Novel wearable intelligent electrocardio garment

Abstract

The invention relates to a novel wearable intelligent electrocardio garment which comprises an electrocardio waistcoat body, a user side and a monitoring cloud platform, one side of the electrocardio waistcoat body is fixedly connected with an output end of an electrocardio detector, the output end is in wireless data connection with the user side, and the user side is in wireless data connection with the monitoring cloud platform. The electrocardiogram data of the user can be monitored in real time, the abnormal condition of the heart can be captured in time, timely health guarantee is provided for the user, meanwhile, the electrocardiogram classification algorithm integrated with artificial intelligence deep learning and manual real-time analysis are adopted, the diagnosis accuracy and consistency of the electrocardiogram data are doubly guaranteed, the diagnosis accuracy and reliability are improved, and the user experience is improved. Through the wireless data transmission technology, the user side and the monitoring cloud platform can receive and store electrocardiogram data in real time and conduct remote processing and analysis, and doctors and professionals can conveniently conduct remote monitoring and management on the health condition of the user.

Description

Technical Field

[0001] The invention relates to the technical field of medical detection, and in particular to a novel wearable intelligent electrocardiograph clothing. Background Art

[0002] With the accelerated pace of life and the arrival of an aging society, the incidence of atrial fibrillation, one of the chronic cardiovascular diseases, is still increasing year by year. The high disability and mortality rates of complications such as stroke and heart failure caused by it have caused serious public health problems worldwide. The "Atrial Fibrillation Early Diagnosis and Treatment Innovation Team" is an innovative team project proposed to apply for construction based on the current reality that the concept of early diagnosis and treatment of atrial fibrillation in my country has not yet been implemented, the early diagnosis rate of atrial fibrillation is generally low, the early rhythm control is still not ideal, and the popularization rate of new technologies is not enough.

[0003] The project advocates the concept of early diagnosis and treatment of atrial fibrillation, in order to improve the team's early diagnosis and treatment of atrial fibrillation patients and improve the clinical prognosis of atrial fibrillation patients. The technological innovation and upgrading of atrial fibrillation diagnostic tools is an important part of achieving the improvement of early diagnosis and treatment of atrial fibrillation.

[0004] Therefore, there is an urgent need for an intelligent wearable ECG device that has an anti-vest wearable mode, adjustable electrode positioning, and supports dual guarantees of consistency assessment and analysis of ECG data platforms of artificial intelligence platform + manual platform - an anti-vest smart ECG clothing, so as to improve the accuracy and detection rate of early diagnosis of atrial fibrillation.

[0005] In addition, the device explores the establishment of an integrated technical diagnosis and treatment model for early diagnosis and early treatment of atrial fibrillation, that is, after early diagnosis of atrial fibrillation, suitable cases with non-valvular atrial fibrillation or atrial fibrillation combined with heart failure within 1 year of disease course are given priority to undergo transcatheter radiofrequency ablation for rhythm control, and / or percutaneous left atrial appendage occlusion, establish an integrated diagnosis and treatment training system for early diagnosis and early treatment of atrial fibrillation and optimize the implementation of the diagnosis and treatment plan, in order to improve the sinus rhythm maintenance rate after ablation and reduce the risk of paroxysmal atrial fibrillation developing into persistent atrial fibrillation, and reduce the incidence of adverse outcomes such as stroke and heart failure. Although the traditional electrocardiogram detection method is accurate, it requires professional equipment and personnel to operate, and cannot be monitored in real time and continuously. Therefore, it is of great significance to develop a wearable, remote electrocardiogram monitoring device. Summary of the invention

[0006] In order to solve the problems of the prior art, the present invention provides a novel wearable intelligent electrocardiograph clothing.

[0007] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: a new type of wearable smart ECG clothing, including an ECG vest body, a user end and a monitoring cloud platform, one side of the ECG vest body is fixedly connected to the output end of the ECG detector and is wirelessly connected to the user end, the user end is wirelessly connected to the monitoring cloud platform, the ECG detector is used to detect ECG data in real time, the user end is used to process and analyze the ECG data received from the ECG detector, and transmit the ECG data to the monitoring cloud platform, the monitoring cloud platform is used to use an integrated artificial intelligence deep learning algorithm plus manual real-time analysis to double guarantee the diagnostic accuracy and consistency of the ECG data;

[0008] The main body of the ECG vest consists of an inner layer and an outer layer, wherein the inner layer and the outer layer are provided with fixing grooves inside, an adjustment groove is provided on one side of the inner layer, the adjustment groove and the fixing groove are interconnected, a fixing plate is fixedly connected to one side of the inner wall of the outer layer, an electrode plate detection head is fixedly connected to one side of the fixing plate, and the fixing plate and the first Velcro are adhered to each other.

[0009] Preferably, a fixing belt is fixedly connected to one side of the ECG vest body, and a second Velcro is fixedly connected to the other side of the ECG vest body, and the fixing belt and the second Velcro are adhered to each other.

[0010] Preferably, a plurality of adjustment grooves are provided on the inner side of the inner layer, and the input end of the electrocardiograph detector is electrically connected to a data line, and one end of the data line is connected to an electrode sheet detection head.

[0011] Preferably, the ECG detector includes an electrode detection head, a preprocessing circuit board, a wireless data transmitter and a battery. The battery is used to power the preprocessing circuit board, the wireless data transmitter and the battery. The number of the electrode detection heads is set to ten for real-time detection of ECG data.

[0012] Preferably, the monitoring cloud platform internally includes an ECG classification algorithm module, a manual analysis module and an evaluation and analysis module. The ECG classification algorithm module is used to process and analyze the ECG data using an artificial intelligence deep learning algorithm. The manual analysis module is used for staff to manually process and analyze the ECG data. The evaluation and analysis module is used to summarize and compare the analysis data of the ECG classification algorithm module and the manual analysis module to double-confirm that the diagnosis results are consistent.

[0013] The beneficial effects of the present invention are:

[0014] 1. The present invention is composed of an inner layer and an outer layer. The inner layer is provided with a fixing groove and an adjustment groove, which can realize automatic adjustment, so that the electrode sheet can better fit the user's body curve and improve comfort. At the same time, a fixing sheet is fixedly connected to one side of the inner wall of the outer layer, and is adhered to the first Velcro, which is convenient for users to put on and take off and adjust, further improving the precise adaptation for different people, improving the applicability of the equipment, and further improving the accuracy of detection. The design of the smart electrocardiograph is suitable for most adults of different body shapes to wear, and can be popularized to the general public, improving the early detection and diagnosis and treatment of heart diseases;

[0015] 2. The present invention can monitor the user's ECG data in real time, promptly capture cardiac abnormalities, and provide timely health protection for the user. At the same time, by adopting an ECG classification algorithm that integrates artificial intelligence deep learning and manual real-time analysis, it can doubly guarantee the diagnostic accuracy and consistency of the ECG data, and improve the accuracy and reliability of the diagnosis. Through wireless data transmission technology, the user end and the monitoring cloud platform can receive and store ECG data in real time, and perform remote processing and analysis, which is convenient for doctors and professionals to remotely monitor and manage the user's health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall back structure of the present invention.

[0017] Figure 2 It is a schematic diagram of the overall cross-sectional structure of the present invention.

[0018] Figure 3 It is a schematic diagram of the overall front structure of the present invention.

[0019] Figure 4 It is a schematic diagram of the connection structure of the electrocardiograph detector of the present invention.

[0020] Figure 5 It is a schematic diagram of the installation structure of the electrode sheet detection head of the present invention.

[0021] Figure 6 It is a connection diagram of the ECG vest body, user terminal and monitoring cloud platform of the present invention.

[0022] Figure 7 It is a schematic diagram of the overall system connection structure of the present invention.

[0023] Figures 1 to 7In: 1. ECG vest body; 2. Inner layer; 3. ECG detector; 4. Data cable; 5. Electrode probe head; 6. Fixing slot; 7. First Velcro; 8. Outer layer; 9. Fixing plate; 10. Adjustment slot; 11. Fixing belt; 12. Second Velcro; 13. User end; 14. Monitoring cloud platform; 15. Wireless data transmitter; 16. Pre-processing circuit board; 17. ECG classification algorithm module; 18. Manual analysis module; 19. Evaluation and analysis module; 20. Battery. DETAILED DESCRIPTION

[0024] like Figures 1 to 7 A new type of wearable smart ECG clothing shown in the figure includes an ECG vest body 1, a user terminal 13 and a monitoring cloud platform 14. One side of the ECG vest body 1 is fixedly connected to the output end of the ECG detector 3 and is wirelessly connected to the user terminal 13. The user terminal 13 is wirelessly connected to the monitoring cloud platform 14. The ECG detector 3 is used to detect ECG data in real time. The user terminal 13 is used to process and analyze the ECG data received from the ECG detector 3 and transmit the ECG data to the monitoring cloud platform 14. The monitoring cloud platform 14 is used to use an integrated artificial intelligence deep learning algorithm plus manual real-time analysis to double guarantee the diagnostic accuracy and consistency of the ECG data.

[0025] The ECG vest body 1 is composed of an inner layer 2 and an outer layer 8. The inner layers 2 and the outer layers 8 are provided with fixing grooves 6. One side of the inner layer 2 is provided with an adjustment groove 10. The adjustment groove 10 and the fixing groove 6 are interconnected. A fixing plate 9 is fixedly connected to one side of the inner wall of the outer layer 8. An electrode probe head 5 is fixedly connected to one side of the fixing plate 9. The fixing plate 9 and the first Velcro 7 are adhered to each other. When in use, according to the physique of the wearer, it is necessary to fine-tune the positions of the ten electrode probe heads 5 so that the electrode probe heads 5 can be accurately detected for different patients. At this time, the fixing plate 9 is moved inside the adjustment groove 10. After the position is accurate, the fixing plate 9 is adhered and fixed to the first Velcro 7, and then the positions of the multiple electrode probe heads 5 are adjusted. After that, the wearer wears it in reverse. The staff adheres and fixes the fixing belt 11 and the second Velcro 12. According to the physique of the wearer, the length of the adhesion between the fixing belt 11 and the second Velcro 12 is adjusted, so that the device can be adapted to people with different physiques.

[0026] After wearing, ten electrode probes 5 perform real-time detection on the wearer. After passing through the ECG detector 3, the data is transmitted to the user terminal 13 via the wireless data transmitter 15 for processing. At the same time, the user terminal 13 transmits the ECG data remotely to the remote monitoring center of the detection cloud platform through 5G communication technology. The integrated artificial intelligence deep learning algorithm plus manual real-time analysis is used to double guarantee the diagnostic accuracy and consistency of the ECG data.

[0027] This will facilitate the establishment of a new diagnosis and treatment model that integrates the technology of early diagnosis and early treatment of atrial fibrillation, including the establishment of an integrated diagnosis and treatment training system for early diagnosis and early treatment of atrial fibrillation, the formulation of theoretical lectures and surgical demonstration training courses and technical guidance practices; the optimization and implementation of the diagnosis and treatment plan for early diagnosis and early treatment of atrial fibrillation, and the formulation of project evaluation and assessment plans.

[0028] A fixing belt 11 is fixedly connected to one side of the ECG vest body 1, and a second Velcro 12 is fixedly connected to the other side of the ECG vest body 1. The fixing belt 11 and the second Velcro 12 are adhered to each other. A plurality of adjustment grooves 10 are provided on the inner side of the inner layer 2. The input end of the ECG detector 3 is electrically connected to a data line 4, and one end of the data line 4 is connected to an electrode detection head 5.

[0029] The ECG detector 3 includes an electrode probe head 5, a preprocessing circuit board 16, a wireless data transmitter 15 and a battery 20. The battery 20 is used to power the preprocessing circuit board 16, the wireless data transmitter 15 and the battery 20. The number of electrode probe heads 5 is set to ten, which are used to detect ECG data in real time. The monitoring cloud platform 14 includes an ECG classification algorithm module 17, a manual analysis module 18 and an evaluation analysis module 19. The ECG classification algorithm module 17 is used to process and analyze the ECG data using an artificial intelligence deep learning algorithm. The manual analysis module 18 is used for staff to manually process and analyze the ECG data. The evaluation analysis module 19 is used to summarize and compare the analysis data of the ECG classification algorithm module 17 and the manual analysis module 18 to double-confirm that the diagnosis results are consistent.

[0030] In summary, the present invention has the following working principle: when in use, according to the physique of the wearer, the position of the ten electrode sheet detection heads 5 needs to be fine-tuned, so that the electrode sheet detection heads 5 can accurately detect different patients. At this time, the fixing sheet 9 is moved inside the adjustment slot 10. After the position is accurate, the fixing sheet 9 is adhered and fixed to the first Velcro 7, and then the position of multiple electrode sheet detection heads 5 is adjusted. After that, the wearer wears it in reverse, and the staff adheres and fixes the fixing belt 11 to the second Velcro 12. According to the physique of the wearer, the length of the adhesion between the fixing belt 11 and the second Velcro 12 is adjusted, so that the device is suitable for people with different physiques.

[0031] After being put on, ten electrode detection heads 5 perform real-time detection on the wearer. After passing through the ECG detector 3, the data is transmitted to the user terminal 13 via the wireless data transmitter 15 for processing. At the same time, the user terminal 13 remotely transmits the ECG data to the remote monitoring center of the detection cloud platform through 5G communication technology, and adopts integrated artificial intelligence deep learning algorithm plus manual real-time analysis to doubly guarantee the diagnostic accuracy and consistency of the ECG data, thus completing the overall workflow of the device.

Claims

1. A novel wearable smart electrocardiograph vest, comprising an electrocardiograph vest body (11), a user terminal (13) and a monitoring cloud platform (14), characterized in that: One side of the electrocardiogram vest body (11) is fixedly connected to the output end of the electrocardiogram detector (3) and is wirelessly connected to the user end (13). The user end (13) is wirelessly connected to the monitoring cloud platform (14). The electrocardiogram detector (3) is used to detect electrocardiogram data in real time. The user end (13) is used to process and analyze the electrocardiogram data received from the electrocardiogram detector (3) and transmit the electrocardiogram data to the monitoring cloud platform (14). The monitoring cloud platform (14) is used to use an integrated artificial intelligence deep learning algorithm plus manual real-time analysis to double-guarantee the diagnostic accuracy and consistency of the electrocardiogram data. The electrocardiogram vest body (11) is composed of an inner layer (2) and an outer layer (8), wherein the inner layer (2) and the outer layer (8) are provided with fixing grooves (6), one side of the inner layer (2) is provided with an adjustment groove (10), the adjustment groove (10) and the fixing groove (6) are interconnected, one side of the inner wall of the outer layer (8) is fixedly connected with a fixing plate (9), one side of the fixing plate (9) is fixedly connected with an electrode plate detection head (5), and the fixing plate (9) and the first Velcro (7) are adhered to each other.

2. The novel wearable smart electrocardiograph clothing according to claim 1 is characterized in that: A fixing belt (11) is fixedly connected to one side of the ECG vest body (1), and a second Velcro (12) is fixedly connected to the other side of the ECG vest body (1), and the fixing belt (11) and the second Velcro (12) are adhered to each other.

3. The novel wearable smart electrocardiograph clothing according to claim 1 is characterized in that: A plurality of adjustment grooves (10) are provided on the inner side of the inner layer (2); the input end of the electrocardiogram detector (3) is electrically connected to a data line (4); one end of the data line (4) is connected to an electrode sheet detection head (5).

4. The novel wearable smart electrocardiograph clothing according to claim 3 is characterized in that: The electrocardiogram detector (3) comprises an electrode sheet detection head (5), a preprocessing circuit board (16), a wireless data transmitter (15) and a storage battery (20). The storage battery (20) is used to supply power to the preprocessing circuit board (16), the wireless data transmitter (15) and the storage battery (20). The number of the electrode sheet detection heads (5) is set to ten, and is used to detect electrocardiogram data in real time.

5. The novel wearable smart electrocardiograph clothing according to claim 1 is characterized in that: The monitoring cloud platform (14) includes an ECG classification algorithm module (17), a manual analysis module (18) and an evaluation analysis module (19). The ECG classification algorithm module (17) is used to process and analyze ECG data using an artificial intelligence deep learning algorithm. The manual analysis module (18) is used for staff to manually process and analyze ECG data. The evaluation analysis module (19) is used to summarize and compare the analysis data of the ECG classification algorithm module (17) and the manual analysis module (18) to double-confirm that the diagnosis results are consistent.

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