49 results about "Sleep analysis" patented technology

The invention discloses a sleep analysis system, method and device based on multi-mode PSG data and a medium, and relates to the technical field of artificial intelligence. The system comprises a data acquisition module used for acquiring multi-modal PSG data of a target object; the data processing module is used for preprocessing the multi-mode PSG data; the sleep recognition module is used for performing multi-dimensional prediction on the sleep condition of the target object according to the preprocessed multi-mode PSG data; wherein the sleep recognition module is provided with a sleep staging model for recognizing sleep stages, a breathing event recognition model for recognizing breathing event types and a micro-awakening recognition model for recognizing micro-awakening events; and the data statistics module is used for integrating and analyzing the preprocessed multi-modal PSG data and the multi-dimensional prediction result of the sleep recognition module to obtain a sleep analysis report of the target object. According to the scheme, the multi-mode PSG data is fully utilized to realize automatic joint identification of multiple sleep tasks in the same system.

This invention discloses a breathable, conductive hydrogel electrode, its preparation method, and its applications. The breathable, conductive hydrogel electrode comprises a porous fiber support structure and a hydrogel conductive network. The porous fiber support structure has a three-dimensional porous structure formed by interwoven porous fibers, each fiber possessing a three-dimensional interconnected channel structure formed by interconnected pores distributed on and within the porous fibers. The hydrogel conductive network is distributed on the surface and within the porous fiber support structure, forming a continuous ion conduction path within the electrode. The breathable, conductive hydrogel electrode provided by this invention has advantages such as high breathability, self-humidification, low impedance, and breathability, achieving high-fidelity, long-term acquisition of physiological signals such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It can be widely used in electrophysiological signal monitoring, smart wearables, sleep analysis, and human-computer interaction.

The present disclosure relates to a sleep analysis segment detection method and system, and specifically, a sleep analysis segment detection method according to an embodiment of the present disclosure, which is performed in a system including a layer unit, a detection unit, a memory unit, and a data analysis unit, may include detecting a user's biosignal from the detection unit provided in the layer unit; storing the user's biosignal detected by the detection unit in the memory unit; and analyzing the user's biosignal stored in the memory unit by the data analysis unit to detect the user's sleep analysis segment.

System and methods of a mobile application to remotely monitor residents with device-free sensing of human motion over Wi-Fi is provided. A detailed behavioral analysis of various functions related to the sleep and activity of the subject has been provided. The Remote Care Application (RCA) is a mobile application on several platforms that displays the results of all the analysis and behavioral patterns obtained from the motion from the subject. The system relies on existing wireless communication signals and machine learning techniques in order to automatically detect the motion of the subject, sleep analysis of the subject, activity analysis of the subject.

The application discloses an automatic sleep staging method, system, device and storage medium, wherein the method comprises the following steps: acquiring a polysomnogram of a sleeper; extracting physiological signal features from the polysomnogram; performing an adversarial enhancement process on the physiological signal features to obtain enhanced physiological signal features; learning high-order correlation relationships between multi-modal physiological signals based on the enhanced physiological signal features; performing multi-modal fusion according to the high-order correlation relationships; and obtaining sleep stage classification results. The application obtains physiological signal data with balanced information quantity through adversarial enhancement of physiological signals, and realizes automatic sleep state detection through data fusion, thereby improving the prediction accuracy. In addition, the high-order correlation between modalities is captured for the collected multi-modal physiological signal features, and the redundant information and correlation information of the multi-modal physiological signals are effectively utilized for feature fusion. The application can be widely applied to the technical field of sleep analysis.

The invention relates to the technical field of enuresis nursing equipment, and discloses a night intelligent urine monitoring and nursing wearable system for enuresis children, which comprises a wearable diaper unit, a physical sign monitoring module, an intelligent interaction terminal, a data fusion analysis module and an intervention strategy generation module. According to the invention, through multi-modal data fusion of the distributed capacitive humidity sensor array and physiological sign monitoring, high-precision capture and urine volume estimation of the whole enuresis event process are realized; on this basis, sleep analysis and historical data are combined to generate a dynamic and personalized hierarchical intervention strategy, so that the accuracy and effectiveness of alarm are improved, false alarm and missing alarm are reduced, and meanwhile, comprehensive and associated data support including urine parameters and physiological states is provided for clinical diagnosis; the comprehensive effects of auxiliary treatment and improvement of child sleep quality and nursing efficiency are achieved.

The present disclosure relates to systems and methods for improving analysis of sleep data by classifying users based on sleeper type. In particular, a wearable computing system can obtain a first set of motion sensor data for a user during a first time period from a motion sensor. The wearable computing system can determine a sleeper type for the user from a plurality of sleeper types based on the first set of motion sensor data received from the motion sensor. The wearable computing system can select a sleep analysis model from a plurality of sleep analysis models based on the sleeper type determined for the user. The wearable computing system can analyze a second set of motion sensor data from a second time period using the selected sleep analysis model to determine one or more sleep features for the user during the second time period.

The application discloses a simple method for mechanically depriving fruit flies of sleep, and comprises the following specific steps: S1, sleep baseline determination before sleep deprivation; S2, putting fruit flies which need to be deprived of sleep together with monitoring small tubes into large tubes, plugging the tube openings with sponges, and binding the tubes with rubber bands after determining the same sleep baseline; S3, mechanical oscillation for depriving fruit flies of sleep; S4, sleep monitoring; and S5, analyzing data through sleep analysis software. The application adds a rotating oscillation device on the basis of an original fruit fly behavior monitoring system, realizes short-term deprivation of fruit fly sleep, provides a method for studying acute sleep deprivation of fruit flies, and has the characteristics of simplicity, high efficiency, economy and practicability.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on data analysis, and the method comprises the steps: synchronously collecting a BCG original signal and electroencephalogram data, and determining a first sleep staging label according to the electroencephalogram data; determining a second sleep staging label according to the BCG original signal; determining an actual sleep staging tag in combination with the first sleep staging tag and the second sleep staging tag; determining sleep characterization states according to the actual sleep stage labels of the users, wherein the sleep characterization states comprise a sleep consistent state and a sleep inconsistent state; and determining a plurality of users for sleep analysis according to the sleep characterization state and the actual sleep staging label of each user so as to perform sleep feature analysis on each user. According to the method, the sleep characterization state is determined through statistical analysis of the actual sleep staging labels of all the users, then the users needing sleep analysis are screened out, and effective management and personalized monitoring of group sleep are achieved.

The invention discloses a control method of a massage device for assisting sleep, which comprises the following steps: S1, collecting a multi-modal physiological signal of a user in a sleep process in real time through a non-contact or micro-contact sensor, and preprocessing the multi-modal physiological signal; s2, inputting the collected multi-modal physiological signals into a pre-trained sleep analysis model, and calculating and outputting sleep stage probability distribution and sleep stability index of the current user in real time; s3, dynamically generating an optimal massage control instruction according to the sleep stage probability distribution and the sleep stability index at the current moment and a preset intervention target library; and S4, recording physiological signal change and sleep stage evolution data before and after massage intervention, and continuously optimizing the sleep analysis model and the massage decision strategy in combination with the morning subjective sleep quality score of the user.

1. The name of the design product: sleep analysis graphical user interface of electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the content of the graphical user interface in the display screen panel. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the user interface of the product is the interactive interface of the smart sleep monitor applet. 6. The human-computer interaction mode of the graphical user interface: the display interface in the front view is the home page interface; the gray "data analysis" button below the front view can be clicked for interaction, and clicking the corresponding "data analysis" button switches to the interface of interface change state diagram 1 to display the sleep state statistical analysis interface of the day; the blue "sleep analysis" button above the interface change state diagram 1 can be clicked for interaction, and clicking the corresponding "sleep analysis" button switches to the interface of interface change state diagram 2 to enter the graphical analysis interface. 7. The "gray module" covering part in the figure is a variable figure, and the variable text is replaced by "X".

The embodiment of the invention provides a multi-modal data fusion sleep monitoring system and method based on collaboration. The system comprises a mobile detection terminal, a charging dock used for charging the mobile detection terminal and a cloud data platform, the mobile detection terminal only needs to collect physiological data, and the charging dock serves as data transfer and is selectively detachably connected with multiple detection modules to supplement the modality of detection data. The cloud data platform performs data analysis and fusion to obtain a sleep analysis result, so that comprehensive information data of various types / modalities can be obtained under the condition of limited cost, and meanwhile, the cloud data platform has strong data processing capability.

This invention discloses a sleep staging system based on multi-threshold neighborhood extreme value pattern statistics, comprising an EEG signal acquisition unit, a signal preprocessor, a multi-threshold neighborhood extreme value time-domain feature extractor, a gray wolf optimizer, and a random forest classifier. The preprocessing involves initial filtering and 30-second segmentation of selected EEG signals. Based on the duration of the single-channel EEG signal annotation, the single-channel EEG signals are divided into two categories: original signal segments and signal segments requiring data augmentation. Next, data augmentation is performed on specified segments, and the extreme values ​​of the augmented data segments are selected. Then, the neighborhood differences of the extreme values ​​are calculated, and multi-threshold and multi-state processing is performed on the 5-stage matrix obtained from these differences to derive the multi-threshold neighborhood extreme value pattern statistical code. The thresholds in the multi-threshold processing are determined by the gray wolf optimizer. Finally, the obtained feature matrix is ​​input into the random forest classifier. This invention enhances the automatic sleep staging capability based on EEG signal augmentation and designs and manufactures a complete system encompassing EEG acquisition and sleep analysis visualization.

The embodiment of the invention provides a single-channel electroencephalogram sleep analysis method and related equipment, and belongs to the technical field of electroencephalogram signal processing and sleep health monitoring. The method comprises the following steps: carrying out preprocessing and time sequence enhancement on a single-channel electroencephalogram signal; extracting local and global features by using a double-branch feature encoder; feature distribution alignment between a source domain and a target domain is realized through a double-contrast adversarial learning module in combination with cross-view time sequence contrast learning and conditional adversarial alignment; dynamically adjusting a target domain sample weight based on information gain through a learnable reweighting module so as to correct label distribution offset; and finally, high-precision sleep staging or OSAHS event detection is realized by utilizing a classifier. According to the method, the generalization ability and robustness of the model in a cross-subject scene are remarkably improved, accurate analysis can be achieved only through single-channel electroencephalogram signals, the hardware cost and the use threshold are reduced, and the method is suitable for scenes such as family sleep monitoring and telemedicine screening.

The invention belongs to the technical field of artificial intelligence, and relates to a data processing method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the alignment fusion processing of collected multi-modal data of a user, and obtaining processed data; performing sleep analysis on the processing data based on a sleep analysis model to obtain a sleep stage division result; performing data matching and statistics on the sleep stage division result and the environment data to obtain a stage labeling result; performing causal analysis on the stage labeling result based on a causal attribution model to generate a causal attribution result; comprehensively analyzing the causal attribution result and the individual feature information of the user to generate a target intervention strategy of the user; and executing corresponding sleep intervention processing on the user based on the target intervention strategy. In addition, the target intervention policy may be stored in the blockchain. The method can be applied to home-based care scenes in the field of medical science and technology, the accuracy of sleep detection is improved, and the fitness and accuracy of the generated intervention strategy are improved.

This invention provides a cognitive management method and system for insomnia with intelligent suggestions for bedtime. The method includes: collecting and preprocessing multi-dimensional user data to obtain standardized input data; training a sleep analysis model based on historical datasets and continuously optimizing the model using user feedback data; using the sleep analysis model to perform inference based on real-time user data for the day, dynamically generating and outputting a personalized sleep plan including suggested bedtimes; obtaining actual sleep details and calculating sleep quality indicators; conducting continuous risk monitoring based on sleep quality indicators and triggering tiered intervention suggestions; and feeding the sleep quality indicators and user feedback data as training data into the model optimization step to achieve closed-loop optimization. This invention solves the problems of rigid suggestions, lack of personalization, and insufficient continuous optimization capabilities in existing technologies, achieving precise, dynamic, and intelligent sleep management.

The invention relates to a method for monitoring a user's sleep in a motor vehicle (10), wherein a control device (22): activates a rest program of the motor vehicle (10) (S1) that assists the user while resting in the motor vehicle (10); detects that the user is resting or sleeping in the motor vehicle (10) (S2); and receives an occupant monitoring signal (S3) from a sensor device (14) of the motor vehicle (10), which describes at least one body state parameter that describes a state of one or more body parts and / or one or more body actions of the resting or sleeping user. Based on the occupant monitoring signal, the control device (22) provides a user-specific sleep analysis model (S6) that describes an overall state of the user's body and determines the sleep quality of the user's sleep (S7) based on the provided sleep analysis model.The control device (22) then generates a display signal (S8) that describes the information on the determined sleep quality and transmits this to a display device (30) of the motor vehicle (10) (S9).

The present invention provides a sleep staging analysis method and device based on electroencephalogram (EEG) signals, belonging to the field of data analysis technology. The method and device specifically include: using sleep staging results corresponding to different trusted monitoring positions to determine the staging deviation period in the sleep analysis results, determining the matching situation between the EEG signals of different trusted monitoring positions at different times and monitoring data of other dimensions, and combining the deviation between the EEG signals at different times and other trusted monitoring positions to determine the abnormal change time of different trusted monitoring positions. Based on the distribution data of the abnormal change time of different trusted monitoring positions in the staging deviation period and the period before the staging deviation period, when it is determined that a reference monitoring position exists in the staging deviation period, the reference monitoring position is used to determine the sleep staging result in the staging deviation period, thereby improving the accuracy of the sleep staging result.

The present application relates to a kind of low-frequency event fidelity decontamination system for sleep synchronization EEG-fMRI, belong to electroencephalogram signal processing technical field.Solve the problem of low-frequency event morphological characteristics of existing synchronization EEG-fMRI decontamination scheme under sleep scene.System includes EEG acquisition interface, auxiliary information input interface, processor, memory, result output interface and basic preprocessing module, low-frequency event reference construction module, low-frequency event sample generation module, decontamination model module, event fidelity training module, inference control module and quality control output module.System is by constructing low-frequency event reference, generates targeted training sample, uses event fidelity loss function to train decontamination model, implements differentiation correction control in inference stage, realizes suppression magnetic resonance related artifact while keeping low-frequency sleep event peak and valley position, amplitude characteristics and duration, improves subsequent sleep analysis and multimodal research reliability.

The invention discloses a sleep data acquisition method, a sleep data analysis method, a sleep data acquisition device and a sleep data processing system, and the method comprises the steps: dividing an acquired sleep signal according to a set length, and obtaining a plurality of sleep signal data segments; acquiring discrete cosine transform (DCT) data segments respectively corresponding to the multiple sleep signal data segments; extracting the DCT data segment in a set spectral range to obtain a key DCT data segment, the key DCT data segment comprising a plurality of frequency components; according to the energy corresponding to the multiple frequency components, key frequency components are extracted from the multiple frequency components to obtain sleep data used for sleep analysis, and the energy corresponding to the frequency components represents the intensity or contribution degree of the frequency components in the sleep signals. According to the invention, the accuracy of the sleep data for sleep analysis can be improved.

The application provides a sleep analysis method and device and electronic equipment, and relates to the technical field of health monitoring. The method comprises the following steps: acquiring a radar echo signal, the radar echo signal being an echo signal received after a radar device sends a radar signal to a target object; acquiring body movement information and a breathing phase signal of the target object according to the radar echo signal; and performing sleep staging identification according to the body movement information and the breathing phase signal to acquire sleep analysis data. In the embodiment of the application, the non-contact device is used to detect the radar echo signal, so that the user experience can be improved, the change of sleep depth can be reflected, the sleep analysis data can be acquired, errors that may occur when a single information is used for determination can be avoided, and the flexibility and accuracy of sleep analysis can be improved.

The present application relates to the technical field of sleep analysis, and discloses a sleep state analysis method for a wearable device and related components, including: obtaining acceleration-related data collected by an acceleration sensor, environmental variable-related data collected by an environmental sensor, and physiological sign-related data collected by a physiological sign sensor, wherein the acceleration-related data includes X-axis, Y-axis, and Z-axis acceleration sequences. Determine the maximum score value among the scores corresponding to the acceleration sequences of each axis within a preset time period. Determine the correlation coefficients between the maximum score value and the environmental variable-related data, as well as the physiological sign-related data, and analyze each correlation coefficient to determine the factors causing changes in the user's sleep state. Thus, based on the maximum score value among the scores corresponding to the acceleration sequences of the wearable device on each axis, analyze the correlation coefficients between the maximum score value and the environmental variable-related data, as well as the physiological sign-related data, to determine the factors causing changes in the user's sleep state, thereby improving the user experience.

A method comprising receiving data from a sleep sensor integrated in a mattress system, the sleep sensor coupled via a cord to a data collection unit, analyzing the data at the data collection unit, and providing results of the analyzed data to a user mobile device via a wireless connection, for display to the user.

The present invention is both a device and a method for verifying a subject's identity while using a medical device or undergoing a medical diagnostic or therapeutic procedure, particularly at home or at a remote location. The method and device can be used for inpatient and remote sleep and signal analysis with biometric identification. The present invention is further related to the devices and sensors used in executing the method, and includes various embodiments of a method of inpatient and remote sleep analysis.

This invention discloses a sleep analysis method that integrates spatiotemporal multi-scale features, comprising the following steps: Step 1, acquiring multi-channel raw physiological signal data from a polysomnography device and preprocessing it to obtain a standardized multi-channel signal data block of fixed length; Step 2, inputting the standardized multi-channel signal data block into a pre-trained deep learning feature extraction network, which includes parallel temporal feature extraction branches and spatial feature interaction branches, and outputting a multi-scale fused feature vector; Step 3, inputting the multi-scale fused feature vector into a pre-trained sleep event recognition model based on the Transformer architecture, which then outputs a sleep analysis report. This invention has the advantages of high efficiency, accuracy, and strong generalization.

1. The name of the design product: the mattress function selection graphical user interface (12) of an electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface content of the screen. 4. The picture or photo that best indicates the design points: the front view. 5. The use of the graphical user interface: for experiencing various personalized functions of a smart mattress. 6. The area of the graphical user interface in the product: the gray area of each figure is the deleted content screen, and "XX" represents text or numbers. 7. The human-computer interaction mode of the graphical user interface: the graphical user interface can realize human-computer interaction by touching the screen panel to load subsequent graphical user interfaces or run applications. 8. The change state description of the graphical user interface: "front view" is the main interface view, after clicking the "sleep analysis" control at the bottom of "front view", the interface changes to "change state figure 1"; after clicking the "month" control at the top of "change state figure 1", the interface changes to "change state figure 2".

The present invention discloses an automatic sleep staging system based on a deep neural network and a brain-computer interface, comprising: a data acquisition module for collecting and recording the user's EEG signals; a data preprocessing module for processing EEG signals; a staging model training module, which uses a deep neural network to construct an automatic sleep staging model, and uses the constructed automatic sleep staging model to perform feature representation, feature enhancement, and feature capture on single-channel EEG signals, and obtains the optimal automatic sleep staging model through multiple iterations; an automatic staging module, which uses the optimal automatic sleep staging model to perform fully automatic sleep stage discrimination on preprocessed sleep EEG samples; and a feedback module, which is used to feed back the results processed by the automatic staging module, output them in real time, and write them into the whole night's sleep analysis report. The present invention provides an end-to-end automatic sleep staging framework, which overcomes the inefficiency and tediousness of traditional manual staging and improves the classification accuracy of human sleep stages.

The invention relates to a monitoring and / or analysis device (24), in particular a sleep monitoring and / or sleep analysis device, with a sensor unit (10) comprising at least one contactless respiratory rhythm sensor (12) which is provided for remote detection of a respiratory rhythm of a living being (14). It is proposed that the respiratory rhythm sensor (12) is designed as a laser feedback interferometry (LFI) sensor.

The application relates to the technical field of sleep analysis, in particular to a sleep prediction method and system based on daytime brain electrical cross-frequency coupling and timing characteristics, which comprises the following steps: synchronously collecting daytime brain electrical signals and daytime skin electric response signals of a user in a daytime wake state; extracting timing characteristics of the daytime brain electrical signals to form a daytime brain electrical feature set representing daytime brain fatigue, cognitive load and emotional arousal; preprocessing the daytime skin electric response signals to extract a daytime skin electric feature set containing skin electric fluctuation amplitude and skin electric baseline level characteristics; fusing the daytime brain electrical feature set and the daytime skin electric feature set to generate a daytime neural state feature vector; and inputting the daytime neural state feature vector into a pre-trained deep learning prediction model to output a nighttime sleep prediction result of the user, thereby achieving significant improvement in prediction timeliness, objectivity and prediction accuracy.

The invention discloses a sleep-aiding music generation method and device based on multi-modal signal feedback, and the method comprises the steps: collecting the multi-modal physiological data of a target user in the sleep process, extracting and screening the target key features of the multi-modal physiological data, and carrying out the sleep analysis based on the target key features, obtaining a sleep quality scoring model of the target user; screening individual sleep-aiding audio materials of the target user through multiple rounds of structured audition in combination with physiological data feedback, and constructing a material library; extracting physiological feature parameters of the target user in a slow-wave sleep stage, and establishing a linear mapping model of the physiological feature parameters and music control parameters; and when identifying that the user is in the slow wave sleep stage, adjusting and optimizing the audio clip according to the linear mapping model, and dynamically updating a music parameter control model according to the change of a scoring result output by the sleep quality scoring model. And the accuracy and timeliness of music intervention are improved.