87 results about "Sleep Stages" patented technology

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention provides a sleep assisting system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of sleep assisting. The system comprises an acquisition unit, a processing unit, a stimulation unit and an evaluation feedback unit. Determining a sleep stage of the user, analyzing phases of multi-scale electroencephalogram rhythm components such as slow waves and spindle waves in a cross-scale manner, and extrapolating and predicting a phase value of a target electroencephalogram rhythm at a future moment in combination with a recent phase trajectory; taking the predicted phase as a target state, mapping a rising / falling slope of a slow wave form into an intensity change parameter of photoacoustic and magnetic vibration stimulation by constructing a mapping channel, and selecting an optimal stimulation combination for output through a decision model based on the intensity change parameter; calculating a phase locking effect index, and adjusting a phase prediction model parameter by using an optimization algorithm according to the index so as to optimize subsequent stimulation; and linkage regulation and control of an external environment are supported, and centralized monitoring of sleep states of multiple users can be realized. According to the invention, self-adaptive guidance of sleep brain rhythms is realized, and the sleep quality is effectively improved.

The invention provides a multi-mode adaptive wave simulation control system and method for a deep sleep physiotherapy cabin. Belongs to the technical field of fluid mechanics and health intervention. The method comprises the steps that multi-modal physiological parameter collection is conducted on a user on a deep sleep physiotherapy cabin, a real-time physiological data set is generated, the current sleep stage of the user is deduced through a time-frequency analysis and machine learning model on the basis of the data set, a sleep stage label is generated, a pre-trained wave mode generator is dynamically called according to the sleep stage label, and the deep sleep physiotherapy cabin is obtained. An initial set of wave parameters matching the current sleep stage is generated. By sensing the physiological state of the user in a non-contact mode, deducing the sleep stage and dynamically generating and adjusting the wave modes such as sleep guiding and deep sleep maintaining, water fluctuation accurately adapts to the physiological requirements of the user in different sleep stages, a traditional static wave mode is abandoned, highly-personalized sleep adjusting experience is provided for the user, and the sleep quality is effectively improved.

The invention discloses a sleep state self-adaptive layered regulation and control method and platform based on an environment regulation window, and relates to the technical field of self-adaptive control, and the method comprises the steps: carrying out the sleep state analysis according to a physiological data sequence, obtaining a real-time sleep stage, carrying out the scheduling task distribution, and generating a function unit scheduling sequence; performing data acquisition priority management and control on the wearable device to obtain real-time sleep associated data; generating a sleep environment adjustment strategy by taking the real-time sleep stage as an environment adjustment constraint; after updating the sleep environment of the user, activating an environment adjusting window; and operating a hierarchical scheduling architecture by taking an environment adjustment window as a time constraint, and circularly performing adaptive control updating. The technical problem that in the prior art, due to the fact that the sleep environment is difficult to adjust accurately in real time according to the sleep state of the user, the sleep quality of the user is low is solved, and the technical effects that the sleep state of the user is accurately judged, the sleep environment is dynamically and adaptively adjusted, and the sleep quality is improved are achieved.

The invention provides a multi-mode sleep intervention system and method based on self-adaptive dynamic control. The system comprises a sensor module used for collecting brain wave activity signals in real time in a non-invasive mode; the AI module is used for performing sleep stage identification and stage division on the brain wave activity signal through a sleep stage prediction model, judging a current sleep state through a first AI strategy engine and automatically generating an optimal first intervention parameter sequence; the control module is used for issuing a corresponding control instruction to the multi-mode intervention module according to the optimal first intervention parameter sequence; and the multi-mode intervention module is used for executing one or more of sound, light, magnetism, temperature, touch and smelling multi-mode intervention according to the control instruction. Through multi-mode fusion perception, closed-loop monitoring control, dynamic adjustment of an intervention strategy and multi-mode interaction and intervention, the effects of accurately perceiving the sleep state in real time, improving the data anti-interference capability, self-adapting to sleep management, adapting to individual differences and meeting the requirements of different intervention scenes are achieved.

The invention relates to the technical field of sleep environment intelligent regulation and control, and provides a sleep thermal comfort self-adaptive regulation and control method based on electroencephalogram nerve feedback. The method comprises the following steps: firstly, acquiring three electroencephalogram channel signals FP1, FP2 and FPz of the forehead of a user, and identifying a sleep stage through preprocessing and an automatic staging algorithm; then quantile alignment, robust processing and channel confidence weighted fusion are carried out on the electroencephalogram signals of all the channels, and comprehensive electroencephalogram features and multiple sets of electroencephalogram feature parameters are extracted; calculating a nerve thermal comfort comprehensive index NTx based on the characteristics, and realizing prediction of an individual thermal comfort score TSV by using an LSTM model; finally, a temperature control instruction is automatically generated according to the TSV and the sleep stage, linkage adjustment is conducted on an air conditioner and bed area temperature control equipment, and individualized and continuous sleep thermal comfort self-adaptive optimization is achieved.

The invention discloses an ear electroencephalogram acquisition sleep monitoring device based on conductive leather and a personalized audio intervention method of the ear electroencephalogram acquisition sleep monitoring device, and aims to solve the problems that existing sleep assisting equipment is poor in wearing comfort and inaccurate in monitoring, and an intervention scheme is lack of personalization. The device comprises preparation of a conductive leather electrode and integration of the conductive leather electrode, an earplug, a loudspeaker and other devices. According to the conductive leather electrode, a conductive polymer (such as poly (3, 4-ethylenedioxythiophene) and a derivative thereof) is combined with natural leather through an in-situ polymerization method to form a flexible electrode material with high biocompatibility. The electrode is combined with an earplug, a loudspeaker and other devices and can be stably attached to the ear canal, and high-signal-to-noise-ratio sleep physiological electric signal (such as myoelectricity, electroencephalogram or electrocardiosignals) collection is achieved. The sleep stage is identified through machine learning, and the loudspeaker module is controlled to play different types of sleep-aiding audios. The core lies in that the device can synchronously evaluate sleep physiological signal changes of a user under different audio stimulation, so that personalized sleep-aiding audios which are most effective for the individual are screened out, and an exclusive scheme library is established. The system has the advantages of being comfortable to wear, high in signal quality, wide in applicability and the like, and can be widely applied to the fields of health monitoring, human-computer interaction, medical diagnosis and the like.

This invention discloses a sleep stage identification method, comprising: a multimodal data acquisition step; a preprocessing step, which normalizes the acquired physiological signals and segments them according to a set duration to obtain several original signal segments; a Williams RGB compression step, which maps the original signals to an RGB image to obtain a Williams RGB energy map; a feature extraction step, which inputs the Williams RGB energy map into a neural network model for feature extraction; and a comparison and classification step, which compares the feature values ​​with RGB standard thresholds to determine the corresponding sleep stage. This sleep stage identification method avoids directly processing the complex frequency domain features of physiological signals, thus reducing computational complexity. Three physiological signals within a certain time period only need to be represented by a single pixel in the image, achieving physiological signal compression and dimensionality reduction, further reducing computational load. This invention also effectively utilizes medical knowledge graphs to provide interpretable evidence for the sleep stage identification decision-making process.

The present application relates to the field of artificial intelligence and intelligent sleep technology, and discloses a health state evaluation model generation method and system based on multi-point body surface temperature and vital sign data during sleep. The method continuously collects body surface temperature data of multiple body contact areas during the user's sleep process, and synchronously collects data such as heart rate, respiratory rate, body movement, environmental temperature and bed surface temperature, to construct a multi-dimensional sleep physiological data set. On this basis, the multi-dimensional sleep physiological data is preprocessed, feature extraction and model analysis are performed, and the health state evaluation result and personalized health insight information of the user during sleep are generated. The staged evaluation can also be carried out in combination with the change of sleep stage, and the model is continuously optimized based on the long-term temperature adjustment behavior of the user. By using the present application, the accuracy, continuity and individual adaptability of sleep health state evaluation can be improved, and more accurate sleep health analysis basis is provided for the user.

The invention discloses a sleep-aiding equipment linkage remote control method and device based on the Internet of Things, and belongs to the technical field of equipment remote control, and the method specifically comprises the steps: collecting sleep-related data of a user, and analyzing the sleep state of the user based on the sleep-related data, the sleep-related data including heart rate, breath, body movement and environmental noise, and the sleep-related data including heart rate, breath, body movement and environmental noise; based on the sleep state, a cooperative control instruction used for multiple sleep aiding devices is generated, the cooperative control instruction comprises coordination parameters used for adjusting the action sequence and action intensity of the devices, the cooperative control instruction is sent to the sleep aiding devices through a remote communication channel, and self-adaptive adjustment is conducted according to device feedback and external disturbance; according to the invention, it is ensured that multiple devices can implement matched action modes in different sleep stages of a user, and stable instruction generation capability and flexible remote control capability are provided for a complex sleep scene.

Embodiments described herein relate to a wearable device for monitoring a user's sleep metrics and adjusting the user's sleep conditions or environment. In some embodiments, the wearable device may include earbuds worn in each ear of a user, each earbud including one or more sensors for measuring biometric data related to a user's sleep. The wearable device may interface with and / or communicate with a supplemental device. In some embodiments, the supplemental device may be a charging case that may be capable of charging the earbuds. Biometric data collected using the wearable device may be used to determine sleep information including a user's sleep state and / or a sleep stage. In some embodiments, an audio output of the wearable device may be adjusted to improve a user's sleep conditions. In some embodiments, a beside speaker may be used in addition to or instead of the wearable device to improve a user's sleep.

Methods and apparatus infer or indicate sleep stage(s) of a patient from a respiratory flow rate signal of the patient. The method may include applying a plurality of detection pathways to a signal representing a respiratory flow rate of the patient, wherein each detection pathway is configured to generate start events and end events indicating start times and end times of episodes respectively of a corresponding sleep stage, wherein each start event and each end event has a priority; and combining the start events and end events based on their priorities to produce an indication of the sleep stage of the patient. The apparatus may include a sensor configured to generate a signal representing a property of a flow of air within a patient interface; and a processor configured to implement a method of inferring a sleep stage of the patient from the signal.

The application discloses a sleep-aiding device linkage remote control method and device based on the Internet of Things, and belongs to the technical field of device remote control. The method specifically comprises the following steps: collecting sleep-related data of a user, and analyzing the sleep state of the user based on the sleep-related data; the sleep-related data comprises heart rate, respiration, body movement and environmental noise; based on the sleep state, a cooperative control instruction for multiple sleep-aiding devices is generated; the cooperative control instruction comprises a coordination parameter for adjusting the action sequence and the action intensity of the devices; the cooperative control instruction is sent to each sleep-aiding device through a remote communication channel, and adaptive adjustment is performed according to device feedback and external disturbance. The application ensures that multiple devices can implement matched action modes in different sleep stages of the user, and provides stable instruction generation capability and flexible remote control capability for complex sleep scenes.

Provided is a system and method for determining a sleep state using a biological activity Doppler signal which comprises: a Doppler signal acquisition unit for acquiring a Doppler signal including biological activity information using a radar; an auxiliary signal processing unit for acquiring a tossing and turning motion and a snoring noise as auxiliary signals using a thermal image sensor and a noise sensor; a Doppler signal analysis unit for acquiring spectrum energy at preset intervals by analyzing the Doppler signal, determining whether the spectrum energy is acquired periodically, and performing classification of the spectrum energy using the auxiliary signals; and a sleep-stage definition unit for defining a sleep state of each stage in an entire sleep stage using a combination of a ratio of respiration spectrum energy and heartbeat spectrum energy, among the spectrum energy, and the non-periodic spectrum energy.

The invention discloses a sleep management system based on a smart watch, and relates to the technical field of health management, the system comprises a multi-modal data fusion acquisition module, a dynamic sleep stage division model, a personalized sleep intervention engine and a closed loop feedback optimization module; the personalized sleep intervention engine generates and executes a personalized intervention scheme based on a sleep stage result and a user portrait output by the dynamic sleep stage division model; the multi-modal data fusion acquisition module adds oxyhemoglobin saturation (SpO2), skin conductivity (EDA) and environmental parameters (temperature, humidity and illumination), and compared with two-dimensional data in the prior art, 8-dimensional fusion data can capture subtle features of a sleep state (such as association between SpO2 sudden drop caused by night hypoxia and interruption of a deep sleep period and a sleep stress state reflected by skin electrical activity fluctuation); and a data foundation is laid for accurate identification.

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.

The invention relates to a brain-computer interface, in particular to a sleep guidance and optimization system based on the brain-computer interface, which is characterized in that a sleep stage identification module segments electroencephalogram signals, constructs a time-frequency joint distribution characteristic matrix according to power spectral density corresponding to a segmentation result, identifies sleep stages based on the time-frequency joint distribution characteristic matrix, and optimizes the sleep stages based on the time-frequency joint distribution characteristic matrix; the sleep quality evaluation module calculates a multi-dimensional sleep quality evaluation index in real time according to the sleep stage recognition result, realizes dynamic sleep monitoring based on the multi-dimensional sleep quality evaluation index, receives a user description by using the sleep cycle planning module, converts the user description into a constraint condition through semantic understanding, and sends the constraint condition to the sleep stage recognition module; a sleep cycle is planned by combining a dynamic sleep monitoring result and constraint conditions, and a sleep cycle guiding module sends brain waves with different frequencies to the user in corresponding time periods to guide the user to execute the planned sleep cycle; according to the method, the defects that personalized sleep cycle planning and guiding are difficult to realize and a comprehensive sleep optimization scheme cannot be provided in combination with personalized requirements of the user can be effectively overcome.

The disclosure provides an electronic device and a method of detecting a sleep stage. The method includes the following. A radar signal is received, and a physiological signal is extracted from the radar signal. Fast Fourier transform is performed on the physiological signal by using a first window to obtain a transformed signal. A peak area ratio corresponding to the transformed signal is obtained according to a peak of the transformed signal. A first prediction result of the sleep stage is generated according to the peak area ratio by using a first machine learning model. The first prediction result is outputted.

The invention relates to the technical field of intelligent wearable equipment, in particular to a multi-stage alarm clock wake-up system based on biological signal recognition, which collects and fuses multi-modal physiological signals in real time through wearable equipment, dynamically calculates wake-up demand indexes and accurately recognizes sleep stages. Graded combined stimulation of vibration, acoustics and electric tactile sense is adaptively triggered based on the sleep depth, and a mobile terminal remote cooperative control and multi-device networking enhancement mechanism is combined; meanwhile, a gesture detection closed loop is used for verifying the waking state, the stimulation intensity is automatically upgraded when no response is made, and finally progressive waking-up matched with the sleep cycle is achieved. Therefore, the wake-up success rate in the deep sleep period is remarkably improved, the phenomenon of secondary falling asleep is eliminated, and the user experience is improved through collaborative decision while ultra-low power consumption is maintained.

The invention provides a Schumann wave sleep aiding method and system based on electroencephalogram closed-loop control, and relates to the technical field of sleep aiding and physiological signal processing.The method comprises the steps that continuous electroencephalogram data of a user is collected; the first electroencephalogram data and the second electroencephalogram data are preprocessed, first calibration data and second calibration data are obtained, feature sequences corresponding to the first calibration data and the second calibration data are calculated, sleep stage labels corresponding to the feature sequences are marked, and a sleep stage classification model is constructed; inputting a feature sequence corresponding to the second calibration data into the sleep stage classification model, obtaining a sleep stage label corresponding to the feature sequence, and if the second calibration data meets a switching constraint condition, updating the sleep stage label and mapping the sleep stage label into a Schumann wave target parameter vector; and generating a Schumann wave regulation and control instruction according to the Schumann wave target parameter vector so as to regulate and control output parameters of a Schumann wave generator. According to the invention, the pertinence and stability of regulation and control can be improved.

Devices, systems, and methods are disclosed. The devices, systems, and methods detect, during a sleep session of a user, one or more parameters related to movement of the user, heart activity of the user, audio associated with the user, or a combination thereof; process the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or a sleep stage; and calculate an apnea-hypopnea index of the user during the sleep session based at least in part on the sleep state.

The application provides an ear-inserted brain-computer interface system and an ear-inserted device, comprising a cloud processor and an ear-inserted device, wherein the cloud processor and the ear-inserted device are in communication connection; the ear-inserted device comprises an electroencephalogram acquisition module and an intervention module, the electroencephalogram acquisition module is used for acquiring ear canal electroencephalogram signals of a user during sleep, and the intervention module is used for intervening in the user in response to an instruction of the cloud processor, wherein the user wears the ear-inserted device; the cloud processor is used for receiving the ear canal electroencephalogram signals from the ear-inserted device, determining a sleep stage of the user, and generating a first instruction corresponding to the sleep stage. The ear canal electroencephalogram signals during sleep can be acquired, the sleep stage of the user can be accurately identified, and an instruction corresponding to the sleep stage can be generated in real time to intervene in the user, so that the sleep quality of the user is greatly improved.

Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinson's Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include a novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

The invention relates to the technical field of sleep regulation, and provides an intelligent sleep environment regulation system and method. The system comprises a main control module, a sleep sensing module, a sleep stage inference module, a lighting effect strategy generation module and a lighting effect execution module, wherein the sleep sensing module senses vital sign signals, temperature distribution information and space contour information in a monitoring area in a non-contact manner; the sleep stage inference module is used for performing fusion analysis on the multi-source heterogeneous data acquired by the sleep perception module and inferring the sleep stage of the user; the main control module generates different light control instructions based on different sleep stages, the lighting effect strategy generation module responds to the light control instructions and outputs lighting effect driving signals corresponding to the sleep stages, and the lighting effect execution module generates sleep adjustment lighting effects according to the lighting effect driving signals. According to the scheme, wearing discomfort and sleep interference are eliminated, and the defects that a single sensor is low in precision, poor in interference resistance and weak in scene understanding are overcome.

The application discloses a sleep optimization system based on brain wave regulation, and relates to the technical fields of sleep health and electroencephalogram regulation, comprising a hierarchical gap dynamic mapping module, a gap resonance compensation module, a cross-layer interlocking calibration module and a gap feature adaptive memory module, and the four modules form a closed loop collaborative work; in the application, the closed loop collaborative technology of the hierarchical structure of brain waves, three-dimensional gap feature dynamic mapping, targeted resonance compensation suitable for sleep stages, cross-layer interlocking calibration and two-dimensional clustering self-optimization of stage gaps is used, the problems that the existing sleep optimization system does not use the brain wave hierarchical division mechanism, the single frequency band regulation has poor adaptability, the multi-frequency band regulation is easy to produce signal interference and has low efficiency, and the deep sleep is not continuous and the wakefulness is residual are solved, the precise adaptation to sleep stages and individual differences is realized, the frequency band interference is eliminated, the continuity and proportion of deep sleep are improved, and the residual wakefulness and shallow sleep wandering are reduced, so that the safe and efficient personalized sleep optimization effect is achieved.

The invention discloses a wearable device sleep data processing method and system supporting low power consumption, and relates to the related technical field of data processing.The method comprises the steps that sleep stages are divided, monitoring sampling frequencies and sensing sources are distributed to all the sleep stages, and monitoring rules are set. Sample driving is used as a training mode, a low-power-consumption module is constructed, and the low-power-consumption module is arranged in a processor of the wearable device and comprises a front data interface and a lightweight data model. And performing mode management on peripheral equipment of the processor based on the monitoring rule, and monitoring and acquiring multi-mode sensing data. And based on a front data interface, performing multi-modal fusion and data lightweight compression on the multi-modal sensing data, transferring to a lightweight data model for sleep state evaluation, and performing time sequence integration to output a sleep map. The technical problems that in the prior art, a sleep monitoring device is high in power consumption, and it is difficult to accurately monitor the sleep state of a user in a low-power-consumption state are solved.

This invention provides a head-mounted intelligent wearable neuromodulation and sleep state regulation device and method based on neural coupling tuning and state transfer induction, including a flexible contact array, a state recognition module, a state transfer induction module, a neural coupling modulation module, a stimulation output module, and a feedback optimization module. The system acquires brain state feature vectors and, based on sleep stages and state regulation goals, divides the head into multiple neural functional node regions. Based on the brain state feature vectors, it dynamically generates node combination relationships, node activation sequences, and node collaborative paths. The state transfer induction module controls multimodal stimulation signals to form rhythmic synchronization, phase coupling, and node collaborative regulation, and outputs the signals to the probes corresponding to the target nodes. Simultaneously, it collects feedback signals in real time and dynamically optimizes the topological relationships between nodes and stimulation parameters to achieve closed-loop transfer regulation of brain states between different sleep stages. This invention can improve the individual adaptability, regulation accuracy, and safety of sleep state regulation.