Sleep monitoring system for real-time monitoring and early warning of lead falling

Abstract

The invention relates to the field of medical equipment and health monitoring, and discloses a sleep monitoring system for lead shedding real-time monitoring and early warning, comprising: an intelligent material lead electrode for fitting the skin and providing high conductivity; the micro air pressure adjusting module is used for adjusting the attaching force through air pressure to guarantee tight contact between the electrode and the skin; the multi-sensor fusion module is used for monitoring the contact state between the electrode and the skin in real time; the microcontroller is used for processing a sensor signal and detecting a lead state; and the signal processing and fusing module is used for processing and fusing various sensor signals in real time and detecting a lead state. Accurate monitoring and dynamic adjustment are achieved through multi-sensor fusion and a deep learning algorithm, the detection precision and the system adaptability are improved, real-time data transmission and user reminding are ensured through the wireless transmission and vibration feedback module, the monitoring efficiency is improved, the equipment endurance is prolonged through the efficient energy management module, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment and health monitoring, and in particular to a sleep monitoring system for real-time monitoring and early warning of lead detachment. Background Art

[0002] In the field of modern medicine and health monitoring, ECG monitoring equipment plays an important role in clinical diagnosis, home health monitoring and scientific research. Electrocardiogram (ECG) helps doctors diagnose a variety of heart diseases such as arrhythmia and myocardial infarction by recording the electrical activity of the heart. It is a non-invasive, real-time monitoring method. Especially in sleep monitoring systems, ECG monitoring can provide comprehensive sleep quality analysis and heart health information.

[0003] However, in practical applications, ensuring stable contact between the lead electrodes and the skin is a major challenge faced by ECG monitoring equipment. The lead electrodes are prone to poor contact due to the user's movement, changes in body position and changes in skin condition, which in turn affects the quality of the ECG signal. Existing technologies usually rely on a single sensor (such as a resistance sensor) to detect the contact status between the electrode and the skin, but this method is easily interfered by the external environment and user behavior, resulting in frequent misjudgments and missed judgments. A single sensor is difficult to provide comprehensive contact status information, which affects the stability and accuracy of the ECG signal.

[0004] In terms of data processing, traditional static algorithms lack self-learning and dynamic adjustment capabilities, usually relying on preset thresholds and rules and unable to adjust according to actual usage. This method is particularly inadequate in long-term monitoring, where the contact state between the electrode and the skin may change frequently, and static algorithms are difficult to adjust and adapt in time, resulting in low system robustness and detection accuracy.

[0005] Therefore, in view of the above problems, the present invention proposes a sleep monitoring system for real-time monitoring and early warning of lead detachment. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a sleep monitoring system with real-time monitoring and early warning of lead detachment, which solves the problems of misjudgment of single sensor technology, lack of adaptability of static algorithms, poor real-time data transmission and high energy consumption in existing ECG monitoring equipment.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A sleep monitoring system for real-time monitoring and early warning of lead detachment, comprising:

[0008] Smart material lead electrodes, designed to fit the skin and provide high conductivity;

[0009] Micro air pressure regulating module, used to adjust the fit force through air pressure to ensure close contact between the electrode and the skin;

[0010] Multi-sensor fusion module, used to monitor the contact status between electrodes and skin in real time;

[0011] A microcontroller for processing sensor signals and detecting lead status;

[0012] Signal processing and fusion module, used to process and fuse multiple sensor signals in real time and detect lead status;

[0013] A wireless transmission module for transmitting monitoring data and alarm information to a monitoring device;

[0014] Batteries and energy harvesting modules to provide the power required by the system and extend the battery life of the device;

[0015] Real-time signal processing algorithm to analyze multi-sensor signals to determine if leads have fallen off;

[0016] Deep learning algorithm for intelligent analysis and judgment of lead status;

[0017] An early warning system is used to immediately send out an early warning signal when it detects that the lead is about to fall off;

[0018] Integrated vibration feedback module to remind users when the lead is about to fall off;

[0019] Data recording and analysis module, used to record ECG signals and lead status, and perform long-term data analysis;

[0020] User interface for displaying ECG signals, lead status, and warning information on a mobile device or monitoring device.

[0021] Preferably, the multi-sensor fusion module includes a resistance sensor, an optical sensor, a temperature sensor and a nano-pressure sensor. The resistance sensor is used to monitor the resistance change between the electrode and the skin in real time, the optical sensor is used to monitor the light reflection change of the skin contact, the temperature sensor is used to detect the temperature change of the skin surface, and the nano-pressure sensor is used to sense the contact pressure change between the electrode and the skin.

[0022] Preferably, the deep learning algorithm includes a convolutional neural network and a recursive neural network model, which is used to take multi-sensor data as multi-dimensional input, extract features using a convolutional neural network, and then capture time series changes through a recursive neural network to achieve high-precision detection of lead-off.

[0023] Preferably, the smart material lead electrode has a built-in resistance sensor and a nano-pressure sensor.

[0024] Preferably, the multi-sensor fusion module sets monitoring rules and thresholds, including:

[0025] Resistance threshold R threshold , when the resistance R exceeds the threshold, it is judged that the lead is off or has poor contact;

[0026] Reflected light intensity change threshold ΔI threshold , when the reflected light intensity change ΔI exceeds the threshold, it is judged that the lead is off or in poor contact;

[0027] Temperature change threshold ΔT threshold , when the temperature change ΔT exceeds the threshold, it is judged that the lead is off or in poor contact;

[0028] Pressure change threshold ΔP threshold When the pressure change ΔP exceeds the threshold, it is judged that the lead is off or in poor contact.

[0029] Preferably, the smart material lead electrode is made of shape memory alloy and graphene material.

[0030] Preferably, the battery and energy harvesting module include a thin-film lithium battery, a flexible solar cell and a thermoelectric energy harvester, which are used to utilize environmental energy to extend the battery life of the device.

[0031] Preferably, the intelligent early warning system includes:

[0032] Multi-layer intelligent detection algorithm for analyzing and fusing multiple sensor data;

[0033] Sensor data processing, used to collect, preprocess and fuse sensor data

[0034] The real-time monitoring and early warning module is used to monitor the lead status in real time and immediately issue an early warning signal when an abnormality is detected.

[0035] Preferably, the multi-layer intelligent detection algorithm includes a real-time signal processing layer, a deep learning layer, and a dynamic threshold adjustment layer.

[0036] Preferably, the dynamic adjustment layer includes:

[0037] Static threshold setting:

[0038] Initially set the static threshold of each sensor, including the resistance threshold R threshold , reflected light intensity change threshold ΔI threshold , Temperature change threshold ΔT threshold and pressure change threshold ΔP threshold ;

[0039] Dynamic adjustment mechanism:

[0040] Based on the output of the deep learning model, the threshold of each sensor is adjusted in real time;

[0041] Adopt a feedback mechanism to adjust the threshold using past warning results, including true positives, false positives, true negatives, and false negatives;

[0042] The update threshold formula is: NewThreshold=CurrentThreshold+α×(TruePositiveRate-FalsePositiveRate), where α is the learning rate parameter.

[0043] Working principle: When the user wears and starts the sleep monitoring system for real-time monitoring and warning of lead detachment, the system will first initialize and self-check to ensure that all modules are working properly. The smart material lead electrode fits tightly to the skin, and the micro air pressure adjustment module dynamically adjusts the electrode's fit to ensure its stability and comfort.

[0044] Subsequently, the multi-sensor fusion module starts to collect resistance, optical, temperature and pressure data in real time, which are pre-processed and formatted by the microcontroller. The signal processing and fusion module extracts and fuses the key features of these data, such as resistance change rate, light reflection intensity change rate, temperature change rate and pressure change rate, and passes the fused data to the deep learning algorithm module.

[0045] The deep learning algorithm module uses convolutional neural network (CNN) and recurrent neural network (RNN) models to analyze the fused data. The CNN model extracts spatial features, and the RNN model captures time series changes, thereby intelligently judging the lead status. The analysis results are passed to the intelligent early warning system, which conducts a comprehensive evaluation through a multi-layer intelligent detection algorithm and dynamically adjusts the monitoring threshold of each sensor based on the output results of the deep learning model and past early warning results.

[0046] When the early warning system determines that the lead is about to fall off, it immediately sends out an early warning signal, and the integrated vibration feedback module reminds the user to make adjustments through vibration. At the same time, the data recording and analysis module records the ECG signal and lead status data, and generates health reports and trend analysis. The user interface displays the ECG signal, lead status and early warning information in real time on the mobile device or monitoring device. The user can view historical data and analysis reports to obtain personalized health advice. Finally, the system transmits the monitoring data and alarm information to the remote monitoring device or cloud in real time through the wireless transmission module for doctors or guardians to view.

[0047] The present invention provides a sleep monitoring system for real-time monitoring and early warning of lead detachment. It has the following beneficial effects:

[0048] 1. The present invention uses a multi-sensor fusion module, including a resistance sensor, an optical sensor, a temperature sensor and a nano-pressure sensor, to monitor the contact status between the electrode and the skin in real time. Compared with the use of a single sensor, the multi-sensor fusion technology provides a more comprehensive and accurate contact status monitoring, can more effectively detect and judge the lead status, and solves the problem of misjudgment prone to traditional single sensor technology.

[0049] 2. The present invention is combined with a deep learning algorithm to process and analyze multi-dimensional sensor data and accurately detect lead detachment. The introduction of the deep learning algorithm not only improves the accuracy and robustness of detection, but also uses a dynamic threshold adjustment mechanism and a feedback mechanism to continuously optimize the threshold setting of the sensor, further improving the system's adaptability and detection accuracy.

[0050] 3. The wireless transmission module of the present invention ensures the real-time transmission of monitoring data and alarm information. Combined with the integrated vibration feedback module, it can promptly remind the user when the lead is about to fall off. Compared with traditional equipment, the real-time data transmission and feedback mechanism of the present invention improves the monitoring efficiency and user interactivity, ensuring that the user can take timely measures to avoid accidents.

[0051] 4. The battery and energy harvesting module of the present invention realizes efficient energy management by integrating thin-film lithium batteries, flexible solar cells and thermoelectric energy harvesters, and uses environmental energy to extend the battery life of the equipment. This not only improves the convenience of using the equipment, but also reduces the frequency of battery replacement and maintenance costs, solving the problem of high energy consumption of traditional equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a framework diagram of the system of the present invention;

[0053] Figure 2 It is a connection diagram of the system modules of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example:

[0056] Please see attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a sleep monitoring system for real-time monitoring and early warning of lead detachment, comprising:

[0057] Smart material lead electrodes, designed to fit the skin and provide high conductivity;

[0058] Micro air pressure regulating module, used to adjust the fit force through air pressure to ensure close contact between the electrode and the skin;

[0059] Multi-sensor fusion module, used to monitor the contact status between electrodes and skin in real time;

[0060] A microcontroller for processing sensor signals and detecting lead status;

[0061] Signal processing and fusion module, used to process and fuse multiple sensor signals in real time and detect lead status;

[0062] A wireless transmission module for transmitting monitoring data and alarm information to a monitoring device;

[0063] Batteries and energy harvesting modules to provide the power required by the system and extend the battery life of the device;

[0064] Real-time signal processing algorithm to analyze multi-sensor signals to determine if leads have fallen off;

[0065] Deep learning algorithm for intelligent analysis and judgment of lead status;

[0066] An early warning system is used to immediately send out an early warning signal when it detects that the lead is about to fall off;

[0067] Integrated vibration feedback module to remind users when the lead is about to fall off;

[0068] Data recording and analysis module, used to record ECG signals and lead status, and perform long-term data analysis;

[0069] User interface for displaying ECG signals, lead status, and warning information on a mobile device or monitoring device.

[0070] Specifically, the sleep monitoring system for real-time lead detachment monitoring and early warning of the present invention realizes its functions through the following steps:

[0071] The smart material lead electrodes fit tightly to the skin and collect ECG signals through their high conductivity.

[0072] The micro air pressure regulating module adjusts the air pressure as needed to ensure close contact between the electrode and the skin and improve signal quality.

[0073] The multi-sensor fusion module integrates resistance sensors, optical sensors, temperature sensors and nano-pressure sensors to monitor the contact status between the electrode and the skin in real time and transmit the data to the microcontroller.

[0074] The microcontroller processes the sensor signals and determines the lead status through real-time signal processing algorithms and deep learning algorithms.

[0075] The signal processing and fusion module combines multiple sensor signals for comprehensive analysis to detect whether the leads have fallen off, and transmits data and alarm information to the monitoring device through the wireless transmission module.

[0076] When the early warning system detects that the lead is about to fall off, it immediately sends out an early warning signal and, at the same time, integrates a vibration feedback module to remind the user.

[0077] The data recording and analysis module records ECG signals and lead status, performs long-term data analysis, and displays relevant information on a mobile device or monitoring device through a user interface.

[0078] Batteries and energy harvesting modules provide the power required by the system and extend the device's battery life through flexible solar cells and thermoelectric energy harvesters.

[0079] The multi-sensor fusion module includes a resistance sensor, an optical sensor, a temperature sensor and a nano-pressure sensor. The resistance sensor is used to monitor the resistance change between the electrode and the skin in real time, the optical sensor is used to monitor the light reflection change of the skin contact, the temperature sensor is used to detect the temperature change of the skin surface, and the nano-pressure sensor is used to sense the contact pressure change between the electrode and the skin.

[0080] Specifically, the multi-sensor fusion module integrates resistance sensors, optical sensors, temperature sensors and nano-pressure sensors to achieve comprehensive monitoring of the contact status between electrodes and skin. The specific working principle is as follows:

[0081] Resistance sensor: monitors the resistance change between the electrode and the skin in real time. When the electrode is in good contact with the skin, the resistance is small; when the contact is poor or falls off, the resistance increases significantly. The specific implementation is as follows:

[0082] Installed in the electrode, it measures the resistance between the electrode and the skin in real time.

[0083] When the electrode is in good contact with the skin, the resistance is low; when the contact is poor or falls off, the resistance increases significantly.

[0084] The data is transmitted to the microcontroller via a resistive sensor interface.

[0085] Optical sensor: monitors changes in light reflection from skin contact. Changes in skin contact conditions will cause changes in light reflection intensity, and the optical sensor can detect these changes. The specific implementation is as follows:

[0086] Mounted around the electrodes, a light source and light receiver are used to monitor the intensity of light reflected from the skin.

[0087] When the electrode is in good contact with the skin, the light reflection intensity remains stable; when the contact is poor or falls off, the light reflection intensity changes.

[0088] The light intensity variation data collected by the optical sensor is transmitted to the microcontroller.

[0089] Temperature sensor: detects temperature changes on the skin surface. When the electrode contacts the skin, the temperature is relatively stable; when the contact is poor or falls off, the temperature may change. The specific implementation is as follows:

[0090] Integrated into the electrode, it measures the temperature where the electrode contacts the skin.

[0091] When the electrode is in contact with the skin, the temperature is relatively stable; when the contact is poor or falls off, the temperature changes greatly.

[0092] The data from the temperature sensor is transmitted to the microcontroller in real time.

[0093] Nano pressure sensor: senses the change in contact pressure between the electrode and the skin. The change in contact pressure reflects the change in the degree of fit between the electrode and the skin. The specific implementation is as follows:

[0094] Embedded in the electrode, it senses the contact pressure between the electrode and the skin.

[0095] When the electrode is in close contact with the skin, the pressure value is high and stable; when the contact is poor or falls off, the pressure value changes significantly.

[0096] The data from the pressure sensor is transmitted to the microcontroller via an interface.

[0097] The multi-sensor fusion module comprehensively analyzes the data from the above sensors and provides accurate electrode contact status information.

[0098] The deep learning algorithm includes convolutional neural network and recursive neural network models, which are used to take multi-sensor data as multi-dimensional input, extract features using convolutional neural network, and then capture time series changes through recursive neural network to achieve high-precision detection of lead-off.

[0099] Specifically, data preprocessing: standardize the multi-dimensional data from resistance sensors, optical sensors, temperature sensors and nano-pressure sensors.

[0100] The standardized data is integrated into a multi-dimensional input matrix and input into CNN.

[0101] Convolutional Neural Network (CNN): Design multiple convolutional layers, each layer contains several convolution kernels, perform convolution operations on the input data, and extract spatial features.

[0102] After several convolution and pooling operations, the feature map is obtained.

[0103] Use activation function (ReLU) to perform nonlinear transformation on feature maps.

[0104] Recurrent Neural Networks (RNN):

[0105] The feature map extracted by CNN is used as the input of RNN.

[0106] Design multiple RNN units (LSTM or GRU) to process time series data through their internal loop structure and capture the changes of features over time.

[0107] RNN state update formula: h t =σ(W h h t-1 +W x X t +b)

[0108] Comprehensive judgment: The final state of the RNN output passes through the fully connected layer and outputs the prediction result of the lead state.

[0109] The prediction results are converted into probability distribution through the Softmax function to determine whether the lead is off.

[0110] Model training and optimization: Use historical data to train the CNN-RNN model and use the cross-entropy loss function (Cross-EntropyLoss) to evaluate the model performance:

[0111]

[0112] Among them, y i is the true label, is the model predicted probability.

[0113] Use the optimization algorithm (Adam) to update parameters, minimize the loss function, and improve the accuracy of the model.

[0114] Smart material lead electrodes embedded with resistance sensors and nano-pressure sensors.

[0115] Smart material lead electrodes are made of shape memory alloy and graphene materials.

[0116] Specifically, shape memory alloys have the ability to memorize their shape and can return to a preset shape according to temperature changes or external forces, ensuring that the electrode fits tightly to the skin. Its high elasticity and flexibility enable the electrode to adapt to changes in the surface shape of the skin.

[0117] Graphene has extremely high conductivity and flexibility, and can provide excellent electrical signal conduction performance, while reducing the stimulation of electrodes to the skin and improving wearing comfort.

[0118] The multi-sensor fusion module sets monitoring rules and thresholds, including:

[0119] Resistance threshold R threshold , when the resistance R exceeds the threshold, it is judged that the lead is off or has poor contact;

[0120] Reflected light intensity change threshold ΔI threshold , when the reflected light intensity change ΔI exceeds the threshold, it is judged that the lead is off or in poor contact;

[0121] Temperature change threshold ΔT threshold , when the temperature change ΔT exceeds the threshold, it is judged that the lead is off or in poor contact;

[0122] Pressure change threshold ΔP threshold When the pressure change ΔP exceeds the threshold, it is judged that the lead is off or in poor contact.

[0123] Specifically, the multi-sensor fusion module can accurately determine the contact status between the electrode and the skin by setting specific monitoring rules and thresholds. The data of each sensor is evaluated by the preset thresholds. When the data exceeds these thresholds, the system will determine that the electrode is in poor contact or has fallen off. The specific working principle is as follows:

[0124] Resistance threshold: The resistance sensor monitors the resistance between the electrode and the skin in real time. When the measured resistance RR exceeds the preset resistance threshold, the system determines that the electrode is in poor contact or has fallen off.

[0125] Reflected light intensity change threshold: The optical sensor monitors the change in the intensity of light reflected from the skin. When the reflected light intensity change ΔI exceeds the set threshold, the system determines that the electrode is in poor contact or has fallen off.

[0126] Temperature change threshold: The temperature sensor detects the temperature change on the skin surface. When the temperature change ΔT exceeds the set threshold, the system determines that the electrode is in poor contact or has fallen off.

[0127] Pressure change threshold: The nano pressure sensor senses the contact pressure change between the electrode and the skin. When the pressure change ΔP exceeds the set threshold, the system determines that the electrode is in poor contact or has fallen off.

[0128] By setting specific thresholds, the multi-sensor fusion module can accurately determine the contact status of the electrodes, reducing misjudgments and missed judgments. It also combines multi-dimensional data of resistance, light reflection, temperature and pressure to provide more comprehensive contact status monitoring. The sensor monitors and evaluates data in real time to ensure rapid response and early warning when the lead falls off or the contact is poor.

[0129] Battery and energy harvesting modules include thin-film lithium batteries, flexible solar cells, and thermoelectric energy harvesters, which are used to use ambient energy to extend device life.

[0130] Specifically, thin-film lithium batteries serve as the main power source, providing stable power supply for equipment operation.

[0131] The film design makes it flexible and can fit the curve of the human body, increasing wearing comfort.

[0132] Flexible solar cells use ambient light (both natural and artificial) to convert light energy into electrical energy.

[0133] The flexible design allows it to fit the surface of the device without affecting the appearance and comfort of use of the device.

[0134] Solar cells generate electricity through the photovoltaic effect.

[0135] Thermoelectric energy harvester: uses the temperature difference between the wearer's body temperature and the ambient temperature to generate electricity through the thermoelectric effect.

[0136] By converting temperature differences into electrical energy, it supplements the power needs of the equipment.

[0137] The intelligent early warning system includes:

[0138] Multi-layer intelligent detection algorithm for analyzing and fusing multiple sensor data;

[0139] Sensor data processing, used to collect, preprocess and fuse sensor data

[0140] The real-time monitoring and early warning module is used to monitor the lead status in real time and immediately issue an early warning signal when an abnormality is detected.

[0141] The multi-layer intelligent detection algorithm includes a real-time signal processing layer, a deep learning layer, and a dynamic threshold adjustment layer.

[0142] Dynamic adjustment layers include:

[0143] Static threshold setting:

[0144] Initially set the static threshold of each sensor, including the resistance threshold R threshold , reflected light intensity change threshold ΔI threshold , Temperature change threshold ΔT threshold and pressure change threshold ΔP threshold ;

[0145] Dynamic adjustment mechanism:

[0146] Based on the output of the deep learning model, the threshold of each sensor is adjusted in real time;

[0147] Adopt a feedback mechanism to adjust the threshold using past warning results, including true positives, false positives, true negatives, and false negatives;

[0148] The update threshold formula is: NewThreshold=CurrentThreshold+α×(TruePositiveRate-FalsePositiveRate), where α is the learning rate parameter.

[0149] Specifically, multi-layer intelligent detection algorithm:

[0150] Real-time signal processing layer: Process sensor data in real time, use filtering algorithms to remove noise, and extract key features.

[0151] The initial data of each sensor is standardized to ensure the consistency and comparability of the data.

[0152] Deep learning layer: Design a convolutional neural network (CNN) to extract spatial features of multi-dimensional sensor data. Design a recurrent neural network (RNN) to capture time series changes. Combine CNN and RNN models to perform in-depth analysis and fusion of sensor data.

[0153] Dynamic Threshold Adjustment Layer:

[0154] Static threshold setting: Initially set the static threshold of each sensor.

[0155] Dynamic adjustment mechanism:

[0156] Based on the output results of the deep learning model, the threshold of each sensor is adjusted in real time.

[0157] Use a feedback mechanism to adjust the threshold based on past warning results (true positive, false positive, true negative, false negative).

[0158] Sensor data processing:

[0159] Data acquisition: Use high-precision sensors (resistance sensors, optical sensors, temperature sensors, and nano-pressure sensors) to acquire data in real time.

[0160] Data preprocessing: Standardize, filter and denoise sensor data to ensure data quality and consistency.

[0161] Data fusion: Input the preprocessed data into the multi-layer intelligent detection algorithm for comprehensive analysis.

[0162] Real-time monitoring and early warning module:

[0163] Real-time monitoring: Utilize multi-layer intelligent detection algorithms to analyze lead status in real time and determine whether leads are falling off or in poor contact.

[0164] Early warning signal: When an abnormal lead status is detected, an early warning signal is immediately sent to the user through the vibration feedback module and the wireless transmission module.

[0165] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sleep monitoring system for real-time monitoring and early warning of lead detachment, characterized in that: include: Smart material lead electrodes, designed to fit the skin and provide high conductivity; Micro air pressure regulating module, used to adjust the fit force through air pressure to ensure close contact between the electrode and the skin; Multi-sensor fusion module, used to monitor the contact status between electrodes and skin in real time; A microcontroller for processing sensor signals and detecting lead status; Signal processing and fusion module, used to process and fuse multiple sensor signals in real time and detect lead status; A wireless transmission module for transmitting monitoring data and alarm information to a monitoring device; Batteries and energy harvesting modules to provide the power required by the system and extend the battery life of the device; Real-time signal processing algorithm to analyze multi-sensor signals to determine if leads have fallen off; Deep learning algorithm for intelligent analysis and judgment of lead status; An early warning system is used to immediately send out an early warning signal when it detects that the lead is about to fall off; Integrated vibration feedback module to remind users when the lead is about to fall off; Data recording and analysis module, used to record ECG signals and lead status, and perform long-term data analysis; User interface for displaying ECG signals, lead status, and warning information on a mobile device or monitoring device.

2. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The multi-sensor fusion module includes a resistance sensor, an optical sensor, a temperature sensor and a nano-pressure sensor. The resistance sensor is used to monitor the resistance change between the electrode and the skin in real time, the optical sensor is used to monitor the light reflection change of the skin contact, the temperature sensor is used to detect the temperature change of the skin surface, and the nano-pressure sensor is used to sense the contact pressure change between the electrode and the skin.

3. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The deep learning algorithm includes a convolutional neural network and a recursive neural network model, which is used to take multi-sensor data as multi-dimensional input, extract features using a convolutional neural network, and then capture time series changes through a recursive neural network to achieve high-precision detection of lead-off.

4. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The smart material lead electrode has a built-in resistance sensor and a nano pressure sensor.

5. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The multi-sensor fusion module sets monitoring rules and thresholds, including: Resistance threshold R threshold , when the resistance R exceeds the threshold, it is judged that the lead is off or has poor contact; Reflected light intensity change threshold ΔI threshold , when the reflected light intensity change ΔI exceeds the threshold, it is judged that the lead is off or in poor contact; Temperature change threshold ΔT threshold , when the temperature change ΔT exceeds the threshold, it is judged that the lead is off or in poor contact; Pressure change threshold ΔP threshold When the pressure change ΔP exceeds the threshold, it is judged that the lead is off or in poor contact.

6. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The smart material lead electrode is made of shape memory alloy and graphene material.

7. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The battery and energy harvesting module includes a thin-film lithium battery, a flexible solar cell and a thermoelectric energy harvester, which is used to utilize environmental energy to extend the battery life of the device.

8. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 1, characterized in that: The intelligent early warning system comprises: Multi-layer intelligent detection algorithm for analyzing and fusing multiple sensor data; Sensor data processing, used to collect, preprocess and fuse sensor data The real-time monitoring and early warning module is used to monitor the lead status in real time and immediately issue an early warning signal when an abnormality is detected.

9. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 8, characterized in that: The multi-layer intelligent detection algorithm includes a real-time signal processing layer, a deep learning layer, and a dynamic threshold adjustment layer.

10. The sleep monitoring system for real-time monitoring and early warning of lead detachment according to claim 9, characterized in that: The dynamic adjustment layer includes: Static threshold setting: Initially set the static threshold of each sensor, including the resistance threshold R threshold , reflected light intensity change threshold ΔI threshold , Temperature change threshold ΔT threshold and pressure change threshold ΔP threshold ; Dynamic adjustment mechanism: Based on the output of the deep learning model, the threshold of each sensor is adjusted in real time; Adopt a feedback mechanism to adjust the threshold using past warning results, including true positives, false positives, true negatives, and false negatives; The update threshold formula is: NewThreshold=CurrentThreshold+α×(TruePositiveRate-FalsePositiveRate), where α is the learning rate parameter.

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