Multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases

Abstract

The invention provides a multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases, which belongs to the technical field of monitoring equipment and comprises a main control unit, a multi-modal physiological parameter acquisition unit, a data processing unit, a communication unit, a power supply unit, a storage unit and an alarm unit. The multi-modal physiological parameter acquisition unit is integrated with an electrocardio acquisition module, a blood pressure acquisition module, a blood oxygen saturation acquisition module and a pulse wave acquisition module, acquires various core parameters and transmits the core parameters to the data processing unit; the data processing unit extracts fusion feature parameters through adaptive Kalman filtering noise reduction and a deep learning algorithm, the fusion feature parameters are processed by the main control unit and then transmitted to an external terminal by the communication unit, and the alarm unit gives out sound and light alarms when the parameters are abnormal. The device adopts a wearable flexible design and adapts to multiple parts of a human body. According to the invention, comprehensive and accurate acquisition and processing of multiple parameters are realized, monitoring convenience, data security and abnormity early warning timeliness are improved, and daily cardiovascular and cerebrovascular disease monitoring requirements are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring equipment, in particular to a multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases. BACKGROUND

[0002] The monitoring of cardiovascular and cerebrovascular diseases relies on the continuous and comprehensive capture of multiple physiological parameters of the human body, and the existing monitoring means has many limitations. Traditional monitoring devices are mostly single parameter acquisition types, which can only obtain one or a few parameters such as electrocardiogram and blood pressure, and cannot fully reflect the overall physiological state of the cardiovascular and cerebrovascular system, which may lead to misjudgment or omission of disease risk due to parameter loss.

[0003] Some monitoring devices use wired connection or fixed design, which is not comfortable to wear, limits the user's daily activities, cannot realize long-term continuous monitoring, and is difficult to capture occasional or dynamic changes in physiological abnormalities. At the same time, the physiological signals collected are easily affected by environmental interference and human motion to produce noise, and the existing filtering technology is mostly fixed mode, with limited noise reduction effect, resulting in insufficient data accuracy.

[0004] In terms of data transmission, the existing devices mostly only support a single communication mode, have poor adaptability in the absence of a network or in close-range transmission scenarios, and lack local storage functions, which may cause data loss once the communication is interrupted. In addition, most devices do not have a timely abnormality warning mechanism, or the warning method is single, which cannot quickly remind the user when the parameters are abnormal, and may delay the risk disposal opportunity. These problems make it difficult for the existing monitoring devices to meet the daily, accurate and convenient monitoring needs of cardiovascular and cerebrovascular diseases.

[0005] Therefore, a multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases is proposed. SUMMARY

[0006] The present application provides a multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases to solve the problems raised in the background art.

[0007] The specific technical solutions are as follows: A multi-modal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases, comprising a main control unit, a multi-modal physiological parameter acquisition unit, a data processing unit, a communication unit and a power supply unit, wherein: The signal output terminal of the multimodal physiological parameter acquisition unit is connected to the signal input terminal of the data processing unit, and is used to acquire various cardiovascular and cerebrovascular related physiological parameters of the human body and transmit them to the data processing unit; the signal output terminal of the data processing unit is connected to the signal input terminal of the main control unit, and is used to preprocess and extract features from the acquired physiological parameters before transmitting them to the main control unit; the signal input terminal of the communication unit is connected to the signal output terminal of the main control unit, and is used to transmit the parameter data processed by the main control unit to an external terminal; the power supply unit is electrically connected to the main control unit, the multimodal physiological parameter acquisition unit, the data processing unit, and the communication unit respectively, and is used to provide working power to each unit.

[0008] As a preferred embodiment of the present invention, the multimodal physiological parameter acquisition unit includes an electrocardiogram acquisition module, a blood pressure acquisition module, a blood oxygen saturation acquisition module, and a pulse wave acquisition module, and the signal output terminal of each module is connected to the signal input terminal of the data processing unit.

[0009] As a preferred embodiment of the present invention, the electrocardiogram acquisition module uses a dry electrode sensor, which contacts human skin through a flexible conductive material for non-invasive acquisition of human electrocardiogram signals.

[0010] As a preferred embodiment of the present invention, the blood pressure acquisition module includes a barometric pressure sensing component and a pulse wave velocity detection component, which work together to achieve continuous acquisition and dynamic monitoring of non-invasive blood pressure.

[0011] As a preferred embodiment of the present invention, the data processing unit has a built-in filtering module and a feature extraction module. The filtering module is used to perform noise reduction processing on the collected physiological parameters, and the feature extraction module is used to extract feature parameters related to cardiovascular and cerebrovascular diseases from the physiological parameters.

[0012] In a preferred embodiment of the present invention, the filtering module employs an adaptive Kalman filtering algorithm, and the feature extraction module employs a deep learning algorithm to extract fused features from multimodal physiological parameters.

[0013] In a preferred embodiment of the present invention, the communication unit includes a wireless communication module and a wired communication module. The wireless communication module supports at least one communication method among Bluetooth, Wi-Fi, and cellular networks, and the wired communication module uses a USB interface to realize data transmission.

[0014] As a preferred embodiment of the present invention, it further includes a storage unit, wherein the signal input terminal of the storage unit is connected to the signal output terminal of the main control unit, and is used to locally store the original physiological parameters and the processed feature parameters.

[0015] As a preferred embodiment of the present invention, it further includes an alarm unit, the signal input terminal of which is connected to the signal output terminal of the main control unit. When the main control unit determines that the physiological parameters exceed a preset threshold, it controls the alarm unit to issue an audible and visual alarm signal.

[0016] As a preferred embodiment of the present invention, the device adopts a wearable structure design, and the acquisition end of the multimodal physiological parameter acquisition unit is integrated on a flexible wearable carrier, which is adapted to the human wrist, chest or upper arm.

[0017] The present invention has the following beneficial effects: This invention provides a multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases. By integrating core units such as main control, multimodal acquisition, data processing, communication, and power supply, along with storage, alarm units, and a wearable structure design, it constructs an integrated, end-to-end cardiovascular and cerebrovascular physiological parameter monitoring system. The multimodal acquisition module covers core parameters such as electrocardiogram, blood pressure, blood oxygen saturation, and pulse wave. Combined with adaptive Kalman filtering and deep learning algorithms, it not only solves the problems of limited single-parameter monitoring and signal noise interference but also improves the accuracy of feature parameter extraction, providing comprehensive and high-quality data support for disease assessment. The wearable flexible carrier design and multi-mode communication module significantly improve wearing comfort and usage flexibility, breaking through the scenario limitations of traditional fixed monitoring devices. It supports continuous daily monitoring by users, while local storage and audible and visual alarm functions ensure data security and timely risk warnings. The overall device structure is simplified, the operating threshold is lowered, and the practicality, accuracy, and convenience of cardiovascular and cerebrovascular disease monitoring are significantly improved. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the architecture of a multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases provided in an embodiment of the present invention. Figure 2 A comparison chart of the comprehensiveness of monitoring provided in the embodiments of the present invention; Figure 3 A data security comparison chart provided for embodiments of the present invention; Figure 4 A comparison chart showing the ease of use of the embodiments of the present invention; Figure 5 A comparison chart of data accuracy provided for embodiments of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0020] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0021] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0022] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] Example Reference Figures 1-5 ,in Figure 2 The comparison between multi-parameter monitoring and single-parameter monitoring is shown. The solid blue line represents the multi-parameter monitoring capability of this device, while the dashed red line represents the effect of traditional single-parameter monitoring, demonstrating the device's more complete ability to capture the state of the cardiovascular system. Figure 3 The line graph compares the data retention capabilities of this device (marked with dots) and the comparison device (marked with squares) under different storage periods, demonstrating the improved security of this device in local storage and offline analysis; Figure 4 The bar chart compares the operation time of this device (left) with that of a traditional device (right) in different usage scenarios, highlighting the improvements of this device in terms of wearing comfort, ease of operation, and scenario adaptability. Figure 5The comparison of noise reduction effects using Kalman filtering (solid green line) and deep learning algorithms (dashed purple line) demonstrates the advantages of the data processing unit of this device in signal noise removal and feature extraction accuracy. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases provided in this embodiment includes: a main control unit, a multimodal physiological parameter acquisition unit, a data processing unit, a communication unit, and a power supply unit, wherein: The signal output terminal of the multimodal physiological parameter acquisition unit is connected to the signal input terminal of the data processing unit, used to acquire various cardiovascular and cerebrovascular related physiological parameters of the human body and transmit them to the data processing unit; the signal output terminal of the data processing unit is connected to the signal input terminal of the main control unit, used to preprocess and extract features from the acquired physiological parameters and then transmit them to the main control unit; the signal input terminal of the communication unit is connected to the signal output terminal of the main control unit, used to transmit the parameter data processed by the main control unit to an external terminal; the power supply unit is electrically connected to the main control unit, the multimodal physiological parameter acquisition unit, the data processing unit, and the communication unit respectively, used to provide working power to each unit.

[0024] By integrating the main control, multimodal acquisition, data processing, communication, and power supply units, an integrated multimodal physiological parameter acquisition architecture is constructed. The collaborative operation of each unit enables seamless integration of the entire process from parameter acquisition and processing to transmission. This not only simplifies the overall structure of the device but also allows for the simultaneous acquisition of multiple physiological parameters required for cardiovascular and cerebrovascular disease monitoring, providing comprehensive and continuous data support for subsequent disease assessment and avoiding the disconnect caused by a single module operating independently.

[0025] Specifically, in this embodiment, the multimodal physiological parameter acquisition unit includes an electrocardiogram acquisition module, a blood pressure acquisition module, a blood oxygen saturation acquisition module, and a pulse wave acquisition module. The signal output terminals of each module are connected to the signal input terminals of the data processing unit.

[0026] The multimodal physiological parameter acquisition unit integrates ECG, blood pressure, blood oxygen saturation, and pulse wave acquisition modules, covering the core parameters for monitoring cardiovascular and cerebrovascular diseases. Compared with single-parameter acquisition methods, it can more comprehensively capture the physiological state of the human cardiovascular and cerebrovascular system, reduce monitoring bias caused by missing parameters, and improve the ability to identify abnormal cardiovascular and cerebrovascular conditions.

[0027] Specifically, in this embodiment, the ECG acquisition module uses a dry electrode sensor, which contacts human skin through a flexible conductive material to collect human ECG signals non-invasively.

[0028] The ECG acquisition module uses dry electrode sensors combined with flexible conductive materials to complete ECG signal acquisition without invasive procedures. The flexible conductive material conforms better to the human skin, improving wearing comfort, while eliminating the cumbersome steps of using traditional electrode pads, lowering the user's operating threshold, and making it more suitable for daily or long-term ECG signal monitoring scenarios.

[0029] Specifically, in this embodiment, the blood pressure acquisition module includes a barometric pressure sensing component and a pulse wave velocity detection component, which work together to achieve continuous acquisition and dynamic monitoring of non-invasive blood pressure.

[0030] The blood pressure acquisition module combines a barometric pressure sensor and a pulse wave velocity detection component to achieve continuous and dynamic blood pressure acquisition under non-invasive conditions. This overcomes the limitations of traditional single-sampling blood pressure measurements, enabling real-time capture of dynamic blood pressure trends and providing more accurate data that reflects actual physiological conditions for assessing cardiovascular health, thus enhancing the practicality of blood pressure monitoring.

[0031] Specifically, in this embodiment, the data processing unit has a built-in filtering module and a feature extraction module. The filtering module is used to reduce noise in the collected physiological parameters, and the feature extraction module is used to extract feature parameters related to cardiovascular and cerebrovascular diseases from the physiological parameters.

[0032] The data processing unit incorporates filtering and feature extraction modules to specifically address noise interference in the acquired signals and accurately identify core characteristic parameters related to cardiovascular and cerebrovascular diseases. This ensures the purity of the acquired data while simplifying subsequent analysis processes, providing more targeted, high-quality data for disease risk assessment and improving the efficiency and effectiveness of data processing.

[0033] Specifically, in this embodiment, the filtering module uses an adaptive Kalman filter algorithm, and the feature extraction module uses a deep learning algorithm to extract fused features from multimodal physiological parameters.

[0034] The filtering module employing the adaptive Kalman filter algorithm can adaptively adjust the noise reduction strategy according to signal changes, removing different types of signal noise more efficiently than fixed filtering methods. Combining deep learning algorithms for multimodal parameter fusion feature extraction strengthens the correlation analysis between different parameters, further improving the accuracy of feature parameter extraction and providing algorithmic support for the precise monitoring of cardiovascular and cerebrovascular diseases.

[0035] Specifically, in this embodiment, the communication unit includes a wireless communication module and a wired communication module. The wireless communication module supports at least one of Bluetooth, Wi-Fi and cellular networks, and the wired communication module uses a USB interface to realize data transmission.

[0036] The communication unit includes both wireless and wired communication modules. The wireless module supports multiple mainstream communication methods, while the wired module uses a universal USB interface. Data transmission methods can be flexibly selected according to the usage scenario, satisfying both the convenience of short-range wireless transmission and enabling stable data export or data synchronization during charging via wired connection, thus improving the device's adaptability and flexibility.

[0037] Specifically, in this embodiment, a storage unit is also included. The signal input terminal of the storage unit is connected to the signal output terminal of the main control unit, and is used to locally store the original physiological parameters and the processed feature parameters.

[0038] The newly added storage unit enables local storage of raw physiological parameters and processed characteristic parameters, preserving monitoring data without relying on real-time data transmission. This not only avoids data loss due to communication interruptions but also enables subsequent data tracing and offline analysis, improving the device's data security and practicality in complex operating environments.

[0039] Specifically, in this embodiment, an alarm unit is also included. The signal input terminal of the alarm unit is connected to the signal output terminal of the main control unit. When the main control unit determines that the physiological parameters exceed the preset threshold, it controls the alarm unit to issue an audible and visual alarm signal.

[0040] The alarm unit works in conjunction with the main control unit to promptly issue audible and visual alarm signals when physiological parameters exceed preset thresholds. This allows users or relevant personnel to be alerted to abnormal cardiovascular parameters immediately, shortening risk response time, reducing health risks caused by undetected parameter abnormalities, and enhancing the device's safety early warning capabilities.

[0041] Specifically, in this embodiment, the device adopts a wearable structure design, and the acquisition end of the multimodal physiological parameter acquisition unit is integrated on a flexible wearable carrier, which is adapted to the human wrist, chest or upper arm.

[0042] Adopting a wearable structure and integrating the data acquisition device into a flexible wearable carrier, it adapts to multiple parts of the human body. This significantly improves wearing comfort and fit, reduces the impact of the device on daily activities, and supports continuous monitoring during normal life and activities. It breaks through the usage scenario limitations of traditional fixed monitoring devices and expands the applicability of the device.

[0043] Specifically, in this embodiment, the feature extraction module uses a multimodal fusion weight equation to calculate the fused feature parameters. The multimodal fusion weight equation is defined as follows: ; Wherein, weight w i The calculation is as follows: ; in: F is a fusion feature parameter used for cardiovascular and cerebrovascular disease risk assessment; n represents the number of physiological parameter modes; f i The eigenvalues ​​are the eigenvalues ​​of the i-th mode; Var(e i ) represents the variance of the estimation error of the i-th mode signal, which is obtained through the adaptive Kalman filter module; Sim(f i ,F ref f is the eigenvalue of the i-th mode. i With reference feature F ref Similarity; γ and δ are adjustment parameters used to balance the effects of signal quality and feature consistency; F ref The reference feature vector is trained from historical data of healthy individuals.

[0044] The derivation of the equation: This equation is derived based on information fusion theory and the attention mechanism in machine learning. The goal is to dynamically allocate weights in multimodal physiological parameter fusion to highlight reliable and relevant features. The derivation process is as follows: 1. Problem Analysis: Multimodal physiological parameters (such as ECG, blood pressure, blood oxygen saturation, and pulse wave) are affected by noise and motion interference during acquisition, and the reliability and importance of each modality signal vary. Direct averaging and fusion will reduce accuracy, therefore adaptive weighting is required.

[0045] 2. Weight Design: The weights should satisfy the following: Positively correlated with signal quality: signal estimation error variance Var(e i A small value indicates a reliable signal, and the weight should be large.

[0046] Positively correlated with feature consistency: Feature and reference feature F ref High similarity indicates conformity to a normal pattern, and therefore a higher weight should be assigned.

[0047] 3. Mathematical Modeling: An exponential function is used to ensure that the weights are positive and normalized. Weight w i With exp(-γ·Var(e i )+δ·Sim(f i ,F ref They are directly proportional, where: -γ·Var(e i The penalty signal error is applied, and the larger the error, the smaller the weight.

[0048] δ·Sim(f i ,F refRewards are based on consistency of features; the higher the similarity, the greater the weight.

[0049] 4. Normalization: By summing the denominators, we ensure that the sum of all weights is 1, making the fusion feature F a weighted average.

[0050] Example: Taking electrocardiogram (HRV characteristics), blood pressure (SBP characteristics), blood oxygen saturation (SpO2 characteristics), and pulse wave (PWV characteristics) as examples: 1. Obtain the estimation error variance of each mode signal from the adaptive Kalman filter module: Var(e1) = 0.05 (ECG); Var(e2) = 0.02 (blood pressure); Var(e3) = 0.01 (blood oxygen saturation); Var(e4) = 0.03 (pulse wave); 2. Calculate the values ​​of each feature and the reference feature F. ref Similarity (using cosine similarity): Sim(f1,F ref =0.8; Sim(f2,F ref =0.9; Sim(f3,F ref =0.7; Sim(f4,F ref ) = 0.85; 3. Set the adjustment parameters γ=0.5, δ=0.5.

[0051] 4. Calculate the weights:

[0052] Calculate w2, w3, and w4 similarly, and then normalize them.

[0053] 5. Calculate the fusion feature F = w1·f1 + w2·f2 + w3·f3 + w4·f4.

[0054] Parameter description: n: The number of physiological parameter modes, for example, n=4 (ECG, blood pressure, blood oxygen saturation, pulse wave).

[0055] f i : The eigenvalue of the i-th mode, for example: f1: HRV (heart rate variability) characteristics of the electrocardiogram; f2: Characteristics of systolic blood pressure (SBP); f3: Characteristics of blood oxygen saturation; f4: PWV (Pulse Wave Velocity) characteristics of the pulse wave; Var(e i ): The variance of the estimation error of the i-th mode signal, calculated in real time by the adaptive Kalman filter module, reflects the signal quality. The smaller the value, the more reliable the signal.

[0056] Sim(f i ,F ref ): Similarity measure, calculated using cosine similarity or Pearson correlation coefficient, ranging from [0,1]. The larger the value, the more the feature conforms to the normal pattern.

[0057] γ: Signal quality adjustment parameter, typically ranging from 0.1 to 1.0, used to control the degree of influence of signal error on weights.

[0058] δ: Feature consistency adjustment parameter, typically ranging from 0.1 to 1.0, used to control the degree of influence of feature similarity on weights.

[0059] F ref The reference feature vector is obtained from historical data of healthy individuals through training a deep learning model (such as an autoencoder) and represents the normal cardiovascular and cerebrovascular physiological state.

[0060] Technical effects: 1. Improve the accuracy of fusion features: By dynamically adjusting the weights, features with high signal quality and conforming to normal patterns are selected first, reducing the impact of noise and outliers.

[0061] 2. Enhanced robustness: When some modal signals are damaged (such as by motion interference), the equation automatically reduces its weights to ensure the reliability of the fused features.

[0062] 3. Improve disease monitoring performance: Fusion features more comprehensively reflect the cardiovascular and cerebrovascular status, and improve the sensitivity and specificity of abnormality detection.

[0063] 4. Synergy with existing solutions: The equations are seamlessly integrated with adaptive Kalman filtering and deep learning algorithms, enhancing the feature extraction capabilities of the data processing unit.

[0064] Working principle and process: In the feature extraction module of the data processing unit, the equation operates according to the following process: 1. Input: The estimation error variance Var(e) of each mode signal is obtained from the adaptive Kalman filter module. i ), and extract feature values ​​f from the preprocessed physiological parameters. i .

[0065] 2. Similarity calculation: Load reference feature F from storage unit. ref Calculate each f i With F ref Similarity Sim(fi ,F ref ).

[0066] 3. Weight Calculation: Calculate the weight w for each mode using equations. i .

[0067] 4. Calculation of fused features: Calculate the fused features. .

[0068] 5. Output: The fused feature F is transmitted to the main control unit for anomaly detection, storage, or transmission.

[0069] In summary, this device, through the coordinated operation of its various functional units, achieves full-process monitoring and processing of cardiovascular and cerebrovascular related physiological parameters. Its working logic is as follows: 1. The power supply unit provides a stable power supply to the main control unit, multimodal physiological parameter acquisition unit, data processing unit, communication unit, storage unit and alarm unit to ensure the synchronous operation of each unit.

[0070] 2. The multimodal physiological parameter acquisition unit simultaneously acquires multiple core physiological parameters of the human body through acquisition modules such as electrocardiogram, blood pressure, blood oxygen saturation, and pulse wave. The acquired raw signals are directly transmitted to the data processing unit.

[0071] 3. The data processing unit first removes environmental noise and motion interference from the original signal through an adaptive Kalman filter module, and then uses a deep learning algorithm to extract fusion features from the multimodal parameters, selecting key feature parameters related to cardiovascular and cerebrovascular diseases. The processed data is then transmitted to the main control unit.

[0072] 4. The main control unit coordinates the work of each unit. On the one hand, it sends the processed feature parameters and raw data to the storage unit for local storage. On the other hand, it transmits the data to the external terminal through the communication unit. At the same time, it compares the feature parameters with the preset threshold in real time. If the threshold is exceeded, the alarm unit is triggered.

[0073] 5. The communication unit can select wireless communication or wired USB interface to transmit data according to the usage scenario. After receiving the instruction from the main control unit, the alarm unit will issue an audible and visual alarm signal to complete the abnormal warning.

[0074] How to use: 1. Wearing device: Select the appropriate part such as wrist, chest or upper arm according to the usage needs, and fix the flexible wearable carrier with integrated collection end to the skin to ensure that the dry electrode sensor, air pressure sensor and other collection components are in full contact with the skin without loosening or displacement.

[0075] 2. Start-up device: The device is automatically initialized when the power supply unit is turned on, each unit enters the working state, and the main control unit synchronously completes the loading of the preset threshold.

[0076] 3. Monitoring and Operation: After the device is started, no additional operation is required. The multimodal physiological parameter acquisition unit automatically and continuously collects parameters such as electrocardiogram, blood pressure, blood oxygen saturation, and pulse wave. The data processing unit performs noise reduction and feature extraction simultaneously, and the main control unit processes and transmits the data in real time.

[0077] 4. Data Viewing: Users can receive monitoring data transmitted by the communication unit through an external terminal, and can also export the raw data and processed feature parameters in the storage unit through the wired USB interface after monitoring is completed for offline analysis.

[0078] 5. Abnormal response: If an audible and visual alarm occurs during monitoring, the user or relevant personnel should check the terminal data in a timely manner and take medical consultation or intervention measures if necessary. The alarm signal can be automatically deactivated or manually turned off after the parameters return to normal.

[0079] 6. End of use: After monitoring is complete, turn off the power. If the power supply unit is low on power, it can be charged via the wired USB interface for the next use.

[0080] Overall technical effect: This device, through integrated unit design and multimodal architecture, comprehensively addresses many shortcomings of traditional cardiovascular and cerebrovascular monitoring devices, resulting in a holistic technological advantage: 1. Enhanced monitoring comprehensiveness: The multimodal physiological parameter acquisition unit integrates multiple core parameter acquisition modules, acquiring key data such as ECG, blood pressure, blood oxygen saturation, and pulse wave at once. Compared with single parameter monitoring, it more completely reflects the physiological state of the cardiovascular system, effectively reducing monitoring bias caused by missing parameters and providing comprehensive data support for disease assessment.

[0081] 2. Data accuracy optimization: The data processing unit adopts an adaptive Kalman filter algorithm and deep learning fusion extraction technology, which can not only remove different types of signal noise, but also strengthen the correlation analysis between multiple parameters, improve the accuracy of feature parameter extraction, and provide high-quality data for disease risk assessment.

[0082] 3. Improved ease of use: The wearable flexible carrier design is compatible with multiple parts of the human body, making it comfortable to wear and not affecting daily activities. The dry electrode sensor can achieve non-invasive data collection without complicated operations. The combination of wireless and wired communication methods adapts to different usage scenarios, reducing the user's operating threshold and meeting the needs of long-term continuous monitoring.

[0083] 4. Enhanced data security: The storage unit enables local retention of both raw and processed data, preventing data loss due to communication interruptions. It also supports offline analysis and data traceability, improving the device's applicability in complex environments.

[0084] 5. Timely safety warning: The alarm unit and the main control unit are linked in real time, and an audible and visual alarm is issued quickly when parameters are abnormal, which shortens the risk response time, reduces the health risks caused by the failure to detect abnormalities in time, and further ensures the practicality and safety of monitoring.

[0085] Overall, through the collaborative design of its various units, this device achieves comprehensive, accurate, convenient, and safe monitoring of cardiovascular and cerebrovascular diseases, effectively making up for the shortcomings of existing technologies and better meeting the actual needs of daily monitoring and health management.

[0086] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases, characterized in that, It includes a main control unit, a multimodal physiological parameter acquisition unit, a data processing unit, a communication unit, and a power supply unit, among which: The signal output terminal of the multimodal physiological parameter acquisition unit is connected to the signal input terminal of the data processing unit, and is used to acquire various cardiovascular and cerebrovascular related physiological parameters of the human body and transmit them to the data processing unit; the signal output terminal of the data processing unit is connected to the signal input terminal of the main control unit, and is used to preprocess and extract features from the acquired physiological parameters before transmitting them to the main control unit; the signal input terminal of the communication unit is connected to the signal output terminal of the main control unit, and is used to transmit the parameter data processed by the main control unit to an external terminal; the power supply unit is electrically connected to the main control unit, the multimodal physiological parameter acquisition unit, the data processing unit, and the communication unit respectively, and is used to provide working power to each unit.

2. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, The multimodal physiological parameter acquisition unit includes an electrocardiogram acquisition module, a blood pressure acquisition module, a blood oxygen saturation acquisition module, and a pulse wave acquisition module. The signal output terminals of each module are connected to the signal input terminals of the data processing unit.

3. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 2, characterized in that, The ECG acquisition module uses a dry electrode sensor, which contacts human skin through a flexible conductive material for non-invasive acquisition of human ECG signals.

4. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 2, characterized in that, The blood pressure acquisition module includes a barometric pressure sensor and a pulse wave velocity detection component, which work together to achieve continuous non-invasive blood pressure acquisition and dynamic monitoring.

5. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, The data processing unit has a built-in filtering module and a feature extraction module. The filtering module is used to reduce noise in the collected physiological parameters, and the feature extraction module is used to extract feature parameters related to cardiovascular and cerebrovascular diseases from the physiological parameters.

6. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 5, characterized in that, The filtering module employs an adaptive Kalman filter algorithm, and the feature extraction module uses a deep learning algorithm to extract fused features from multimodal physiological parameters.

7. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, The communication unit includes a wireless communication module and a wired communication module. The wireless communication module supports at least one communication method among Bluetooth, Wi-Fi, and cellular networks, and the wired communication module uses a USB interface to realize data transmission.

8. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, It also includes a storage unit, the signal input terminal of which is connected to the signal output terminal of the main control unit, for local storage of raw physiological parameters and processed characteristic parameters.

9. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, It also includes an alarm unit, the signal input terminal of which is connected to the signal output terminal of the main control unit. When the main control unit determines that the physiological parameters exceed the preset threshold, it controls the alarm unit to issue an audible and visual alarm signal.

10. The multimodal physiological parameter acquisition device for monitoring cardiovascular and cerebrovascular diseases according to any one of claims 1-9, characterized in that, The device adopts a wearable structure design, and the acquisition end of the multimodal physiological parameter acquisition unit is integrated on a flexible wearable carrier, which is adapted to the human wrist, chest or upper arm.

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