208 results about "Sleep staging" patented technology

The invention discloses a sleep monitoring and intervention method and system based on electroencephalogram signals, and belongs to the technical field of sleep monitoring and intervention. The method comprises the steps that electroencephalogram signals are collected through a dry electrode worn on the head of a user; the signals are preprocessed; performing automatic sleep staging on the pre-processed signal based on a layered space-time joint modeling network, and outputting a waking period label, an N1 period label, an N2 period label, an N3 period label and an REM period label; whether intervention conditions are met or not is judged based on the staging labels and the real-time electroencephalogram spectrum features; and if yes, generating and applying an alternating current stimulation signal for intervention. The system comprises a wearable electroencephalogram acquisition device, a signal analysis module and an intervention module. According to the method, the deep learning model integrating front-end self-adaptive processing, the graph neural network and Transform is adopted for sleep staging, closed-loop phase synchronous stimulation driven by the digital phase-locked loop is combined on the basis, connection from sleep monitoring to closed-loop intervention is achieved, and the sleep monitoring accuracy and the intervention effect are effectively improved.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

A computing device for monitoring a sleep stage of a user in a contactless manner includes a processor, a radar unit, and a memory. The processor executes instructions from memory to cause the computing device to perform a series of signal processing methodologies on the waveforms received from the radar unit to accurately determine the sleep stage of the user in a contactless manner.

The invention discloses a multi-scale time-frequency network sleep stage classification method based on single-channel electroencephalogram, and belongs to the technical field of medical signal processing. Aiming at the problems of capturing multi-scale time-frequency characteristics, processing variability among subjects and modeling long-range time dependence of a sleep staging method, the method comprises the following steps: firstly, acquiring electroencephalogram signal data, and performing time sequence segmentation, sleep stage category label determination and standardized preprocessing by taking a single-channel electroencephalogram signal as an analysis object; performing data set division by adopting nested cross validation, and constructing a multi-scale time-frequency network model; the model comprises a feature extraction module and a sequence learning module, wherein the feature extraction module comprises a time domain branch and a frequency domain branch; a subject adaptive feature calibration module is proposed to dynamically compensate the influence brought by individual difference and signal quality fluctuation; respectively training a feature extraction module and a sequence learning module by adopting a component type training strategy; and inputting to-be-classified electroencephalogram signal data into the trained model, and outputting a corresponding sleep stage classification result.

The invention discloses a sleep real-time staging modeling method, a sleep real-time encoding and decoding method and a sleep real-time staging modeling system. EEG, EOG and EMG signals of a subject are collected, statistics, frequency domain and frequency domain characteristics and other characteristics are extracted after preprocessing, sleep stages marked by experts serve as real labels, a model is trained in a supervised learning mode, and real-time classification of the sleep stages is achieved. Furthermore, high-precision encoding and decoding of sleep content are realized by applying stimulation prompt during sleep, collecting and preprocessing whole-brain EEG signals, distinguishing NREM and REM stages, training encoding and decoding models respectively, and aligning nerve characterization during waking and sleep by utilizing comparative learning.

The invention relates to the technical field of physiological signal processing and sleep monitoring, in particular to a sleep staging method and a sleep staging system for collecting multiple physiological signals, and the sleep staging method and the sleep staging system for collecting the multiple physiological signals synchronously collect auditory meatus photoelectric volume pulse waves, temperature and head micro-motion signals through an in-ear sensor array. According to the method, the signal quality index is calculated, time domain, frequency domain and nonlinear features are extracted, a dynamic weighted fusion mechanism is adopted, feature weights are adjusted according to the signal quality index, a hierarchical depth time sequence learning model is input for sleep staging, and the sleep staging accuracy is improved to 89% or above and is improved by 15-20% compared with a single brain wave method. A sleep state evaluation report and a personalized feedback intervention strategy generated by the system are beneficial for improving sleep quality, a dynamic weighted fusion mechanism enhances system robustness, adapts to different signal qualities and ensures stable performance, and the invention provides an efficient and accurate new method for the field of sleep monitoring.

The invention discloses a sleep staging method and system. The method comprises the steps that physiological feature original signals of a target object are obtained; performing signal state detection on the physiological feature original signal; if the user leaves the bed or the signal is invalid, performing data cleaning on the sleep staging data of the day to obtain a final sleep staging result, and if the user is in the bed state and the signal is valid, performing preprocessing operation on the physiological feature original signal, and performing separation to obtain at least two physiological parameter signals related to the sleep state; performing multi-dimensional feature extraction on the physiological parameter signals to construct a target physiological feature array; based on the target physiological feature array, the target object information array and the sleep state information array, a sleep staging result corresponding to the current moment is obtained through a preset sleep staging model. According to the invention, non-inductive home monitoring can be realized by relying on non-intrusive equipment such as an intelligent mattress and an intelligent pillow in an intelligent home scene, and the signal anti-interference capability and the sleep staging accuracy are effectively improved.

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

The invention relates to a self-adaptive sleep assisting method, system and device based on multi-mode perception and a medium. The method comprises the steps of performing seasonal correction based on latitude and longitude coordinates, constructing a dynamic rhythm curve, and obtaining user portrait data and real-time multi-source data; performing Kalman filtering fusion on the respiratory frequency signal and the vibration spectrum to generate a fused sleep staging result, performing classification processing on the original audio data, and generating a noise type label and duration; on the basis of the dynamic rhythm curve and a preset adjustment rate, calculating an upward light real-time parameter, and on the basis of the fused sleep staging result, the noise type label and the user portrait data, generating a downward light real-time parameter; and inputting the real-time parameters of the upper light and the real-time parameters of the lower light into a light field coupling model to calculate a light field distribution target value, and generating a light source driving instruction. According to the method, through multi-modal data perception, rhythm curve construction and light field cooperative regulation and control, the sleep staging precision, the light intervention suitability and the sleep assisting effect can be improved.

The invention belongs to the technical field of sleep stage identification. According to the sleep staging recognition model pre-training method and system based on the polysomnogram, in the training process of a basic encoder, balance samples are classified and constructed according to sleep staging labels, enhanced group samples are generated according to the balance samples, and sleep staging representation is extracted from the group samples, so that the sleep staging recognition model pre-training method and system based on the polysomnogram are obtained. Compared with the prior art, the method has the advantages that stage-invariant features are reserved, individual differences are reduced, efficient feature alignment is achieved under limited labels, sleep stage representation of the to-be-detected individuals is extracted from the polysomnogram by the aid of a pre-trained basic encoder, and sleep stage recognition precision is improved.

The invention discloses a method for detecting sleep apnea and related events based on a brain-computer interface. The method comprises the following steps: collecting physiological signals during sleep; performing signal preprocessing operation on the physiological signal to obtain a preprocessed physiological signal; performing automatic sleep staging on electroencephalogram signals in the preprocessed physiological signals by adopting a deep learning model to obtain a sleep staging result; extracting features corresponding to the sleep staging results to obtain sleep feature data; positioning an apnea related event through the sleep feature data to obtain an event positioning result; calculating the event positioning result and the preprocessed physiological signal by adopting a statistical method to obtain event statistical data; calculating an apnea hypopnea index according to the sleep staging result, the event statistical data and the total sleep duration; and generating a sleep monitoring report according to the sleep staging result, the event statistical data and the apnea hypopnea index.

The invention discloses a sleep quality evaluation method and system and a storage medium, and the method comprises the steps: inputting electrocardiosignal segmentation data into a trained sleep staging and breath detection multi-task model, and outputting a preliminary sleep staging result and an apnea detection result; based on the acceleration segmented data, extracting posture features of each corresponding time period, inputting the posture features into a decision tree posture classifier to obtain sleeping posture categories of each time period, and correcting a preliminary sleep staging result and an apnea detection result based on a preset posture and heart rate combined correction rule; and finally, calculating the corrected sleep staging result and the apnea result based on a preset sleep scoring rule to obtain a sleep quality evaluation result. Therefore, accurate quantitative evaluation of sleep staging and sleep quality is realized, and a reliable basis is provided for accurate intervention and monitoring of subsequent sleep health.

The invention provides an electroencephalogram signal processing method and device based on a non-invasive brain-computer interface, and belongs to the technical field of medical equipment. According to the change risk of the amplitude data of the electrode of the analysis processing target, the similarity degree of other analysis processing targets and the signal stabilization electrode, the identification processing scheme of the electroencephalogram staging model of the analysis processing target is determined, and based on the deviation data of the electroencephalogram staging result between different identification processing schemes, the electroencephalogram staging model of the analysis processing target is obtained. Determining the interference risk type of the analysis processing target and the position of the interference electrode, and determining the electroencephalogram signal processing method of the position of the interference electrode according to the similarity degree of the identification processing scheme with the identification deviation of the position of the interference electrode in each analysis processing target and the interference risk type of the analysis processing target. And the accuracy of the sleep staging result is improved.

A sleep staging method based on semi-supervised learning belongs to the field of artificial intelligence and health monitoring, and comprises the following steps: preprocessing original PPG signals, extracting heart rate and respiration signals, calculating morphological and frequency domain features, extracting sleep staging related representation features, modeling short-time features and long-time changes of sleep, and outputting a classification result of each sleep stage; calculating supervised loss by using the labeled PPG data, predicting unlabeled PPG data to obtain a soft label, screening a high-confidence sample, and converting the soft label into a hard label to calculate unsupervised loss; calculating a confidence coefficient, calculating category center features of each sleep stage category, and performing feature adjustment; performing confidence weighting on the sample features and the category center features; and constructing positive and negative sample pairs to carry out semi-supervised contrast learning. According to the method, the generalization ability of the sleep staging model is enhanced, the sleep staging accuracy is improved, and the method can be applied to scenes such as family health management, auxiliary diagnosis and sleep monitoring.

Apparatus and methods detect sleep staging events. The apparatus (100) may be configured to obtain a facial biopotential signal measured between two electrodes connected to a user's face, which may be configured to form a transverse-ocular measurement vector. The biopotential signal may be measured by a biopotential measurement device comprising the two electrodes. The apparatus (100, 200) may be configured to derive, from the facial biopotential signal, a plurality of biosignals suggestive of sleep staging events. The apparatus may be configured to classify individual segments of the plurality of biosignals as belonging to one of a plurality of sleep stating events. The classification may involve using a trained machine learning model, such as a recurrent neural network, to predict sleep staging events for different segments or epochs of the plurality of biosignals.

The invention provides a sleep stage method based on prototype data comparative representation learning, which comprises the following steps of: S1, acquiring an electroencephalogram signal related to sleep, and respectively performing strong enhancement processing and weak enhancement processing on the electroencephalogram signal to obtain a strong enhanced electroencephalogram signal and a weak enhanced electroencephalogram signal; s2, respectively carrying out coding processing to obtain strong enhancement coding features and weak enhancement coding features; respectively extracting time features to obtain a strong enhancement time view and a weak enhancement time view; s3, performing cross prediction on the strong enhancement time view and the weak enhancement time view to obtain time prediction features; introducing prototype data, and comparing the time prediction features with the prototype data to obtain comparison features; and S4, completing sleep staging based on the comparison characteristics. According to the method, the SPC model is designed by adding the prototype data into the context comparison module, so that the comparison learning efficiency is improved, the overall staging accuracy is improved, and the staging accuracy of each sleep stage is good.

The invention relates to a sleep monitoring and early warning method and system based on respiration data analysis and a medium, and belongs to the technical field of .The respiration data of a user is predicted through a sleep state and sleep apnea hypopnea index prediction model, the sleep state of the user and the sleep apnea hypopnea index are estimated, and the sleep apnea hypopnea index is obtained. And finally, early warning is performed according to the sleep state of the user and the sleep apnea hypopnea index, and meanwhile, a related treatment scheme is generated according to early warning information. According to the method, a deep learning model is pre-trained, sleep staging and sleep apnea hypopnea index estimation are carried out on the model in a unified framework at the same time, and internal correlation between a sleep macrostructure and a respiratory event is effectively decoupled. Then, through a domain adversarial training mechanism, sleep respiration characteristic knowledge learned in the contact type respiration signals is migrated to millimeter wave radar signals, and the problem that the generalization ability of a model is insufficient due to scarcity of radar data labels is solved;

The invention discloses a sleep staging method based on multi-view gating interactive attention fusion. The sleep staging method comprises the steps that a single-channel electroencephalogram signal is preprocessed; an original electroencephalogram sequence and a time-frequency graph obtained through continuous wavelet transform are generated and serve as multi-view-angle input; time sequence features are extracted from the original electroencephalogram sequence through a feature extraction module, and time-frequency features are extracted from the time-frequency graph; fusing the time sequence features and the time frequency features through a feature fusion module, including respectively applying convolution attention to highlight internal key features at an electroencephalogram view angle and a time frequency graph view angle, and performing interaction between view angles through cross attention; convolutional attention output and cross attention output are adaptively fused through a hierarchical expert hybrid mechanism; and outputting a sleep stage classification result through the time convolution network. According to the invention, more comprehensive feature representation is realized.

The invention relates to the technical field of automatic sleep staging, in particular to an electroencephalogram automatic sleep staging method and system based on SAGAN-GP and CNN-BiGRU, and the method comprises the steps: obtaining a Sleep-EDF data set and an SHHS data set; preprocessing the electroencephalogram signal data to obtain a preprocessed Sleep-EDF data set and a preprocessed SHHS data set; a to-be-analyzed generative adversarial network is obtained, network structures of a generator and a discriminator in the GAN are optimized and adjusted, and the optimized and adjusted network structures of the generator and the discriminator are obtained; according to the preprocessed electroencephalogram signal data in the Sleep-EDF data set and the SHHS data set, optimizing and adjusting the network structures of the generator and the discriminator, generating an adversarial network to be analyzed, and performing adversarial training to obtain final electroencephalogram synthesis signal data; and according to the final electroencephalogram synthesis signal data, performing electroencephalogram automatic sleep staging through a convolutional neural network model. According to the invention, the influence of data samples is reduced, and the accuracy of automatic sleep staging is improved.

The invention relates to a multimodal sleep staging method based on a bidirectional interactive RWKV network, and belongs to the technical field of data processing, and the method comprises the following steps: obtaining an electroencephalogram signal, an electrooculogram signal and an electromyogram signal; carrying out depth feature extraction on the electroencephalogram signal, the electrooculogram signal and the electromyogram signal; the first RWKV module is used for realizing context modeling of information in the modals and fusion of information between the modals by taking the first RWKV module as a main part and the second RWKV module as an auxiliary part; the first RWKV module and the second RWKV module are used as a main part and an auxiliary part, and context modeling of information in modals and fusion of information between the modals are achieved through the second RWKV module parallel to the first RWKV module; the outputs of the two RWKV modules are integrated through element-by-element addition to form a fusion feature; and generating a category with the highest probability for the posterior probability distribution of each sleep stage as a sleep stage prediction result. The method is high in staging efficiency and high in accuracy.

The invention discloses a sleep staging method and a storage medium, and belongs to the technical field of sleep staging, and the method comprises the following steps: inputting heart rate data into a pre-constructed sleep staging model according to a time sequence, and sequentially outputting sleep staging results; the sleep staging model comprises a KGNN module, a Sleep MLSTM module, a Sleep SLSTM module and an output module; the KGNN module calculates a context vector according to the heart rate data; the SleepMLSTM module calculates the hidden state of the last time step according to the heart rate data, and adds the hidden state with the context vector to serve as the final output feature of the SleepMLSTM module; the SleepSLSTM module takes the final output feature as an input to generate an output feature; and splicing the final output features of the SleepMLSTM module and the output features of the SleepSLSTM module, and inputting the spliced features into an output module to generate a sleep staging result. The method can solve the problems that an existing sleep staging method is difficult to capture complex physiological correlation between heart rate variability characteristics and is difficult to resist the influence of time-varying noise of original heart rate signals.

The invention relates to a mental stress assessment method based on a non-contact sensor and HRV data analysis. The method comprises the following steps: acquiring a historical RR interval data set and a target day RR interval data set, and correspondingly generating a plurality of different interval characteristics based on RR interval data; calculating a quality score of the RR interval data; carrying out average calculation on the quality scores of all RR interval data contained in each sleep stage to obtain a stage average value corresponding to each sleep stage label; calculating a stability score value corresponding to each sleep stage based on the average value of each stage, and determining an optimal sleep stage; extracting all target day RR interval data of the optimal sleep stage, and generating a target day HRV value; generating a historical HRV sequence based on the historical RR interval data set; and performing numerical processing on the target day HRV value based on the constructed detrending model and the historical HRV sequence to generate a mental assessment HRV sequence, and generating a mental stress assessment result based on the mental assessment HRV sequence.

The invention relates to a sleep staging method based on Markov chain dynamic loss, and relates to the field of data processing. The method comprises the steps that electroencephalogram signals are preprocessed to obtain training samples, a sleep staging model is constructed and trained, sleep staging is conducted through the trained model, and training comprises the steps that basic classification loss is calculated; when the sleep stage of the training sample is transferred to different stages, obtaining a real physiological transition probability through a Markov transition probability matrix, and if the probability is smaller than a threshold value, calculating a loss weight factor of the training sample according to whether the model correctly predicts the sleep stage of the current training sample; calculating an average value of the sequence sensing loss according to the loss weight factor and the basic classification loss; and calculating the gradient of the average value to the parameters of the sleep staging model, and updating the model parameters of the sleep staging model. According to the method and the device, the physiological interpretability of understanding and prediction of the sleep staging model on the overall sleep structure is improved, and then the sleep staging accuracy is improved.

The invention discloses a sleep staging basic model and a training method of the sleep staging basic model, and the model comprises a feature projection module which is used for obtaining electroencephalogram signals of a plurality of electrode channels, and carrying out the characterization of a time sequence segment of each electrode channel, and obtaining a time domain feature and a frequency domain feature; the cross attention module is used for calculating a first cross feature and a second cross feature of the time domain feature and the frequency domain feature, and integrating the first cross feature and the second cross feature to obtain a double-domain fusion feature; the topology embedding module is used for superposing spatial topology embedding on the double-domain fusion features corresponding to the electrode channels to obtain target fusion features, and the spatial topology embedding is used for representing the collection positions of the electrode channels; the deep semantic representation learning module is used for performing sparse multi-expert representation learning on the target fusion features to generate advanced semantic representation; and the output layer is used for carrying out decoding processing based on the advanced semantic representation to obtain a sleep staging result. The sleep staging accuracy of the sleep staging basic model can be effectively improved.

The invention provides a sleep regulation and control method and device based on a non-invasive brain-computer interface, and belongs to the technical field of sleep regulation and control. The method specifically comprises the steps that the coincidence condition of a reliable user and a user with the environment temperature changing is determined and recognized, and verification data of a sleep stage recognition model for recognizing the reliable user are combined; determining an update processing strategy of the sleep staging identification model, performing update processing on the sleep staging identification model according to the update processing strategy of the sleep staging identification model, and determining the sleep staging identification model according to an update processing result of the sleep staging identification model and historical regulation and control data of the sleep environment temperature in the user with the environment temperature changing. According to the regulation and control method for determining the sleep environment temperature of the user with the environment temperature changing, determination of differentiated temperature regulation strategies of the sleep environment temperature under different staging results is achieved, and then the user experience is improved.

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.

The invention discloses a patient sleep awakening three-classification monitoring method and device based on a machine learning model, equipment and a medium, and the method comprises the steps: collecting acceleration data and activity counting and heart rate data of a subject, and carrying out the preprocessing of the collected data; extracting data features based on the preprocessed data, wherein the data features comprise a motion feature, a heart rate feature and a clock proxy feature; the extracted data features are input into a classification model, prediction state data of each Epoch are output, each row in the prediction state data comprises an Epoch starting timestamp, a real sleep staging label and a three-classification prediction result of the model, and therefore the prediction state data of each Epoch can be obtained. According to the method, the three-classification prediction accuracy of awakening, non-fast eye movement sleep and fast eye movement sleep can be effectively improved, and the method can be widely applied to sleep monitoring.

The invention discloses a sleep staging method based on a piezoelectric sensing signal and related equipment, and the method comprises the steps: obtaining the piezoelectric sensing signal of a target object in a sleep period, and segmenting the piezoelectric sensing signal in the sleep period into a plurality of piezoelectric sensing signals according to a preset time length; performing signal amplitude alignment processing on the piezoelectric sensing signal of each segment to obtain an amplitude alignment signal of each segment; performing continuous wavelet transform on the amplitude alignment signal of each segment to generate a time-frequency graph corresponding to each segment; fusing the time-frequency graph of each segment with the time-frequency graph of the adjacent segment to obtain a context time-frequency graph; inputting the context time-frequency graph into a preset sleep staging model to obtain an initial sleep staging result of each segment; and correcting the initial sleep staging result of each segment to obtain a final sleep staging result of each segment. The method can effectively improve the accuracy of sleep staging, and can be widely applied to the technical field of sleep monitoring.

The invention discloses a portable electroencephalogram sleep detection and analysis system and method. The portable electroencephalogram sleep detection and analysis system comprises an in-ear electroencephalogram acquisition unit which is used for acquiring differential EEG signals in ear canals and converting the signals into digital data streams for transmission; the data processing and calculating unit is used for carrying out preprocessing operation on the digital data flow transmitted by the in-ear electroencephalogram acquisition unit; and the sleep staging unit is used for inputting the preprocessed data into an event-driven time sequence classification model, and the model reads a signal sequence in a sliding window mode, completes judgment and classification of sleep stages and generates a corresponding sleep quality evaluation report. According to the invention, the calculation amount is reduced by using the snn with low power consumption, so that the staging system is integrated into the embedded platform, meanwhile, the problem of poor staging effect of wearable sleep monitoring equipment is solved, and the intelligent sleep staging system is high in portability, simple and convenient to use, high in integration and relatively low in cost.