407 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 an all-night snore detection method based on a frequency domain attention and self-attention pooling double-fusion mechanism, and belongs to the field of sound event detection. The method comprises the following steps: designing a waveform feature extraction module based on a frequency domain self-attention sub-module, a Mel-cepstrum feature extraction module based on a deformable convolution residual sub-module, a self-attention pooling double-fusion module and a decoder based on a multilayer perceptron; a deep learning snore event detection model based on a frequency domain attention and self-attention pooling double-fusion mechanism is constructed by using the modules. The model can detect and position snoring sound in all-night sleep sound data in a point-by-point framing detection mode by loading trained parameters, and the starting and ending time of a snoring event in all-night time and the corresponding snoring sound type are obtained. The model can rapidly and accurately detect snore segments in the whole night sleep period, and is helpful for assisting medical staff in rapidly screening patients with sleep breathing disorder in the early stage.

A foam earplug for reception at least partially within the ear canal and including a body portion formed of resilient foam plastic material having a multiple open cell structure and having a size and shape for at least partial reception within the ear canal. A transdermal material having a particular beneficial action, and with an outer layer of the foam earplug infused with the transdermal material so that the benefits of the transdermal material is in transdermal contact with the ear canal. One example is a humectant material and, specifically, a moisturizer / lubricating material, for providing an increase to the lubrication and moisturizing in the ear canal. A second example is where the beneficial action is provided by systemic distribution such as a sleep aid, i.e., Melatonin, so that when the earplugs are worn during sleep, the earplugs will provide a sleep aid to the wearer.

The invention provides a communication method and a communication device. The communication method comprises: a network device sending first information to a terminal device, the first information being used by the terminal device to determine a first access opportunity for initiating random access to the network device, the terminal device being one of a group of terminal devices selected by the network device; accordingly, after receiving the first information, the terminal device enters a dormant state in a first dormant time period and initiates random access to the network device at a first access time period, and the first dormant time period is before the first access time period. In the communication method provided by the application, since the terminal device is dormant in the process of waiting for random access, the power consumption of the terminal device in the process of randomly accessing to the network device is reduced.

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 method for treating a patient via high-frequency (HF) electrostimulation therapy at an external target body location comprises generating a tonic motor activation (TOMAC) electrostimulation waveform using a power source supplying less than 100 volts zero-to-peak voltage amplitude per phase. The waveform has a frequency of 1,000 Hz to 5,000 Hz, a zero-to-peak current amplitude of 10-50 mA, and a duty cycle greater than 25%. The waveform is configured to overcome skin capacitance, induce proprioceptive afferent activity, and induce sustained tonic motor activation of a muscle. The method involves delivering the waveform to the external target body location over a specified duration during the patient's sleep period and receiving at least one TOMAC efficacy indication. The efficacy indication may relate to sleep efficiency, sleep stage, sleep onset latency, daytime sleepiness, CPAP compliance, or patient physiological response indicating TOMAC.

The invention discloses an artificial intelligence-based obstructive sleep apnea judgment method, which comprises the following steps of: S1, acquiring the respiratory rate, the heart rate and the oxygen saturation of a user in a sleep period, and constructing a physiological data set; s2, filtering, denoising and cleaning the collected original physiological data set to remove noise and abnormal values; s3, key physiological features are extracted from the preprocessed physiological data set; s4, training a judgment model by utilizing improved deep learning, and performing comprehensive analysis by the judgment model according to the extracted physiological features to judge whether obstructive sleep apnea occurs or not; s5, the system monitors the sleep state of the user in real time, and provides instant feedback or alarm when obstructive sleep apnea is detected; and S6, the system performs analysis according to the physiological features of different users, and provides judgment results and health suggestions for individuals. The accuracy and real-time performance of obstructive sleep apnea judgment are improved, and accurate sleep apnea monitoring and judgment are achieved.

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 provides an obstructive sleep apnea syndrome monitoring device and a storage medium, and relates to the technical field of sleep monitoring. The device comprises an electrocardio acquisition assembly used for acquiring electrocardio signals of a user in the sleep period; and the processor is used for determining a waveform feature set of the electrocardiosignal in a target time period, determining sleep apnea features of the target time period based on the waveform feature set, and determining an obstructive sleep apnea syndrome monitoring result of the target time period based on the sleep apnea features. According to the invention, the operand of the processor can be reduced, so that the monitoring efficiency of the obstructive sleep apnea syndrome is improved, and the power consumption of the processor is reduced, so that equipment with relatively low electric quantity or operand such as wearable equipment can comprise the processor, whether a user is in an obstructive sleep apnea state or not can be monitored in real time, and the user experience is improved. And the timeliness and the efficiency of monitoring the obstructive sleep apnea syndrome are 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 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 invention provides a communication method and device, which are used for improving the energy-saving effect of network equipment. The method comprises: a terminal device receiving first information for indicating a sleep period of a network device, and determining a paging frame according to the sleep period. And the terminal equipment monitors the paging message on the paging frame. In the embodiment of the invention, when the terminal equipment and the network equipment determine the paging frame, the interval of the paging frame is prolonged by combining the sleep period of the network equipment, for example, the paging frame can be concentrated in the duration time of the network equipment, so that the waking frequency of the network equipment can be reduced, and the energy-saving effect of the network equipment is improved.

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.

In wireless communication, a user equipment (UE) may be configured to have lower-power sleep periods as well as wake-up periods. During a wake-up period, the UE may perform a paging operation that involves synchronization, monitoring for and decoding a paging notification that schedules a paging message, and receiving and decoding the paging message. When performing the paging operation, the UE has an opportunity to measure one or more properties of the wireless channel. In some embodiments, a UE is configured to measure a channel property using a downlink signal during a paging operation and report the measurement. Technical benefits may include the ability to measure a property of the wireless channel using a downlink signal already transmitted for the purposes of the paging operation, and report the measurement result to the network, which the network may then use for more effective control and operation.

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.