure 2 This is a diagram of the main control chip module of the present invention;

[0064] Figure 3 This is a module diagram of the cloud monitoring platform of the present invention.

[0065] In the figure: 1. Wireless monitoring vest; 2. Sensor module; 3. Main control chip; 4. Buzzer; 5. Active alarm; 6. Backup power supply. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0067] In order to solve the technical problems in the existing technology that the local storage space of the device is limited, a large amount of ECG data cannot be stored for a long time, and it cannot be transmitted to medical professionals or the user's guardian in real time based on remote analysis and management, which affects timely treatment, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions:

[0068] A wearable wireless transmission ECG monitoring and early warning vest comprises a wireless monitoring vest 1, in which a sensor module 2 is installed. The wireless monitoring vest 1 has ECG sensors sewn at key positions such as the chest and left abdomen of the vest to ensure that they correspond to the position of the heart and ensure that the signal acquisition is comprehensive and accurate. The wireless monitoring vest 1 is made of soft, breathable elastic material to ensure comfort for long-term wearing. The sensor module 2 is composed of a plurality of sewn ECG sensors, namely, electrode sheets, and is detachable, which is convenient for users to clean regularly. Keeping the electrode sheets clean can effectively improve the accuracy of data acquisition. The sensor module 2 is electrically connected to a main control chip 3, and one side of the main control chip 3 is electrically connected to a buzzer 4 and an active alarm 5. The active alarm 5 is worn on the user's wrist through a wristband. The main control chip 3 exchanges data with a cloud monitoring platform based on a wireless network. A backup power supply 6 is installed on one side of the wireless monitoring vest 1.

[0069] In this embodiment, the main control chip 3 includes:

[0070] The signal acquisition module is used to obtain the ECG monitoring data collected by the sensor module 2, pre-process the ECG monitoring data, and automatically adjust the gain according to the signal strength to ensure that the ECG signals of different patients or the same patient at different times can be optimally collected;

[0071] A signal amplification module, used to amplify the pre-processed ECG monitoring data to generate an analog signal of the ECG monitoring data;

[0072] The analog-to-digital conversion module is used to convert the amplified analog signal into a digital signal and perform data verification on the converted digital signal to ensure the integrity and accuracy of the data during the conversion process;

[0073] A wireless transmission module is used to transmit the verified complete digital signal to the cloud monitoring platform in real time based on a wireless network;

[0074] The power module is used to provide a stable power supply to the main control chip 3 based on the main power supply, and monitor the main power supply status data, and switch to the backup power supply 6 charging mode when the main power supply power is lower than a preset threshold;

[0075] The user information collection module is used to actively collect basic user information, such as name, age, gender, contact information, etc., and perform identity authentication to ensure the authenticity and uniqueness of the user's identity. At the same time, a unique account identifier is assigned to each user;

[0076] User information management module, which is used to review and amend user information, generate a user profile based on the user's basic information and unique account identifier, and allow the user to update and modify it, including changes in contact information, allergy history, etc.;

[0077] The user data binding module is used to bind the wireless monitoring vest 1 to the user's account through the identity authentication process before the user starts ECG monitoring.

[0078] In this embodiment, through comprehensive and accurate ECG signal collection, the vest can monitor ECG data in real time, and promptly notify the user through the buzzer 4 when an abnormality is detected. The wristband active alarm 5 reminds the user through vibration or sound. The wristband active alarm 5 can also actively alarm when the user feels unwell, and send alarm information to the preset emergency contact through the wireless network. The wireless monitoring vest 1 is made of soft and breathable elastic material, so that the user can wear it for a long time without feeling uncomfortable, thereby improving the user's wearing experience. The main control chip 3 interacts with the cloud monitoring platform based on the wireless network for data, so that doctors or family members can remotely monitor the user's ECG condition and provide timely medical intervention suggestions. The backup power supply 6 effectively ensures that the device can continue to work when the main power is exhausted, thereby ensuring the user's life safety.

[0079] In this embodiment, the cloud monitoring platform includes:

[0080] The data processing unit is used to perform redundant processing on the acquired complete digital signal to obtain the processed monitoring data, specifically:

[0081] The complete digital signal is divided into multiple data segments according to a fixed time window, each data segment contains 1 second of ECG data, and the sub-data in each data segment are subjected to transient timing verification, and the modal characteristic change value and sequence feedback coefficient of the sub-data are determined according to the verification results;

[0082] Transient timing verification includes the detection of ECG signal waveform, amplitude, frequency and other characteristics to ensure the integrity and accuracy of the data. The modal characteristic change value is determined by calculating the peak-to-peak value of the signal waveform, heart rate variability and other parameters within the data segment.

[0083] Determine the modal characteristic attenuation index of the sub-data according to the attenuation rate of the modal characteristic change value of the sub-data to the signal energy;

[0084] The target hash value of the sub-data is calculated according to the modal characteristic attenuation index and the sequence feedback coefficient of the sub-data, and the normal ECG data obtained based on the big data are statistically analyzed to determine the preset hash value;

[0085] Confirm whether the target hash value of each sub-data is greater than or equal to the preset hash value. If so, confirm that the sub-data is not redundant data. If not, confirm that the sub-data is redundant data.

[0086] Extract redundant data from the complete digital signal, determine the specific location and attributes of the redundant data, and eliminate the redundant data to obtain the processed monitoring data;

[0087] Specifically, the time length setting of the time window is obtained through the following process, including:

[0088] Set the heart rate collection time period;

[0089] Collect the user's heart rate data in each unit time during the heart rate collection time period, wherein the value of the unit time is 1 minute;

[0090] Retrieve the reference time window length stored in the database;

[0091] Obtaining a heart rate characterization coefficient using the user's heart rate data per unit time;

[0092] The heart rate characterization coefficient is obtained by the following formula:

[0093]

[0094] Where S represents the heart rate characterization coefficient; T represents the length of the reference time window; t represents the length of the unit time; n represents the number of unit times experienced by the heart rate data collection, and nt is not less than 5T; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR max and HR min represents the maximum heart rate value and the minimum heart rate value corresponding to n unit time; m represents the number of unit time intervals between the maximum heart rate value and the minimum heart rate value; λ represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0095]

[0096] Among them, λ represents the adjustment coefficient; n represents the number of unit time experienced by heart rate data collection; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR z represents the median heart rate corresponding to n unit time; k i represents the number of unit times between the heart rate value of the i-th unit time and the unit time to which the middle value of the heart rate belongs; t represents the duration of the unit time;

[0097] Comparing the heart rate characterization coefficient with a preset heart rate characterization coefficient threshold;

[0098] The time length corresponding to the fixed time window is determined according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold.

[0099] The technical effect of the above technical solution is: by collecting the user's heart rate data for each unit time (1 minute) within a specific time period (heart rate collection time period), and combining a series of calculations and analyses, the technical solution can set a personalized time window length for the user. This personalized setting method is more flexible and accurate than a fixed time window, and can better adapt to the physiological characteristics and heart rate change patterns of different users. By calculating the heart rate characterization coefficient S, the technical solution can comprehensively evaluate the user's heart rate changes within the heart rate collection time period. This coefficient not only takes into account the fluctuation range of the heart rate value (HR max and HR min ), and also considers the heart rate value and the reference value (HR e ) and median heart rate (HR z ) and how these deviations change with time (k i and t). Therefore, the heart rate characterization coefficient can reflect the user's heart rate characteristics more comprehensively and accurately. The introduction of the adjustment coefficient λ makes the setting of the time window length more flexible. The calculation of λ takes into account the deviation between the heart rate value and the heart rate median value and the change of these deviations over time, so the length of the time window can be dynamically adjusted according to the user's heart rate changes. This dynamic adjustment mechanism helps to improve the accuracy and adaptability of the time window setting. By comparing the heart rate characterization coefficient S with the preset heart rate characterization coefficient threshold, and determining the time length corresponding to the fixed time window based on the quantitative relationship between the two, the technical solution can ensure that the length of the time window is consistent with the user's physiological characteristics and can meet the needs of practical applications. This optimized time window length helps to improve the accuracy and efficiency of heart rate monitoring.

[0100] In summary, this technical solution realizes personalized, dynamic and accurate time window length setting by introducing parameters such as heart rate characterization coefficient and adjustment coefficient, which helps to improve the accuracy and efficiency of heart rate monitoring and provide users with more accurate health management services.

[0101] Specifically, determining the time length corresponding to the fixed time window according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold includes:

[0102] When the heart rate characterization coefficient is lower than a preset heart rate characterization coefficient threshold, maintaining the time length of the time window as a reference time window length, that is, taking the reference time window length as a fixed time window;

[0103] When the heart rate characterization coefficient exceeds a preset heart rate characterization coefficient threshold, the heart rate characterization coefficient is retrieved;

[0104] Using the heart rate characterization coefficient in combination with the reference time window length, obtaining the time length corresponding to the fixed time window;

[0105] The time length corresponding to the fixed time window is obtained by the following formula:

[0106]

[0107] Among them, T g represents the time length corresponding to the fixed time window; S represents the heart rate characterization coefficient; S y represents the preset heart rate characterization coefficient threshold; n represents the number of unit time experienced by heart rate data collection; k i It represents the number of unit times between the heart rate value of the ith unit time and the unit time to which the middle heart rate value belongs; m represents the number of unit times between the maximum heart rate value and the minimum heart rate value; T represents the length of the reference time window.

[0108] The technical effect of the above technical solution is: the technical solution is based on the heart rate characterization coefficient (S) and the preset heart rate characterization coefficient threshold (S y ) and dynamically adjust the time length (T g ). This dynamic adjustment mechanism enables the length of the time window to more accurately reflect the user's heart rate changes, thereby improving the accuracy and efficiency of heart rate monitoring. By introducing the heart rate characterization coefficient, this technical solution can comprehensively consider the user's heart rate changes during the heart rate collection period, including the fluctuation range of the heart rate value, the deviation between the heart rate value and the reference value and the middle heart rate value. This personalized time window setting method is more flexible and accurate than a fixed time window, and can better adapt to the physiological characteristics and heart rate change patterns of different users.

[0109] When calculating the time length corresponding to a fixed time window, the technical solution not only considers the heart rate characterization coefficient and the preset heart rate characterization coefficient threshold, but also combines the number of unit times experienced by the heart rate data collection (n), the number of unit times between the heart rate value and the unit time to which the heart rate intermediate value belongs (k i ) and the number of unit times (m) between the maximum heart rate value and the minimum heart rate value. This calculation method that takes multiple factors into consideration makes the determination of the time window length more scientific and reasonable. By dynamically adjusting the time window length and personalizing the time window, this technical solution can more accurately capture the user's heart rate changes, thereby improving the accuracy and reliability of heart rate monitoring. This is of great significance for preventing cardiovascular diseases, evaluating exercise effects, and providing personalized health management recommendations.

[0110] In summary, this technical solution improves the accuracy and efficiency of heart rate monitoring by dynamically adjusting the time window length, personalizing the time window, and optimizing the calculation of the time window length, providing users with more accurate health management services.

[0111] The data processing unit further comprises:

[0112] Data analysis module, which is used to analyze the long-term and short-term trends in the processed monitoring data, help users and doctors understand the changes in ECG activity, provide data support for chronic disease management, identify potential ECG activity patterns in the processed monitoring data, help detect heart problems such as arrhythmia, provide a basis for early diagnosis, and present the analysis results to users in charts, reports and other visual forms;

[0113] It also includes statistical analysis of heart rate variability, detection and classification of QRS complexes, and extraction of ECG parameters that are helpful for diagnosis, such as the time interval and amplitude of P waves, QRS complexes, and T waves;

[0114] A feature extraction module is used to extract ECG signals from the processed monitoring data, determine key features of ECG signals, such as the peak value of the R wave, the RR interval, heart rate variability HRV, etc., realize waveform recognition and classification, distinguish normal and abnormal ECG activities, provide data support for subsequent early warning and diagnosis, extract frequency domain features of ECG signals based on the key features, and identify weak changes and potential risks in ECG signals based on the frequency domain features;

[0115] The abnormality monitoring unit is used to identify abnormalities in the monitoring data output by the data processing unit, monitor abnormal waveforms and abnormal parameter change data, and determine the abnormality type and abnormality level according to the abnormality monitoring results, such as slight abnormality, severe abnormality, etc.;

[0116] The anomaly monitoring unit is also used to compare the current data with the user's historical data, analyze the abnormal change trend, and at the same time, obtain the user's real-time positioning data when an abnormality is detected;

[0117] The early warning unit is used to match the corresponding early warning instruction from the preset early warning strategy library according to the abnormal type and abnormal level, and send the early warning instruction to the main control chip 3 based on the wireless network. The main control chip 3 controls the buzzer 4 to sound and light alarm according to the received early warning instruction;

[0118] At the same time, for serious anomalies that exceed the preset risk level, the direct alarm process will be automatically triggered, sending a distress signal to medical institutions or emergency rescue centers, and providing users with real-time positioning data.

[0119] In this embodiment, the waveform, amplitude, frequency and other key features of the ECG signal are carefully detected through transient timing verification, which ensures the integrity and accuracy of the data, reduces data noise and errors, monitors abnormal waveforms and parameter changes in the ECG data in real time, quickly identifies and determines the type and level of abnormalities, discovers potential health problems early, and buys precious treatment time for patients. According to the type and level of abnormalities, personalized early warning responses are implemented, which improves the pertinence and effectiveness of early warnings, reduces false alarms and missed alarms, and can automatically trigger a direct alarm process for severe abnormalities that exceed the preset risk level, so that rescue forces can be quickly mobilized to buy life-saving time for patients.

[0120] In this embodiment, the abnormality monitoring unit includes:

[0121] An abnormality identification module is used to identify abnormal waveforms and parameter change data of the ECG activity pattern of the monitoring data that are inconsistent with the normal pattern based on the identified ECG activity pattern and feature extraction results, determine the abnormal type of the monitoring data according to the abnormal characteristics of the abnormal waveform and parameter change data, such as hardware failure, software error, data abnormality, etc., and determine a preset abnormal threshold based on the classification result;

[0122] The risk assessment module is used to compare the output of the anomaly identification module with the preset anomaly threshold, assess the anomaly level of the monitoring data, analyze the changing trend of the abnormal monitoring data in combination with the user's historical data, predict possible future impacts, and match the corresponding emergency response plan according to the risk assessment results;

[0123] The positioning data processing module is used to automatically trigger the data instruction of collecting the user's real-time positioning when an anomaly is detected, associate the monitoring data with the abnormality with the positioning data, identify the specific location and scene of the anomaly, and generate a detailed report containing information such as the anomaly type, level, and location based on the analysis results.

[0124] In this embodiment, the abnormal waveforms and parameter changes in the monitoring data are accurately identified through the abnormal identification module, and the abnormal identification results are compared with the preset abnormal thresholds, so as to further improve the accuracy and reliability of abnormal identification, and objectively assess the abnormal level of the monitoring data. The evaluation process combines the user's historical data, analyzes the changing trend of the abnormal data, and predicts the possible future impact, which helps to take timely measures before the problem expands. The abnormal data is associated with the real-time positioning data, so that the specific location and scene of the abnormality can be clearly presented, which provides strong support for the on-site handling of the problem. The generated detailed report provides comprehensive and accurate information for subsequent review analysis, accountability and process improvement. The efficient emergency response mechanism greatly shortens the time from problem discovery to problem handling, and reduces the threat of potential risks to user health.

[0125] In this embodiment, the early warning unit includes:

[0126] The warning instruction generation module is used to match the corresponding warning instructions according to the abnormal type and abnormal level assessment results of the monitoring data, and formulate warning strategies based on the user's personal information and ECG monitoring history records to ensure the accuracy and timeliness of the warning instructions;

[0127] In this embodiment, the early warning strategy specifically includes:

[0128] Fixed threshold warning: Set a fixed value as the warning limit, and trigger a warning when the monitored value exceeds this limit;

[0129] Dynamic threshold warning: Dynamically adjust the warning threshold based on historical data or real-time data to adapt to changes in the environment or conditions;

[0130] Rising trend warning: When the monitoring data shows an upward trend for several consecutive cycles, the warning is triggered;

[0131] Downward trend warning: When the monitoring data shows a downward trend for several consecutive cycles, the warning is triggered;

[0132] Abnormal behavior pattern warning: Analyze the user's behavior pattern and trigger a warning when the behavior deviates from the normal pattern;

[0133] Mean warning: When the monitored value deviates from the mean value by more than a certain standard deviation, a warning is triggered;

[0134] Variance warning: When the variance of the monitoring data exceeds the preset threshold, an alert is triggered;

[0135] Multi-parameter joint warning: Combine multiple monitoring parameters and trigger an early warning when the comprehensive indicators of multiple monitoring parameters exceed the preset threshold;

[0136] Multi-level warning: Set multiple warning levels and gradually upgrade warning measures according to the severity of the anomaly;

[0137] Seasonal change warning: Considering the time series characteristics of the data, such as seasonal fluctuations, triggering warnings when the monitored values ​​deviate from seasonal expectations;

[0138] Periodic change warning: For data with clear periodicity, an alarm is triggered when the monitored value changes abnormally within the period;

[0139] The early warning notification module is used to send the generated early warning instruction to the main control chip 3 through the wireless network, and trigger the buzzer 4 to sound and light alarm to remind the user to pay attention, send text messages or application push notifications to the user's preset contacts through the mobile network to inform the abnormal situation and necessary response measures, receive the user's confirmation feedback on the early warning notification, and determine the receipt and processing of the early warning information;

[0140] The rescue dispatch module is used to conduct risk assessment on serious anomalies that exceed the preset risk level, and decide whether to trigger the direct alarm process based on the assessment results, send a distress signal to nearby medical institutions or emergency rescue centers, and provide users with real-time positioning data so that rescue personnel can quickly and accurately find the patient's location, maintain communication with the rescue agency, and update the rescue status in real time based on the rescue feedback data to ensure the smooth progress of the rescue process.

[0141] In this embodiment, through timely and effective early warning and rescue dispatch, the user's safety when encountering abnormal ECG conditions is significantly improved. Personalized early warning strategies and instant notifications enhance the user experience, making health management more considerate and convenient. The automated risk assessment and rescue dispatch process greatly improves the rescue efficiency and success rate, providing strong protection for the user's life safety.

[0142] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A wearable wireless transmission ECG monitoring and early warning vest, comprising a wireless monitoring vest (1), characterized in that: A sensor module (2) is installed in the wireless monitoring vest (1), the sensor module (2) is electrically connected to a main control chip (3), one side of the main control chip (3) is electrically connected to a buzzer (4) and an active alarm (5), the main control chip (3) performs data exchange with a cloud monitoring platform based on a wireless network, and a backup power supply (6) is installed on one side of the wireless monitoring vest (1).

2. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 1, characterized in that: Main control chip (3), including: A signal acquisition module, used to acquire the electrocardiogram monitoring data collected by the sensor module (2) and pre-process the electrocardiogram monitoring data; A signal amplification module, used to amplify the pre-processed ECG monitoring data to generate an analog signal of the ECG monitoring data; The analog-to-digital conversion module is used to convert the amplified analog signal into a digital signal and perform data verification on the converted digital signal; A wireless transmission module is used to transmit the verified complete digital signal to the cloud monitoring platform in real time based on a wireless network; The power supply module is used to provide a stable power supply for the main control chip (3) based on the main power supply, and monitor the main power supply status data, and switch to the backup power supply (6) charging mode when the main power supply power is lower than a preset threshold.

3. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 2, characterized in that: The main control chip (3) also includes: The user information collection module is used to actively collect basic user information and perform identity authentication. At the same time, a unique account identifier is assigned to each user; User information management module, used to review and modify user information and generate the user's personal profile based on the user's basic information and unique account identifier; The user data binding module is used to bind the wireless monitoring vest (1) to the user's account through identity authentication before the user starts electrocardiogram monitoring.

4. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 3, characterized in that: Cloud monitoring platform, including: A data processing unit, used for performing redundant processing on the acquired complete digital signal to obtain processed monitoring data; The abnormality monitoring unit is used to identify abnormalities in the monitoring data output by the data processing unit, monitor abnormal waveforms and abnormal parameter change data, and determine the abnormality type and abnormality level according to the abnormality monitoring results; The anomaly monitoring unit is also used to compare the current data with the user's historical data, analyze the abnormal change trend, and at the same time, obtain the user's real-time positioning data when an abnormality is detected; An early warning unit, used for matching corresponding early warning instructions from a preset early warning strategy library according to the abnormality type and abnormality level, and sending the early warning instructions to the main control chip (3) based on a wireless network; At the same time, for serious anomalies that exceed the preset risk level, the direct alarm process will be automatically triggered, sending a distress signal to medical institutions or emergency rescue centers, and providing users with real-time positioning data.

5. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 4, characterized in that: The data processing unit performs redundancy processing, specifically: The complete digital signal is divided into multiple data segments according to a fixed time window, and the transient timing verification is performed on the sub-data in each data segment, and the modal characteristic change value and sequence feedback coefficient of the sub-data are determined according to the verification result; Determine the modal characteristic attenuation index of the sub-data according to the attenuation rate of the modal characteristic change value of the sub-data to the signal energy; The target hash value of the sub-data is calculated according to the modal characteristic attenuation index and sequence feedback coefficient of the sub-data, and the normal ECG data obtained based on the big data are statistically analyzed to determine the preset hash value; Confirm whether the target hash value of each sub-data is greater than or equal to the preset hash value. If so, confirm that the sub-data is not redundant data. If not, confirm that the sub-data is redundant data. Extract redundant data from the complete digital signal, determine the specific location and attributes of the redundant data, and eliminate the redundancy to obtain the processed monitoring data.

6. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 5, characterized in that: The time length setting of the time window is obtained through the following process, including: Set the heart rate collection time period; Collect the user's heart rate data in each unit time during the heart rate collection time period, wherein the value of the unit time is 1 minute; Retrieve the reference time window length stored in the database; Obtaining a heart rate characterization coefficient using the user's heart rate data per unit time; The heart rate characterization coefficient is obtained by the following formula: Where S represents the heart rate characterization coefficient; T represents the length of the reference time window; t represents the length of the unit time; n represents the number of unit times experienced by the heart rate data collection, and nt is not less than 5T; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR max and HR min represents the maximum heart rate value and the minimum heart rate value corresponding to n unit time; m represents the number of unit time intervals between the maximum heart rate value and the minimum heart rate value; λ represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Among them, λ represents the adjustment coefficient; n represents the number of unit time experienced by heart rate data collection; HR i Represents the heart rate value of the i-th unit time; HR e Indicates the preset heart rate reference value; HR z represents the median heart rate corresponding to n unit time; k i represents the number of unit times between the heart rate value of the i-th unit time and the unit time to which the middle value of the heart rate belongs; t represents the duration of the unit time; Comparing the heart rate characterization coefficient with a preset heart rate characterization coefficient threshold; The time length corresponding to the fixed time window is determined according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold.

7. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 6, characterized in that: Determining the time length corresponding to the fixed time window according to the quantitative relationship between the heart rate characterization coefficient and a preset heart rate characterization coefficient threshold includes: When the heart rate characterization coefficient is lower than a preset heart rate characterization coefficient threshold, maintaining the time length of the time window as a reference time window length, that is, taking the reference time window length as a fixed time window; When the heart rate characterization coefficient exceeds a preset heart rate characterization coefficient threshold, the heart rate characterization coefficient is retrieved; Using the heart rate characterization coefficient in combination with the reference time window length, obtaining the time length corresponding to the fixed time window; The time length corresponding to the fixed time window is obtained by the following formula: Among them, T g represents the time length corresponding to the fixed time window; S represents the heart rate characterization coefficient; S y represents the preset heart rate characterization coefficient threshold; n represents the number of unit time experienced by heart rate data collection; k i It represents the number of unit times between the heart rate value of the ith unit time and the unit time to which the middle heart rate value belongs; m represents the number of unit times between the maximum heart rate value and the minimum heart rate value; T represents the length of the reference time window.

8. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 5, characterized in that: The data processing unit further includes: A data analysis module, which is used to analyze the long-term and short-term trends in the processed monitoring data, identify the potential ECG activity patterns in the processed monitoring data, and present the analysis results to the user in a visual form; The feature extraction module is used to extract the ECG signal from the processed monitoring data, determine the key features of the ECG signal, and extract the frequency domain features of the ECG signal based on the key features.

9. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 8, characterized in that: Abnormal monitoring unit, including: An abnormality identification module is used to identify abnormal waveforms and parameter change data of the ECG activity pattern of the monitoring data that are inconsistent with the normal pattern based on the identified ECG activity pattern and feature extraction results, determine the abnormal type of the monitoring data according to the abnormal characteristics of the abnormal waveform and parameter change data, and determine a preset abnormal threshold based on the classification result; The risk assessment module is used to compare the output of the anomaly identification module with the preset anomaly threshold, assess the anomaly level of the monitoring data, analyze the changing trend of the abnormal monitoring data in combination with the user's historical data, predict possible future impacts, and match the corresponding emergency response plan according to the risk assessment results; The positioning data processing module is used to automatically trigger the data instruction of collecting the user's real-time positioning when an anomaly is detected, associate the monitoring data with the abnormality with the positioning data, and identify the specific location and scene where the anomaly occurs.

10. The wearable wireless transmission ECG monitoring and early warning vest as claimed in claim 9, characterized in that: Early warning unit, including: The warning instruction generation module is used to match the corresponding warning instructions according to the abnormal type and abnormal level assessment results of the monitoring data, and formulate a warning strategy based on the user's personal information and ECG monitoring history records; The early warning notification module is used to send the generated early warning instruction to the main control chip (3) via a wireless network, trigger a buzzer (4) to sound and light alarm, receive confirmation feedback from the user on the early warning notification, and determine the receipt and processing of the early warning information; The rescue dispatch module is used to conduct risk assessment on serious anomalies that exceed the preset risk level, and decide whether to trigger the direct alarm process based on the assessment results, provide users with real-time positioning data, and update the rescue status in real time based on rescue feedback data.

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