301 results about "Sleep period" patented technology

The invention provides a system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

The invention relates to a sleep apnea monitoring method and device, electronic monitoring equipment and a storage medium. The method comprises the following steps: acquiring PPG signal data of a user in a sleep period, performing segmentation processing on the PPG signal data, performing quality scoring on each PPG signal fragment data obtained by the segmentation processing, screening out target PPG signal fragment data of which the quality score is lower than a quality score threshold value, and sending the target PPG signal fragment data to the user; the quality score threshold value dynamically changes based on statistical characteristics of quality scores in different time periods, blood oxygen detection is conducted on the target PPG signal segment data to obtain blood oxygen index data, effective respiratory wave detection is conducted based on the target PPG signal segment data to obtain respiratory index data, and the effective respiratory wave detection is conducted based on the blood oxygen index data and the respiratory index data to obtain the target PPG signal segment data. Whether a sleep apnea event or a hypopnea event is present during sleep of the user is identified. By adopting the method, more accurate sleep apnea monitoring can be realized.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

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.

A sensing unit is configured to generate pressure readings from a phenomena within a sleep environment and to generate temperature readings from a sensed temperature. A processing unit is configured to select a thermoregulation adjustment and to alter a thermal property of a sleep environment based on the selected thermoregulation adjustment.

This document describes systems, methods, devices, and other techniques used for improving a sleeper's body thermoregulation during sleep. One aspect is a system, comprising a bed with a heating element and a cooling element and a controller configured to receive a current sleep state of a sleep stage sequence for a user in the bed, when the current sleep state is a non-rapid eye movement (NREM) state, activating the heating element to increase a skin temperature of the user, and when the current sleep state is a rapid eye movement (REM) state, activating the cooling element to decrease the skin temperature of the user.

A system, method, and appliance provide sleep-related disorder data collection, assessment, and visual representation. The appliance may be configured to be worn during a sleep cycle of an individual and may include or be operatively connected to various sensors that collect physiological data of the individual during the sleep cycle. Aspects of the system are configured to correlate data obtained from various sensors / data sources during an individual's sleep cycle, determine relevant events associated with sleep-related disorders based on the obtained data, provide summaries and information about the determined relevant events, and generate and provide dynamic visualizations of the individual's anatomy corresponding to the determined relevant events. A dynamic visualization may include a 3D model of the individual's airway and mandibular anatomy that, when played back, morphs to represent dynamics of the individual's anatomy during the relevant event.

The invention relates to an intelligent hypnosis method and system based on a brain-computer interface and a storage medium, and belongs to the technical field of human-computer interaction and artificial intelligence. The method comprises the following steps: acquiring an EEG signal of a user in real time through EEG acquisition equipment, and extracting a brain wave segment power value as state input through preprocessing; a deep reinforcement learning model (such as DRQN) selects music type actions (such as classical music and white noise) based on an epsilon-greedy strategy; calculating a reward value (maximizing delta wave increment and inhibiting beta wave) according to the electroencephalogram state change after playing, and optimizing model parameters by adopting Q-Learning; and dynamically adjusting the strategy through iterative interaction until the user reaches a preset sleep target. The system comprises an electroencephalogram acquisition module, a preprocessing module, a reinforcement learning module and a music control module, and realizes closed-loop regulation and control. The method has the advantages of high personalization, high hypnosis efficiency (induction to sleep in 1-7 minutes), flexible adaptation to different users and self-evolution optimization capability, and effectively improves the sleep induction effect.

Disclosed are techniques for wireless communication. In an aspect, a UE determines a completion time associated with a communication event for a wakeup period, the wakeup period characterized by at least radio frequency (RF) circuitry being set to an active state. The UE schedules a transition from the wakeup period to a sleep period at the completion time, the sleep period characterized by at least the RF circuitry being set to an inactive state. The UE performs the communication event during the wakeup period. The UE transitions from the wakeup period to the sleep period at the completion time in accordance with the scheduling. The UE determines, after the transition from the wakeup period to the sleep period, a wakeup time associated with a next wakeup period.

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 relates to an equipment low-power-consumption control method and system based on wireless sensing. The method comprises the following steps: carrying out energy consumption modeling on a radio frequency transmitting circuit of a wireless sensing node to obtain a power control factor set for describing an energy consumption behavior of a power amplifier in the radio frequency transmitting circuit; performing bias intelligent control on the power amplifier according to the power control factor set to obtain a self-adaptive bias voltage configuration set under various input signal power conditions; and performing polling protocol synchronization processing on a wireless communication module of the wireless sensing node according to the self-adaptive bias voltage configuration set to obtain a low-power-consumption communication scheduling instruction set based on a time quasi-synchronization mechanism and a dynamic dormancy strategy, the method is used for controlling the transmitting time slot, the dormancy time period and the back-off waiting time length of the wireless sensing node in the multi-node networking communication. By adopting the method, a structured low-power-consumption control strategy of the wireless sensing node under the condition of dynamic input signal power can be realized.

The invention relates to a train dormancy awakening method and system based on TACS fusion control, and the method comprises the following steps: before a train is dormant, an AOM stores the position information of the train; in the train dormancy process, the speed measurement positioning board card keeps power supply, the speed measurement positioning module continuously collects speed pulse signals, train position information is calculated, and the train position information obtained through calculation is sent to the AOM; before the train is awakened, the AOM compares the position information obtained from the speed measurement positioning module in real time with the position information stored before dormancy, and whether the train is allowed to be awakened or not is judged based on the comparison result. According to the method, power supply is kept for the speed measurement positioning module during the train sleep period, real-time monitoring of the train position during the sleep period is achieved, meanwhile, through position comparison before wakeup, the wakeup efficiency of the train is improved, and wrong wakeup operation caused by abnormal movement of the train is prevented.

The invention discloses a child mouth breathing monitoring method, device and equipment and a medium, and the method comprises the steps that a non-contact sensor group is used for synchronously collecting multi-mode physiological data of a target child in the sleep period, and the multi-mode physiological data at least comprises a face thermal imaging video stream, a thoracic and abdominal micro-motion signal and an environment audio signal; the multi-modal physiological data is processed, multi-modal features related to respiration are extracted respectively, and the multi-modal features comprise thermodynamic change features of mouth and nose areas extracted based on a face thermal imaging video stream, respiratory effort waveforms extracted based on thoracic and abdominal micro-motion signals, and the respiratory effort waveforms extracted based on the thoracic and abdominal micro-motion signals; extracting a sound source spatial position and a spectrum feature of breathing sound based on the environment audio signal; inputting the multi-modal features into a pre-trained mouth breathing recognition model, and outputting a judgment result of the functional mouth breathing event of the target child within a specific time period; and generating a mouth breathing monitoring report of the target child based on the judgment result.

The invention discloses an electroencephalogram sleep staging method and device based on time-space relation learning and a medium. The method comprises the following steps: acquiring electroencephalogram data; constructing a context sequence of adjacent sleep periods according to the electroencephalogram data; inputting the sequence obtained by construction into a sleep staging model, and outputting a staging result; wherein the working mode of the sleep staging model is as follows: generating a space-time diagram by splicing an instance adaptive brain function connection matrix of a single sleep period and a space-time transition matrix of an adjacent sleep period; each adjacent sleep period is directly spliced with a to-be-tested sleep period sample, the adjacent sleep periods and the corresponding time-space diagrams are input into a gated time-space diagram convolution module together, the time-space dependency relationship between brain channels of the adjacent sleep periods is learned, and characteristics used for staging are obtained. According to the method, the global space-time transition relation of the sleep context sequence is captured by combining the construction of the space-time diagram and the design of the gated space-time diagram convolutional network, and the method has relatively good reliability and accuracy in electroencephalogram sleep staging tasks.

A server delay controller includes: a packet harvesting unit that refers to a packet held in a ring buffer when a packet arrives and executes harvesting to delete an entry of a corresponding queue from the ring buffer, a sleep management unit that puts the thread to sleep in a case in which a packet does not arrive for a predetermined period, and cancels the sleep of the thread through a hardware interrupt at the time of packet arrival, and an idle state recovery control unit that periodically wakes up the thread during sleep or wakes up the thread immediately before the packet arrival in accordance with a packet arrival timing.

The invention discloses a Doppler frequency offset estimation method and electronic equipment, and relates to the technical field of communication, and the method comprises the step that the electronic equipment communicates with a low-orbit satellite in a discontinuous reception (DRX) mode. Wherein in a discontinuous reception (DRX) mode, the electronic equipment enters a wake-up period when the wake-up moment arrives, the electronic equipment communicates with a low-orbit satellite in the wake-up period, the electronic equipment enters a sleep period when the sleep moment arrives, and the electronic equipment stops communicating with the low-orbit satellite in the sleep period. When the wake-up moment arrives, the electronic device obtains at least one historical Doppler frequency offset value; and the electronic equipment determines a Doppler frequency offset value corresponding to the wake-up moment based on the historical Doppler frequency offset value. In the scheme, the Doppler frequency offset value at the wake-up moment is estimated through the historical Doppler frequency offset value, and the electronic equipment can calculate the Doppler frequency offset value at the current wake-up moment, so that frequency offset compensation can be carried out in time at the current wake-up moment, and the performance of frequency offset compensation is optimized.

The embodiment of the invention provides a sleep staging analysis method and system based on electroencephalogram signals and a medium. The method comprises the steps that the electroencephalogram signals are obtained, feature extraction is conducted on the electroencephalogram signals, electroencephalogram features are obtained, and the electroencephalogram features are preprocessed; inputting a sleep analysis model based on the preprocessing result, outputting sleep stage data, and performing stage division on sleep based on the sleep stage data to obtain a plurality of sleep stage analysis results; comparing the sleep period analysis result with set standard information to obtain a staging deviation ratio, and judging whether the staging deviation ratio is greater than or equal to a set deviation ratio threshold value or not; if yes, adjusting model parameters of the sleep analysis model based on the correction information; if yes, evaluating the sleep effect based on the sleep evaluation information; the sleep state is subjected to staging processing by analyzing the electroencephalogram signal, so that the sleep effect is analyzed according to different staging data, and the analysis precision of the sleep state is improved.

the first information indicates a cell DTX state of a serving cell of the terminal device, and the C-DRX-related timer is configured to determine whether the terminal device enters a C-DRX active period (S901); and when the first information indicates that the DTX state of the serving cell is an inactive period state, performing a first operation, where the first operation indicates the terminal device to enter a C-DRX sleep period or is used to change the running state of the C-DRX-related timer (S902). The cell DTX state is taken into consideration for whether the terminal device enters the C-DRX sleep period, thereby avoiding an invalid monitoring of a PDCCH performed by the terminal device when the cell DTX state is the inactive period state.

The present invention relates to the technical field of sleep staging, specifically provides a method for identifying a deep sleep stage in a sleep stage, an apparatus, and a storage medium, and aims to solve the problem that existing sleep staging methods cannot accurately identify the deep sleep stage. For this purpose, the method in the present invention comprises: performing data preprocessing on the basis of acquired user sleep feature data to obtain a feature data sequence; and on the basis of a currently stored average baseline value and the feature data sequence, identifying and obtaining a deep sleep stage in a sleep stage to be tested, wherein the initial value of the average baseline value is obtained in light of medical-level sleep feature data, and the feature data sequence is obtained on the basis of a combination result of body motion data and heart rate data. The deep sleep stage identified and obtained by means of the solution of the present invention can be closer to a sleep staging detection result of a medical-level monitoring device. Therefore, the solution of the present invention can effectively improve the accuracy of identification of the deep sleep stage in the sleep staging.

The utility model relates to the technical field of magnetic-axis keyboards, particularly discloses an induction automatic scanning structure in a dormancy period of a magnetic-axis keyboard, and solves the technical problem that power loss is greatly caused as scanning operation needs to be continuously performed no matter the magnetic-axis keyboard is in a working state or a dormancy state due to the characteristics of the magnetic-axis keyboard. Comprising a metal shell, a positioning aluminum plate is arranged in the metal shell, interlayer foam is connected to one side of the positioning aluminum plate, a PABC plate is arranged on the other side of the interlayer foam, and a sensing assembly is arranged in the metal shell and used for sensing a human body; the magnetic-axis keyboard can stop scanning in a dormant state, so that power loss of the magnetic-axis keyboard is reduced, when the magnetic-axis keyboard needs to be used, after a hand of a human body is close to the magnetic-axis keyboard, generated induction signals can be transmitted to the positioning aluminum plate through the first conductive cotton and further transmitted to the human body induction chip on the PABC board through the second conductive cotton, and the human body induction chip is used for sensing the human body. Therefore, the keyboard is triggered to start quickly.

The invention relates to the technical field of medical equipment, in particular to a sleep breathing control method, device, system, equipment and medium, and aims to obtain sleep-related data of a monitored object in a sleep period, judge whether the monitored object needs breathing control or not according to the sleep-related data, and control breathing of the monitored object if the monitored object needs breathing control. If yes, mode setting information of the breathing control system is determined, the mode setting information is used for indicating working modes of the breathing control system, the working modes comprise a CPAP ventilation mode, a BiPAP-T ventilation mode and an Auto BiPAP ventilation mode, and corresponding different control strategies are adopted for the monitored object according to the mode setting information of the breathing control system. It can be seen that according to the sleep related data, whether the monitored object needs to be subjected to breathing control or not is determined, and when the monitored object needs to be subjected to breathing control, accurate sleep breathing control can be conducted on the monitored object according to the mode setting information of the breathing control system, so that the treatment effect becomes better.

The disclosure herein relates to devices, systems and methods for detecting disturbances that may occur during the sleep of a subject. In particular, in some embodiments, the devices, systems, and methods described herein comprise a sensing unit and a processing unit for processing data relating to disturbances that may occur during the sleep of a subject, in which the sensing unit may include a gyroscope adapted to measure movements of the mandible of a subject.

The invention belongs to the technical field of sleep health monitoring, and particularly relates to an optimal area selection chest / abdomen respiration signal extraction method based on human body pressure distribution. The method comprises the steps that data signals of human body pressure distribution changing along with time during sleeping are collected; the collected original pressure data are preprocessed, and clear and accurate pressure distribution data are obtained; for the preprocessed data, based on calculation of parameters such as the frequency, the power and the signal-to-noise ratio of the chest and abdomen area, an optimal breathing area selection algorithm is used for calculating respective band-shaped areas, most suitable for breathing signal extraction, of the chest / abdomen area; for the selected area, respiratory waveforms are extracted through a pressure signal fusion method based on weighted summation, and thoracic / abdominal respiratory signal monitoring is achieved; the sleep breathing monitoring device can accurately monitor sleep breathing in the form of a non-contact mattress under the condition that sleep quality is not interfered, and has a wide clinical application prospect.

The invention provides a method and equipment for constructing lung function characteristics during sleep. The method is applied to lung function data processing. The method comprises the following steps: collecting a breathing velocity waveform of a target object; determining an inspiration stage and an expiration stage in each respiratory cycle based on the respiratory velocity waveform; the duration of each stage is determined based on the inspiration stage and the expiration stage, and the duration of the inspiration sub-stage and the duration of the expiration sub-stage are obtained; basic features, a stability measurement feature group and a comprehensive feature group are calculated based on the inspiration sub-stage duration and the expiration sub-stage duration; according to the method, the breathing speed waveform is acquired without burden, and the comprehensive lung function evaluation feature group suitable for the radar breathing waveform is constructed.

The invention discloses a brain wave sleep-aiding music generation method and device capable of adjusting a sleep state, equipment and a medium, and the method comprises the steps: obtaining an electroencephalogram signal, carrying out the preprocessing of the electroencephalogram signal, and obtaining a sleep period sample; performing sleep stage analysis on the sleep stage sample to obtain a sleep state; acquiring corresponding professional sleep-aiding music from a music library according to the acquired sleep state; extracting features from the electroencephalogram signals, and generating real-time brain wave music according to the extracted features; and inputting the professional sleep-aiding music and the real-time brainwave music into a music style migration model, learning a mutual migration relationship between the real-time brainwave music and the professional sleep-aiding music, and generating the brainwave sleep-aiding music for the current sleep state. Based on the real-time brainwave music and the professional sleep-aiding music, the sleep-aiding brainwave music capable of adjusting the real-time sleep state of the testee is generated, the different sleep states of the testee are adjusted in a targeted mode, and the method has important application value in the field of sleep health.

The invention relates to an insomnia diagnosis and typing method based on multi-modal signal fusion, which comprises the following steps of: acquiring multi-source physiological signals of a testee during a rest period at night to construct a multi-modal information source during a sleep period; performing rhythm recognition and consistency judgment on the collected signals, calculating the rhythm change trend of each modal signal in a preset time window, and determining whether a signal fragment with physiological rhythm synchronization imbalance exists or not according to the rhythm collaboration among modals; performing inter-modal logic consistency analysis on the rhythm anomaly signal segments, identifying contradictory states of different modals in the same time window, and marking the contradictory states as modal conflict nodes; extracting an activation sequence of each modal signal according to a sequential relationship of the conflict nodes so as to determine a dominant modal of the insomnia behavior; analyzing a state transition behavior of the multi-mode signal through a sliding time window, identifying a nonlinear fracture section of a sleep state, and determining an insomnia occurrence time section and jump characteristics in combination with a mode activation sequence so as to realize insomnia diagnosis and typing judgment.

The invention relates to the technical field of infant sleep monitoring, in particular to an infant sleep state monitoring method and system based on sound analysis, and the method comprises the following steps: based on an intelligent infant bed, collecting sound signals sent by an infant in real time through a microphone sensor on the inner side of the bed, recognizing sound in a deep sleep period, sound in a light sleep period and sound in a rapid eye movement period, and transmitting the sound signals to the intelligent infant bed; and acquiring a sleep cycle sound category. According to the invention, the sound signals are collected in real time and are accurately classified according to the frequency range, so that the states of deep sleep, light sleep and rapid eye movement periods of the baby can be more accurately identified, and through careful comparison with reference modes in the audio database of the baby crib, the matching precision of data is improved, and the accuracy of the baby crib is improved. And the utilization efficiency of the sound data is optimized, so that the response capability of the whole monitoring system is improved, the monitoring sensitivity is dynamically adjusted, the sampling frequency is adjusted according to real-time data, the potential of misinformation is reduced, and the practicability of the monitoring system is improved.

The invention provides a sound wave and low-frequency pulse collaborative sleep-aiding oxygen cabin control method, which comprises the following steps of: setting a basic period stage of an oxygen cabin on the basis of a superdaily rhythm of a human body sleep period, and determining a core physiological target of the basic period stage; respectively carrying out time sequence matching of low-frequency pulse, sound wave stimulation and oxygen supply pressurization; carrying out nerve conduction path matching and organ response space-time synchronization based on the corresponding organ nerve induction partitions; establishing a fuzzy control model, performing multi-parameter real-time detection, and dynamically adjusting modal parameters according to monitoring data of human body physiological indexes; setting a pulse intensity upper limit, a sound wave decibel upper limit, an oxygen concentration upper limit and a single pressurization duration, and executing oxygen cabin operation control according to the modal parameters. The method has the advantages in physiological adaptation, collaborative accuracy, individual adaptation and safety guarantee, fits the electroencephalogram and physiological law of natural sleep, and improves the sleep continuity.

The invention relates to a sleep apnea detection method and device, embedded equipment and a storage medium. The method comprises the steps of obtaining PPG signal data of a user during sleep, preprocessing the PPG signal data to obtain a plurality of PPG signal segments, extracting a one-dimensional time sequence feature vector used for reflecting the change condition of heart rate and blood flow for each PPG signal segment, converting the PPG signal segments into two-dimensional frequency spectrum images, and outputting the two-dimensional frequency spectrum images. Extracting a two-dimensional image feature vector used for reflecting spectrum structure characteristics based on the two-dimensional spectrum image, fusing the one-dimensional time sequence feature vector and the two-dimensional image feature vector to obtain a fused feature vector, and performing standardization and dimension reduction processing on the fused feature vector to obtain a target feature vector; and inputting the target feature vector into a trained classification model for classification to obtain a classification result of whether the PPG signal segment has the apnea event. By adopting the method, the accuracy of apnea detection during sleep can be improved.

Sleep need for a user is assessed using continuous physiological data from a wearable monitor. In particular, by calculating a first sleep debt metric based on user strain and a second sleep debt metric based on accumulated sleep debt, an objective metric can be obtained that estimates an amount of sleep needed by the user in a next sleep period. This approach takes advantage of multiple modes of information embedded in the physiological data, such as a sleep and exercise patterns for a user over one or more preceding days.