Cardio-electroencephalogram acquisition and detection system

Abstract

The invention relates to the field of medical instruments, and discloses an electrocardio-electroencephalogram acquisition and detection system which comprises a wearable acquisition equipment module used for acquiring electrocardio-electroencephalogram signals in real time and integrating the functions of electrode plate arrangement, flexible electrodes, intelligent fitting, wire management and fault detection; the data transmission module is responsible for transmitting the collected cardiac and electroencephalogram signals to the central processing unit through a wireless network; the central processing module is used for receiving, processing and storing the cardio-electroencephalogram data transmitted from the wearable equipment and performing real-time analysis and diagnosis; and the remote monitoring and feedback system allows a manager to monitor and manage the state of the equipment in real time through a user interface. By adopting an advanced flexible electrode material and a precise signal processing algorithm, the precision and stability of signal acquisition can be remarkably improved, an intelligent fitting technology built in the system ensures that an electrode slice is in good contact with the skin, and high-quality cardiac and electroencephalogram signals can be stably acquired even when a user moves or the body position changes.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to a cardio-encephalogram (ECG) acquisition and detection system. Background Art

[0002] With the advancement of medical technology and rising awareness of health, electrocardiograms (ECGs) and electroencephalograms (EEGs), as important tools for monitoring heart and brain activity, have gained widespread application in clinical diagnosis, disease management, and scientific research. Traditional EEG signal acquisition equipment, typically used in specialized medical settings like hospitals, relies on large, fixed instruments. While these devices provide high-precision signal acquisition, their bulk, complex operation, and inconvenience limit their application in daily health monitoring and telemedicine.

[0003] In recent years, with the advancement of portable electronics, wireless communication, and flexible materials, portable, wearable EEG data acquisition devices have become increasingly popular. These devices monitor a user's EEG activity in real time and transmit the data to telemedicine platforms via wireless networks, facilitating remote diagnosis and health management. However, existing portable EEG data acquisition devices still have some shortcomings. Due to their simplified design, many portable devices lack the signal acquisition accuracy and anti-interference capabilities of professional medical equipment. Factors such as movement of the device while worn and poor skin contact can easily lead to signal distortion or loss, compromising diagnostic accuracy. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a cardio-encephalogram (ECG) acquisition and detection system to solve the problem that many portable devices cannot reach the level of professional medical equipment in signal acquisition accuracy and anti-interference ability due to their simplified design.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A cardio-encephalogram (ECG) acquisition and detection system, comprising:

[0006] Wearable acquisition device module: used to collect EEG signals in real time. The device integrates electrode placement, flexible electrodes, intelligent fitting, wire management, and fault detection functions, and has comprehensive functions of signal acquisition, storage, communication, and power management.

[0007] Data transmission module: responsible for transmitting the collected EEG signals to the central processing unit via a wireless network. This module includes low-power wireless communication, dual-channel data transmission, signal optimization, and security management functions;

[0008] Central processing module: used to receive, process and store EEG data transmitted from wearable devices, perform real-time analysis and diagnosis, and generate analysis reports and abnormality alerts. This module includes layered signal processing, cloud computing, edge processing and data storage functions;

[0009] Remote monitoring and feedback system: allows managers to monitor and manage device status in real time through a user interface. The system has data display, remote control, intelligent alarm and firmware upgrade functions.

[0010] Preferably, the wearable acquisition device module includes:

[0011] Multi-layer fabric design: The outer layer is wear-resistant and breathable material, the middle layer is conductive fabric with embedded electrodes, and the inner layer is skin-friendly fabric to reduce friction and improve wearing comfort;

[0012] Electrode placement and installation: The electrodes are embedded inside the fabric and precisely arranged to cover multiple signal collection points on the chest and head. The wires are managed through slots in the fabric to reduce external interference.

[0013] Intelligent fit and positioning: Includes embedded sensors that monitor the contact status between the electrode and the skin in real time and automatically adjust the fit to optimize the pressure distribution of the electrode;

[0014] Wire management and integration: Wires are managed by embedded cloth grooves to avoid entanglement or pulling, elastic materials are used to maintain wire tension, and quick-plug interfaces are used at wire connections;

[0015] Fault detection and self-test unit: monitors the operating status of the equipment in real time, detects the connection between the electrodes and wires, automatically triggers the self-test process and generates alarm information.

[0016] Preferably, the data transmission module includes:

[0017] Low-power wireless transmission unit: uses Bluetooth Low Energy and Wi-Fi 6 technologies, with dynamic power consumption management function to extend device usage time;

[0018] Dual-channel data transmission unit: supports active and standby dual-channel data transmission. The active channel is used for real-time data transmission, and the standby channel is automatically activated when the active channel is interrupted, ensuring the continuity and reliability of data transmission.

[0019] Signal optimization unit: adjusts communication parameters according to the current network status, including power control, frequency selection, and channel adjustment to optimize data transmission quality;

[0020] Security Management Unit: Implements data encryption, authentication, and access control, and supports real-time monitoring of communication security and vulnerability remediation.

[0021] Preferably, the central processing module includes:

[0022] Hierarchical signal processing unit: uses a multi-core processor to perform hierarchical signal processing, including noise filtering, feature extraction, pattern recognition, and event detection, ensuring efficient and real-time signal processing;

[0023] Cloud computing and edge processing unit: With cloud computing and edge processing capabilities, complex computing tasks are completed in the cloud, and tasks with high real-time requirements are processed on edge devices;

[0024] Data storage and log management unit: adopts a distributed storage architecture, supports local storage and cloud synchronization, automatically generates operation logs, and records detailed information during data collection, processing, and transmission;

[0025] Intelligent diagnosis and alarm unit: It has multiple built-in medical diagnosis algorithms, analyzes EEG signals in real time, and automatically triggers alarms to generate analysis reports and alarm notifications.

[0026] Preferably, the remote monitoring and feedback system includes:

[0027] Multi-terminal support and data visualization unit: supports access from multiple terminals, including mobile phones, tablets, and computers, and can display the waveforms and historical data of EEG signals in real time, providing multiple views for doctors to analyze;

[0028] Remote control unit: supports remote configuration, diagnosis and maintenance of the device through the data management platform, including collection frequency adjustment, reporting time setting and device restart functions;

[0029] Intelligent alarm and event processing unit: monitors the operating status of the equipment in real time, automatically triggers an alarm when an anomaly is detected, generates a processing flow to notify relevant personnel, and records the processing process and results;

[0030] Firmware upgrade unit: supports remote firmware upgrades and automatically pushes updates over the network to keep device functions up to date and fix known vulnerabilities and errors.

[0031] Preferably, the central processing module further includes:

[0032] Big Data Analysis Unit: This unit uses machine learning algorithms to conduct in-depth analysis of EEG data, uncovering potential health risks and behavioral patterns. Based on these analysis results, it optimizes diagnostic models and continuously improves the system's detection accuracy.

[0033] Prediction model unit: This unit builds and updates the patient's personalized health prediction model based on the collected electrocardioencephalogram signal data. It can identify potential health problems in advance and make intervention recommendations. The prediction model will be automatically updated and adjusted according to the newly collected data.

[0034] Preferably, the wear-resistant and breathable material of the outer layer includes nylon, polyester fiber, elastic spandex, the conductive fabric of the middle layer includes conductive silver fiber fabric, conductive carbon fiber fabric, conductive polymer, and the skin-friendly fabric of the inner layer includes pure cotton, bamboo fiber, and modal.

[0035] Preferably, the central processing module further includes:

[0036] Heart-brain coupling analysis unit: By analyzing the time series correlation and frequency domain characteristics of ECG signals and EEG signals, the synergistic pattern between heart and brain activities is extracted to evaluate the function of the autonomic nervous system or the state of psychological stress. The analysis unit has a built-in algorithm based on time-frequency analysis and causality.

[0037] Preferably, the intelligent fitting and positioning further includes:

[0038] Temperature and humidity adaptive adjustment unit: monitors the skin surface status through embedded temperature and humidity sensors, and dynamically adjusts the fitting pressure and conductivity of the electrode sheet to adapt to signal acquisition requirements under different temperature and humidity conditions.

[0039] Preferably, a method for collecting and detecting electrocardiogram (ECG) data comprises the following steps:

[0040] S1: Data collection and reporting:

[0041] The wearable acquisition device module periodically collects electrocardiogram (ECG) data at preset intervals through the data acquisition unit. The data includes but is not limited to transient potential changes and waveform characteristics.

[0042] The collected data is uploaded to the central processing unit through the data transmission module. If the network signal is poor, the data will be temporarily stored in the storage unit and uploaded again after the signal is restored. It supports breakpoint resuming.

[0043] S2: Data Analysis and Diagnosis:

[0044] After the central processing unit receives and stores the data, the data processing unit begins to analyze the EEG signals, identifying patterns and abnormal behaviors, and combines the big data analysis module to conduct in-depth analysis and prediction;

[0045] Generate diagnostic results and generate health reports or alarm notifications based on historical data;

[0046] S3: Remote monitoring and maintenance:

[0047] Managers monitor the status of all acquisition devices in real time through the remote monitoring system, use remote diagnostic units to check the health of devices, and analyze operation logs;

[0048] The platform supports regular push of firmware updates, adjustment of device configurations, and generation of preventive maintenance plans.

[0049] The present invention provides a cardio-encephalogram (ECG) acquisition and detection system. It has the following beneficial effects:

[0050] 1. The present invention's EEG acquisition and detection system utilizes advanced flexible electrode materials and sophisticated signal processing algorithms, significantly improving the accuracy and stability of signal acquisition. The system's built-in intelligent bonding technology ensures excellent contact between the electrodes and the skin, enabling stable acquisition of high-quality EEG signals even during movement or changes in body position. This significantly enhances data reliability and ensures accurate medical diagnosis.

[0051] 2. The wearable device of the present invention system utilizes a multi-layered fabric design, with a skin-friendly inner layer and a wear-resistant and breathable outer layer, enhancing wearer comfort. Flexible electrodes and intelligent fitting technology ensure the device adheres closely to the skin, adapting to different body shapes and motion states, making it suitable for long-term wear without discomfort. Furthermore, low-power wireless transmission and intelligent power management extend the device's battery life, allowing users to wear it continuously throughout their daily lives without frequent charging.

[0052] 3. The system's built-in medical diagnostic algorithms automatically analyze EEG signals, identifying potential abnormalities such as arrhythmias or epileptic seizures and automatically generating diagnostic reports and alert notifications. This intelligent diagnosis reduces reliance on manual monitoring and improves detection efficiency and accuracy.

[0053] 4. Through big data analysis and predictive models, the system can provide users with personalized health recommendations, identify potential health risks in advance, and continuously optimize health management strategies based on the user's historical data. Users can view their personal health status in real time through the remote monitoring system and receive personalized health reports and recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is an architecture diagram of a cardio-encephalogram (ECG) acquisition and detection system according to the present invention;

[0055] Figure 2 This is a module architecture diagram of the wearable data acquisition device of the present invention;

[0056] Figure 3 This is a diagram of the data transmission module architecture of the present invention;

[0057] Figure 4 This is a diagram of the central processing module architecture of the present invention;

[0058] Figure 5 This is a diagram of the remote monitoring and feedback system architecture of the present invention;

[0059] Figure 6 This is a flow chart of a method for collecting and detecting electrocardiogram (ECG) of the present invention;

[0060] Figure 7 This is a signal processing flow chart of the heart-brain coupling analysis unit of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] Please see the attached Figure 1 - Attachment Figure 7 The embodiment of the present invention provides a cardio-encephalogram (ECG) acquisition and detection system, comprising:

[0063] 1. Wearable acquisition device module

[0064] The wearable acquisition device module is the core of the entire EEG acquisition system. It is responsible for collecting the human body's EEG and ECG signals in real time and has the following key functions:

[0065] Electrode placement: The device is embedded with precisely positioned electrodes, which are connected to the device's signal processing module via flexible conductive material. The placement of the electrodes is carefully designed to ensure coverage of key ECG and EEG signal collection points on the human body, such as the chest lead points and the EEG signal collection area on the scalp.

[0066] Flexible electrodes: The electrodes are made of flexible materials, allowing them to closely conform to the curves of the human body. Whether in motion or at rest, the electrodes can maintain stable contact. This design not only improves the stability of signal acquisition but also enhances wearing comfort.

[0067] Smart Fit: The device is equipped with a smart fit system that uses built-in sensors to monitor the contact between the electrodes and the skin in real time. If poor contact or insufficient pressure is detected, the system automatically adjusts the pressure or position of the electrodes to ensure accurate signal acquisition. This intelligent adjustment feature is particularly useful for users with long-term wear or varying body shapes.

[0068] Wire Management: Wires are carefully routed and secured with recessed grooves and elastic material to prevent tangling or friction during use. Quick-disconnect connectors facilitate maintenance and replacement.

[0069] Fault Detection: The device has a built-in fault detection system that monitors the operating status of the electrodes, wires, and the entire system in real time. When an abnormality is detected (such as an electrode detachment, wire breakage, or signal loss), the system automatically triggers a self-check process and generates an alarm message to notify the user or management personnel for action.

[0070] Signal Acquisition and Storage: The device collects EEG signals with high precision and temporarily stores the data in internal memory. This memory supports breakpoint-resume uploads, ensuring complete upload after interruptions, preventing data loss.

[0071] Communication and power management: The device integrates a wireless communication module (such as Bluetooth Low Energy or Wi-Fi 6) to support real-time data transmission with the central processing unit. The device also has power management functions, including low-power mode, real-time power monitoring, and battery management. Some devices also support solar charging to extend battery life.

[0072] 2. Data transmission module

[0073] The data transmission module is responsible for wirelessly transmitting the collected EEG signals from the wearable device to the central processing unit to ensure the integrity and security of the data. This module has the following functions:

[0074] Low-power wireless communication: Using Bluetooth Low Energy (BLE) or Wi-Fi 6 technology, energy consumption is minimized while maintaining data transmission speeds. This module also includes an intelligent power management system that dynamically adjusts communication parameters based on data transmission requirements and network conditions to optimize energy consumption.

[0075] Dual-channel data transmission: A dual-channel transmission mechanism is designed. The primary channel is used for data transmission under normal circumstances. When the primary channel is blocked or interrupted, the backup channel (such as 4G / 5G or LoRa) is automatically activated to ensure the continuity and stability of data transmission. This mechanism is particularly suitable for use in complex or unstable network environments.

[0076] Signal optimization: The data transmission module is equipped with a signal optimization function that can automatically adjust communication parameters based on real-time network conditions (such as signal strength, latency, and packet loss rate), including power control, frequency selection, and channel switching, to improve transmission efficiency and data integrity.

[0077] Security Management: To ensure data transmission security, the module includes built-in data encryption algorithms (such as AES) and supports identity authentication and multi-level access control. The security management system monitors potential security vulnerabilities during communication in real time and automatically fixes them or notifies administrators for intervention.

[0078] 3. Central processing module

[0079] The central processing module is the data processing and analysis center of the system, responsible for receiving, processing, and storing EEG data from wearable devices and providing real-time diagnosis and feedback. This module includes the following key functions:

[0080] Layered signal processing: A multi-core processor performs layered processing on received EEG data. The first layer performs basic noise filtering and signal correction, the second layer performs feature extraction and pattern recognition (such as QRS complex identification and EEG spectrum analysis), and the third layer is responsible for advanced event detection and anomaly identification (such as real-time detection of arrhythmias or epileptic seizures). This layered architecture improves signal processing efficiency and accuracy.

[0081] Cloud Computing and Edge Processing: The central processing module offers both cloud computing and edge processing capabilities. For tasks requiring significant computing resources (such as complex signal analysis or inference of deep learning models), data can be uploaded to the cloud for processing. For tasks requiring high real-time performance (such as real-time monitoring of ECG signals), computation is performed on edge devices to ensure rapid response.

[0082] Data Storage and Log Management: The module utilizes a distributed storage architecture, allowing data to be stored locally and synchronized to cloud servers, ensuring long-term data preservation and security. The system automatically generates operation logs, recording detailed information about each data processing, transmission, and storage. These logs can be used for troubleshooting and auditing.

[0083] Intelligent Diagnosis and Alarms: The central processing module incorporates multiple medical diagnostic algorithms that analyze collected EEG signals in real time to identify potential anomalies. Upon detecting an anomaly (such as myocardial ischemia or sudden EEG abnormality), the system automatically generates an alarm and sends it to the user or medical personnel. The diagnostic algorithms also feature self-learning capabilities, enabling continuous optimization through the accumulation of historical data to enhance diagnostic accuracy.

[0084] 4. Remote monitoring and feedback system

[0085] The remote monitoring and feedback system is the system's management and user interaction interface, allowing managers and medical personnel to monitor the system's operating status in real time, obtain data analysis results, and remotely control the equipment. The system has the following functions:

[0086] Multi-terminal support and data visualization: The system supports access from a variety of terminals (such as smartphones, tablets, and PCs), displaying real-time EEG waveforms, trend charts, and historical data through a visual dashboard. Data visualization tools offer multiple viewing modes (such as time domain analysis, frequency domain analysis, and event tagging) to help medical staff gain in-depth analysis of patient health.

[0087] Remote Control: Through the data management platform, administrators can remotely configure and control all connected wearable devices. Remote control functions include adjusting the data collection frequency, setting the reporting time, restarting the device, and upgrading the firmware. Remote diagnostics allow administrators to view device operation logs, diagnose device faults, and perform remote maintenance when necessary.

[0088] Intelligent Alerts and Event Handling: The system features a built-in intelligent alert mechanism that monitors the operating status of all devices in real time. When an anomaly is detected (such as device failure, data loss, or signal anomaly), the system automatically triggers an alert and initiates the pre-defined event handling process. Event handling records are automatically saved and reports are generated for subsequent analysis and optimization.

[0089] Firmware Upgrade: The system supports remote firmware upgrades, automatically pushing the latest firmware updates to devices over the network to ensure continuous optimization of device functionality and fixes of known vulnerabilities. Administrators can set up regular update schedules to ensure the system is always up to date.

[0090] 1. Wearable acquisition device module

[0091] The wearable data acquisition device module is the core hardware of the entire EEG data acquisition and detection system. It is responsible for real-time data acquisition of the human body's EEG signals and ensures high-precision data acquisition and user comfort through the coordinated work of multiple functional units. The device mainly includes the following units:

[0092] 1.1 Multi-layer fabric design

[0093] Outer Material: The outer layer is made of wear-resistant and breathable materials, including nylon, polyester fiber and elastic spandex. These materials are high in strength and abrasion resistance, and can provide good breathability, suitable for long-term wear.

[0094] Middle Layer Material: The middle layer is a conductive fabric with embedded electrodes. Conductive materials include conductive silver fiber fabric, conductive carbon fiber fabric, and fabric coated with conductive polymers (such as PEDOT). These materials ensure stable electrical signal transmission and mechanical durability.

[0095] Inner material: The inner layer is made of skin-friendly fabrics such as pure cotton, bamboo fiber and modal. These materials are soft and breathable, can reduce friction and improve wearing comfort, and are suitable for long-term contact with the skin.

[0096] 1.2 Electrode arrangement and installation

[0097] Electrodes are embedded within the middle layer of conductive fabric and precisely positioned to cover the body's key ECG and EEG signal collection points. Wires are routed through slots in the fabric to avoid exposure, reduce electromagnetic interference, and enhance device durability.

[0098] 1.3 Intelligent Fitting and Positioning

[0099] The unit contains embedded sensors that, through underlying sensor fusion technology, monitor the contact status between the electrode and the skin in real time. Based on this feedback, the unit automatically adjusts the pressure and position of the electrodes to optimize signal acquisition quality. This intelligent system is controlled by a low-power embedded microcontroller, ensuring fast response and energy efficiency.

[0100] 1.4 Wire Management and Integration

[0101] The wires are embedded in a cloth groove for management, and elastic material maintains wire tension to prevent entanglement or pulling during wear or exercise. The quick-plug interface design of the wires simplifies electrode replacement and daily maintenance of the device.

[0102] 1.5 Fault detection and self-test unit

[0103] The device integrates a fault detection module that uses underlying self-diagnostic algorithms and a real-time operating system (RTOS) to monitor the connection status of electrodes and wires in real time. The system can detect anomalies, such as electrode detachment or wire breakage, and automatically trigger a self-diagnosis process and generate an alarm.

[0104] 2. Data transmission module

[0105] The data transmission module is responsible for transmitting the collected EEG signals to the central processing unit via the wireless network to ensure the integrity and security of the data. This module includes the following functional units:

[0106] 2.1 Low-power wireless transmission unit

[0107] Using Bluetooth Low Energy (BLE) and Wi-Fi 6 technologies, the device achieves stable data transmission through an embedded wireless communication chip. The underlying layer uses Dynamic Power Management technology to extend the device's operating time by adjusting wireless transmission power and data transmission rate, while ensuring real-time data transmission.

[0108] 2.2 Dual-channel data transmission unit

[0109] A dual-channel transmission mechanism with primary and backup channels is used. The primary channel is used for data transmission under normal circumstances. When the primary channel is interrupted, the backup channel (such as 4G / 5G network) is automatically activated. This mechanism ensures the continuity and stability of data transmission through the underlying redundancy design (Redundancy Design) and transmission protocol switching (Protocol Switching).

[0110] 2.3 Signal Optimization Unit

[0111] The signal optimization unit adjusts communication parameters such as power control, frequency selection, and channel switching based on the current network status through the underlying adaptive communication algorithms to optimize data transmission quality and reduce delays and packet loss.

[0112] 2.4 Security Management Unit

[0113] The security management unit implements underlying encryption technologies, such as AES, and employs authentication and access control mechanisms to protect data security during transmission. The system also supports real-time monitoring of potential security vulnerabilities during communication and addresses them through an automated patching system.

[0114] 3. Central processing module

[0115] The central processing module is the data processing and analysis center of the system, responsible for receiving, processing and storing EEG data from wearable devices and providing real-time diagnosis and feedback. This module includes the following functional units:

[0116] 3.1 Hierarchical Signal Processing Unit

[0117] Using multi-core processors and underlying digital signal processing (DSP) technology, the received EEG data is processed in layers, including:

[0118] The first layer: noise filtering and signal correction, removing background noise through filtering algorithms.

[0119] The second layer: perform feature extraction and pattern recognition, and use signal analysis algorithms to identify key ECG and EEG features.

[0120] The third layer: event detection and anomaly recognition, using machine learning algorithms to detect and identify abnormal electrocardiogram (ECG) signals, such as arrhythmias and epileptic seizures.

[0121] 3.2 Cloud Computing and Edge Processing Units

[0122] The unit has cloud computing and edge computing capabilities:

[0123] Cloud computing: Complex computing tasks are uploaded to cloud servers for processing, using high-performance computing resources for data analysis and storage.

[0124] Edge computing: Tasks with high real-time requirements are processed on edge devices to reduce data transmission delays and ensure fast response.

[0125] 3.3 Data Storage and Log Management Unit

[0126] The system uses a distributed storage architecture to support synchronization between local storage and the cloud. The system uses the underlying database management system (DBMS) and file system (FileSystem) to automatically generate operation logs, recording every step of data collection, processing, and transmission for subsequent auditing and troubleshooting.

[0127] 3.4 Intelligent diagnosis and alarm unit

[0128] It has multiple built-in medical diagnostic algorithms that use deep learning models to analyze EEG signals in real time and identify abnormal signals. Once an abnormality is detected, the system automatically generates an alarm and uses push notification services to notify the user or medical staff.

[0129] 3.5 Big Data Analysis Unit

[0130] Using big data technologies and machine learning algorithms, we analyze the massive amounts of collected EEG data to uncover potential health risks and behavioral patterns. Through data mining and predictive analytics, we optimize diagnostic models and continuously improve detection accuracy.

[0131] 3.6 Prediction Model Unit

[0132] Based on the collected EEG data, a personalized health prediction model for patients is constructed and updated. This model uses adaptive learning algorithms to automatically update and adjust based on newly collected data, identifying potential health issues in advance and providing personalized intervention recommendations.

[0133] 4. Remote monitoring and feedback system

[0134] The remote monitoring and feedback system provides an intuitive user interface and a multifunctional management platform, allowing managers and medical staff to monitor and remotely control equipment status in real time. The system includes the following functional units:

[0135] 4.1 Multi-terminal support and data visualization unit

[0136] The system supports access from multiple terminals and uses a cross-platform framework to achieve real-time data visualization. The visualization tool uses underlying graphics processing unit (GPU) acceleration technology to display EEG waveforms, trend charts, and historical data, and provides multiple viewing modes (such as time domain analysis, frequency domain analysis, and event marking) for physician analysis.

[0137] 4.2 Remote Control Unit

[0138] Through the data management platform, managers can remotely configure and control all connected wearable devices. The underlying technology uses remote procedure call (RPC) technology and IoT protocols (such as MQTT) to achieve remote diagnosis, device restart, firmware upgrade and other functions.

[0139] 4.3 Intelligent Alarm and Event Processing Unit

[0140] This unit utilizes an event-driven architecture to monitor the operating status of equipment in real time. When an anomaly is detected (such as equipment failure, data loss, or signal anomaly), the system automatically triggers an alarm and initiates event processing via a pre-defined workflow engine. Processing records are automatically saved for subsequent analysis.

[0141] 4.4 Firmware Upgrade Unit

[0142] Supports remote firmware upgrades, using OTA (Over-the-Air) technology to automatically push the latest firmware to the device via the network. Administrators can manage update plans through the underlying version control system (Version Control System), ensuring that the system is always running in the latest state and fixing known vulnerabilities.

[0143] The heart-brain coupling analysis unit in the central processing module is designed to analyze the dynamic interaction between electrocardiogram (ECG) and electroencephalogram (EEG) signals to extract the synergistic pattern between heart and brain activities, thereby achieving an accurate assessment of autonomic nervous system function and psychological stress status. The specific implementation of this unit includes the following aspects:

[0144] The heart-brain coupling analysis unit first synchronizes the collected ECG and EEG signals with high precision using timestamps, ensuring millisecond-level time alignment between the two signals. In the preprocessing stage, adaptive filtering techniques (such as wavelet transform and wavelet packet decomposition) are used to remove baseline drift and electromyographic interference from the ECG signals and eye movement artifacts and power supply noise from the EEG signals, respectively, to improve the accuracy of subsequent analysis.

[0145] The unit has a built-in algorithm based on time-frequency analysis to extract the time series correlation and frequency domain features of ECG and EEG signals. Specifically, through short-time Fourier transform (STFT) and Hilbert-Huang transform (HHT), the QRS complex characteristics of the ECG signal (such as RR interval variability) and the frequency band power of the EEG signal (such as α wave, β wave and θ wave) are jointly analyzed to generate dynamic time-frequency spectrograms. These time-frequency spectrograms can intuitively reflect the short-term and long-term correlation between heart rate variability (HRV) and EEG activity.

[0146] To further explore the deep interaction patterns between heart and brain signals, the analysis unit uses an algorithm based on Granger Causality to evaluate the causal influence of ECG signals on EEG signals or EEG signals on ECG signals. For example, by calculating the predictive ability of heart rate changes on EEG alpha wave power, it is possible to determine whether the sympathetic and parasympathetic nerve activities of the autonomic nervous system are in a balanced state. In addition, the unit also combines the Transfer Entropy method to quantify the information flow between heart and brain signals, further improving the accuracy of the analysis.

[0147] Based on the above analysis, the unit can extract synergistic patterns between heart and brain activities, such as the positive correlation between the low-frequency component (LF) of heart rate variability and the power of EEG theta waves, or the synchronization phenomenon of sudden acceleration of heart rate and enhanced EEG beta waves. These patterns can be used to evaluate the function of the autonomic nervous system (such as increased sympathetic nerve excitability) or psychological stress states (such as abnormal EEG beta waves caused by anxiety). The analysis results are stored in the form of feature vectors and displayed as a two-dimensional correlation graph or a three-dimensional time-frequency interactive graph through a visual interface, which is convenient for medical personnel to intuitively interpret.

[0148] The Heart-Brain Coupling Analysis Unit is self-learning. By collecting EEG data from users over time and combining it with a convolutional neural network (CNN) and a long short-term memory (LSTM) network, it continuously optimizes its synergistic pattern extraction algorithm. For example, if the system detects that a specific user's heart-brain coupling pattern deviates from a healthy baseline, it automatically adjusts analysis parameters to generate a personalized health risk assessment model, thereby improving the targetedness and accuracy of the diagnosis.

[0149] Applications for this unit include, but are not limited to: ① Assessing the impact of chronic stress on the cardiovascular and nervous systems to inform mental health management; ② Monitoring the potential risk of sudden cardiac death by triggering early warnings through the early detection of abnormal heart-brain signals; and ③ Studying the interaction between the brain and heart during sleep to support the diagnosis of sleep disorders. Compared to the limitations of single-signal analysis in existing technologies, this unit significantly enhances the system's intelligence and diagnostic depth through heart-brain coupling analysis, providing technical support for multidisciplinary research.

[0150] The intelligent fit and positioning functions in the wearable acquisition device module are further optimized through the addition of a temperature and humidity adaptive adjustment unit. This unit monitors the skin surface condition and dynamically adjusts the electrode performance to ensure the stability and accuracy of signal acquisition under different environmental conditions. Its specific implementation includes the following aspects:

[0151] The temperature and humidity adaptive adjustment unit embeds a miniature temperature and humidity sensor (such as a digital DHT series sensor or a MEMS-based temperature and humidity module) near the electrode pad to monitor temperature and humidity changes on the skin surface in real time. The sensor collects data 10 times per second, covering a temperature range of -10°C to 50°C and a humidity range of 0% to 100% RH, with an accuracy of ±0.5°C and ±2% RH, respectively, ensuring the reliability of environmental data. The collected temperature and humidity data is processed by a low-power microcontroller (MCU) and transmitted to the main control unit of the smart fitting system.

[0152] The unit dynamically adjusts the fitting pressure of the electrode sheet based on temperature and humidity data. For example, in a high humidity environment (such as when the user is sweating), the moist skin surface may cause the electrode to slip or have poor contact. The unit increases the downward pressure of the electrode sheet (range 0.5N to 2N) through a built-in micro-servo motor or airbag structure to ensure stable skin contact; in a low humidity environment (such as cold and dry weather), the unit appropriately reduces the pressure (to below 0.3N) to avoid excessive friction or discomfort caused by dry skin. The adjustment process is driven by an embedded PID (proportional-integral-differential) control algorithm with a response time of less than 500 milliseconds.

[0153] Changes in temperature and humidity can affect the electrical conductivity of the electrode sheet, especially in flexible electrodes. To this end, the unit achieves adaptive optimization through dynamic regulation of the conductive gel or ion conductive layer coated on the electrode surface. For example, under high humidity conditions, the unit enhances the conductivity between the electrode and the skin by releasing a small amount of conductive gel (stored in the micropores built into the electrode sheet); under low humidity conditions, the built-in micro-heating element (power less than 0.1W) in the electrode sheet slightly increases the temperature (increases by 2-3°C) to improve the ion migration efficiency of the conductive material, thereby maintaining the stability of signal acquisition.

[0154] The temperature and humidity adaptive adjustment unit has a built-in environmental adaptability test function. It runs a self-test process every time the device starts up or every 30 minutes to detect the impact of the current temperature and humidity on signal quality (such as a decrease in signal-to-noise ratio or waveform distortion). If an anomaly is detected (such as humidity exceeding 80% RH causing signal drift), the unit automatically adjusts the parameters and records the adjustment log. At the same time, the feedback information is sent to the central processing module through the data transmission module for subsequent performance optimization and user prompts (such as recommending electrode replacement).

[0155] Applications for this unit include: 1. Addressing electrode failure caused by sweating during exercise monitoring, ensuring continuous acquisition of EEG signals during high-intensity exercise; 2. Maintaining electrode conductivity and wearing comfort in cold environments (such as outdoors in winter); and 3. Reducing signal interruptions caused by environmental changes in long-term wear scenarios (such as hospitalized patients or the elderly). Its advantage lies in overcoming the existing technology's dependence on environmental conditions for electrode bonding, significantly improving the system's robustness and range of applications. It is particularly suitable for health monitoring needs in dynamic and extreme environments.

[0156] The temperature and humidity adaptive adjustment unit works in conjunction with the embedded sensors in the smart fitting system to form a closed-loop control mechanism. For example, when the temperature and humidity sensors detect elevated skin humidity, the smart fitting system combines data from the pressure sensors to further optimize electrode position and angle (e.g., adjusting the tilt angle by ±5°) to reduce the interference of sweat accumulation on signal acquisition. This collaborative mechanism enables data exchange via the Bluetooth Low Energy (BLE) protocol, ensuring overall system energy efficiency and responsiveness.

[0157] This EEG acquisition and monitoring system combines advanced hardware design and underlying software technologies to form a complete solution, encompassing data collection, transmission, and processing for wearable devices, as well as remote monitoring and feedback. Utilizing multi-layer fabric design, intelligent bonding, low-power wireless communication, big data analytics, and cloud and edge computing technologies, the system provides users with stable and efficient health monitoring and real-time feedback.

[0158] A method for collecting and detecting electrocardiogram (ECG) comprises the following steps:

[0159] S1: Data collection and reporting

[0160] Data collection and preprocessing:

[0161] Wearable Data Acquisition Device Module: The wearable device uses its built-in multi-layer fabric design and embedded electrodes to periodically collect EEG data at preset intervals. This data includes, but is not limited to, transient potential changes, waveform characteristics, ECG QRS complexes, and EEG alpha and beta waves.

[0162] Data Acquisition Unit: The data acquisition unit converts analog EEG signals into digital signals using a high-precision analog-to-digital converter (ADC), ensuring the accuracy and integrity of signal acquisition. The acquisition unit also performs preliminary noise filtering and signal correction to improve data quality.

[0163] Data storage and reporting:

[0164] Data transmission module: The collected digital signals are uploaded to the central processing unit through the data transmission module. The data transmission module uses Bluetooth Low Energy (BLE) or Wi-Fi 6 technology for low-power wireless transmission. If the network signal is poor or interrupted during transmission, the data will be temporarily stored in the device's local storage unit and uploaded again after the signal is restored. The storage unit has a breakpoint resume function to ensure data integrity and avoid data loss due to network interruptions.

[0165] Upload strategy: The system dynamically adjusts upload frequency and priority based on network conditions and data importance. When critical data (such as abnormal ECG events) is detected, the system prioritizes uploading this data to ensure timely response and diagnosis.

[0166] S2: Data Analysis and Diagnosis

[0167] Data reception and storage:

[0168] Central Processing Unit: After receiving data from the wearable device, the central processing unit performs preliminary storage of the data through its high-performance multi-core processor. The system adopts a distributed storage architecture, and data is synchronized between the local storage unit and the cloud server to ensure data security and persistence.

[0169] Data processing and analysis:

[0170] Layered signal processing: The data processing unit first performs layered processing on the received EEG signals. The first layer uses digital signal processing (DSP) technology to filter out noise and correct signals. The second layer uses feature extraction algorithms to identify key EEG and EEG features, such as the QRS complex in the EEG and alpha and beta waves in the EEG. The third layer uses pattern recognition and event detection algorithms to identify abnormal behaviors, such as arrhythmias or epileptic seizures.

[0171] Big Data Analysis and Prediction: Integrating a big data analysis module, the system conducts in-depth analysis of historical data and uses machine learning algorithms to identify users' electricity usage patterns and potential health risks. Using predictive models, the system proactively identifies potential health issues, such as high-risk arrhythmias, and generates personalized health reports.

[0172] Diagnostic results and report generation:

[0173] Diagnosis and Alerts: The system generates diagnostic results in real time and automatically triggers alerts based on the analysis results. Alert information is sent to users or medical personnel via Push Notification Services to ensure timely processing.

[0174] Health Reports: The system combines historical data to generate detailed health reports, including trend analysis, abnormal event records, and health recommendations. These reports can be viewed and downloaded through the remote monitoring system for users and doctors to refer to.

[0175] S3: Remote Monitoring and Maintenance

[0176] Real-time monitoring and status reporting:

[0177] Remote Monitoring System: Managers can monitor the status of all data acquisition devices in real time through the remote monitoring system. The system displays real-time data waveforms and device operating status. Managers can view detailed device information such as battery level, signal strength, and network connection status through a multi-terminal visual interface.

[0178] Remote Diagnosis: Through the remote diagnostic unit, managers can view the device's operating logs and diagnose the device's health, including the contact status of the electrode pads, the stability of the wire connections, and the quality of data transmission. The system can detect and flag potential problems and provide corresponding solutions or maintenance recommendations.

[0179] Device maintenance and firmware upgrades:

[0180] Firmware Upgrades and Configuration Adjustments: The platform supports regular firmware updates via Over-the-Air (OTA) technology, ensuring device functionality and security remain up-to-date. Administrators can remotely adjust device configurations, such as data collection frequency and transmission interval, to suit different environments or user needs.

[0181] Preventive Maintenance Plan: Based on equipment operating history and diagnostic results, the platform automatically generates a preventive maintenance plan. This plan includes regular inspections, data backups, and equipment restarts, aiming to extend equipment life and reduce unexpected failures. Managers can regularly review the implementation of the maintenance plan through the remote monitoring system and make adjustments as needed.

[0182] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cardio-encephalogram (ECG) acquisition and detection system, characterized in that: include: Wearable acquisition device module: used to collect EEG signals in real time. The device integrates electrode placement, flexible electrodes, intelligent fitting, wire management, and fault detection functions, and has comprehensive functions of signal acquisition, storage, communication, and power management. Data transmission module: responsible for transmitting the collected EEG signals to the central processing unit via a wireless network. This module includes low-power wireless communication, dual-channel data transmission, signal optimization, and security management functions; Central processing module: used to receive, process and store EEG data transmitted from wearable devices, perform real-time analysis and diagnosis, and generate analysis reports and abnormality alerts. This module includes layered signal processing, cloud computing, edge processing and data storage functions; Remote monitoring and feedback system: allows managers to monitor and manage device status in real time through a user interface. The system has data display, remote control, intelligent alarm and firmware upgrade functions.

2. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The wearable acquisition device module includes: Multi-layer fabric design: The outer layer is wear-resistant and breathable material, the middle layer is conductive fabric with embedded electrodes, and the inner layer is skin-friendly fabric to reduce friction and improve wearing comfort; Electrode placement and installation: The electrodes are embedded inside the fabric and precisely arranged to cover multiple signal collection points on the chest and head. The wires are managed through slots in the fabric to reduce external interference. Intelligent fit and positioning: Includes embedded sensors that monitor the contact status between the electrode and the skin in real time and automatically adjust the fit to optimize the pressure distribution of the electrode; Wire management and integration: Wires are managed by embedded cloth grooves to avoid entanglement or pulling, elastic materials are used to maintain wire tension, and quick-plug interfaces are used at wire connections; Fault detection and self-test unit: monitors the operating status of the equipment in real time, detects the connection between the electrodes and wires, automatically triggers the self-test process and generates alarm information.

3. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The data transmission module includes: Low-power wireless transmission unit: uses Bluetooth Low Energy and Wi-Fi 6 technologies, with dynamic power consumption management function to extend device usage time; Dual-channel data transmission unit: supports active and standby dual-channel data transmission. The active channel is used for real-time data transmission, and the standby channel is automatically activated when the active channel is interrupted, ensuring the continuity and reliability of data transmission. Signal optimization unit: adjusts communication parameters according to the current network status, including power control, frequency selection, and channel adjustment to optimize data transmission quality; Security Management Unit: Implements data encryption, authentication, and access control, and supports real-time monitoring of communication security and vulnerability remediation.

4. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The central processing module includes: Hierarchical signal processing unit: uses a multi-core processor to perform hierarchical signal processing, including noise filtering, feature extraction, pattern recognition, and event detection, ensuring efficient and real-time signal processing; Cloud computing and edge processing unit: With cloud computing and edge processing capabilities, complex computing tasks are completed in the cloud, and tasks with high real-time requirements are processed on edge devices; Data storage and log management unit: adopts a distributed storage architecture, supports local storage and cloud synchronization, automatically generates operation logs, and records detailed information during data collection, processing, and transmission; Intelligent diagnosis and alarm unit: It has multiple built-in medical diagnosis algorithms, analyzes EEG signals in real time, and automatically triggers alarms to generate analysis reports and alarm notifications.

5. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The remote monitoring and feedback system includes: Multi-terminal support and data visualization unit: supports access from multiple terminals, including mobile phones, tablets, and computers, and can display the waveforms and historical data of EEG signals in real time, providing multiple views for doctors to analyze; Remote control unit: supports remote configuration, diagnosis and maintenance of the device through the data management platform, including collection frequency adjustment, reporting time setting and device restart functions; Intelligent alarm and event processing unit: monitors the operating status of the equipment in real time, automatically triggers an alarm when an anomaly is detected, generates a processing flow to notify relevant personnel, and records the processing process and results; Firmware upgrade unit: supports remote firmware upgrades and automatically pushes updates over the network to keep device functions up to date and fix known vulnerabilities and errors.

6. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The central processing module also includes: Big Data Analysis Unit: This unit uses machine learning algorithms to conduct in-depth analysis of EEG data, uncovering potential health risks and behavioral patterns. Based on these analysis results, it optimizes diagnostic models and continuously improves the system's detection accuracy. Prediction model unit: This unit builds and updates the patient's personalized health prediction model based on the collected electrocardioencephalogram signal data. It can identify potential health problems in advance and make intervention recommendations. The prediction model will be automatically updated and adjusted according to the newly collected data.

7. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 2, characterized in that: The wear-resistant and breathable material of the outer layer includes nylon, polyester fiber, and elastic spandex; the conductive fabric of the middle layer includes conductive silver fiber fabric, conductive carbon fiber fabric, and conductive polymer; and the skin-friendly fabric of the inner layer includes pure cotton, bamboo fiber, and modal.

8. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The central processing module also includes: Heart-brain coupling analysis unit: By analyzing the time series correlation and frequency domain characteristics of ECG signals and EEG signals, the synergistic pattern between heart and brain activities is extracted to evaluate the function of the autonomic nervous system or the state of psychological stress. The analysis unit has a built-in algorithm based on time-frequency analysis and causality.

9. The electrocardioencephalogram (ECG) acquisition and detection system according to claim 1, characterized in that: The intelligent fitting and positioning also includes: Temperature and humidity adaptive adjustment unit: monitors the skin surface status through embedded temperature and humidity sensors, and dynamically adjusts the fitting pressure and conductivity of the electrode sheet to adapt to signal acquisition requirements under different temperature and humidity conditions.

10. A method for collecting and detecting electrocardiogram (ECG) signals, used in the electrocardiogram (ECG) signal collection and detection system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: Data collection and reporting: The wearable acquisition device module periodically collects electrocardiogram (ECG) data at preset intervals through the data acquisition unit. The data includes but is not limited to transient potential changes and waveform characteristics. The collected data is uploaded to the central processing unit through the data transmission module. If the network signal is poor, the data will be temporarily stored in the storage unit and uploaded again after the signal is restored. It supports breakpoint resuming. S2: Data Analysis and Diagnosis: After the central processing unit receives and stores the data, the data processing unit begins to analyze the EEG signals, identifying patterns and abnormal behaviors, and combines the big data analysis module to conduct in-depth analysis and prediction; Generate diagnostic results and generate health reports or alarm notifications based on historical data; S3: Remote monitoring and maintenance: Managers monitor the status of all acquisition devices in real time through the remote monitoring system, use remote diagnostic units to check the health of devices, and analyze operation logs; The platform supports regular push of firmware updates, adjustment of device configurations, and generation of preventive maintenance plans.

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