972 results about "Sleep quality" patented technology

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention particularly relates to a quartered sleep state monitoring method and a quartered sleep state monitoring system based on a non-contact vibration sensor. The quartered sleep state monitoring method and the quartered sleep state monitoring system are particularly suitable for realizing non-sensitive sleep quality evaluation through an intelligent bed in a family environment. Comprising the following steps that 1, vibration signals of a human body in the sleep process are continuously collected through a non-contact vibration sensor; step 2, preprocessing the vibration signal, including denoising and segmentation processing, to obtain time domain vibration data; step 3, performing frequency domain transformation on the time domain vibration data, and extracting frequency domain features including respiratory rate, heart rate and heart variability parameters; 4, time alignment is carried out on the frequency domain features and sleep staging labels synchronously obtained through a polysomnography, and a training data set is constructed; an XGBoost classification model based on time-frequency domain feature fusion is provided, fundamental frequency and harmonic energy proportions of respiratory signals are extracted through short-time Fourier transform, and quartering classification is achieved in combination with the LF / HF ratio of heart rate variability.

The invention relates to the field of smart home, and provides a smart bed sleep quality analysis method, which comprises the following steps: firstly, determining a sleep monitoring area of a smart bed, obtaining environment information, querying sleeper characteristics according to the environment information, determining target sleep data, performing segmentation processing, calculating a sleep fluctuation value and a characteristic deviation degree, and correcting to obtain a sleep correction parameter; querying key correction indexes, performing trend marking, dividing continuous association time periods, updating to obtain trend updating indexes, mining index association characteristics, constructing a sleep quality analysis strategy in combination with life data of a sleeper, performing simulation analysis to obtain strategy simulation scores, and finally generating a sleep quality analysis report corresponding to the intelligent bed according to the strategy simulation scores. The analysis effect of the sleep quality of the intelligent bed can be improved.

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

The invention relates to the technical field of sleep stage classification, and particularly provides a sleep quality monitoring method fusing multi-scale features. The method comprises the following steps: preprocessing input signals, and inputting the input signals into a multi-mode sleep signal analysis network after ensuring that the signal amplitudes are consistent; capturing features of a plurality of time scales through a multi-granularity feature learning module, and carrying out feature fusion; integrating spatial information by using a spatial-temporal feature enhancement module, and mapping the three-dimensional features into a two-dimensional time sequence feature sequence; on the basis of a time context module of Mama, long-distance time dependence is modeled with linear complexity, features are averagely aggregated along the time dimension through a classification module, a classification result is output through a full connection layer and an activation function Softmax, and the method improves the accuracy of sleep stage classification while ensuring the high efficiency of sleep stage classification.

The invention discloses a health trend analysis early warning method based on big data, and relates to the technical field of data processing, and the method comprises the steps: analyzing female physiological cycle data based on big data, and building a health trend analysis model; the method comprises the steps of obtaining a physiological cycle type of a target user, collecting a sleep state, a physiological cycle and physical sign data of the target user, generating standard health monitoring data, initializing an analysis model according to basic information of the user, performing sleep quality change analysis, evaluating a health trend of the user according to an analysis result in combination with physiological cycle characteristics, and providing personalized sleep improvement suggestions. The technical problem that traditional health management in the prior art neglects the influence of the female physiological cycle on the health state and the sleep quality and lacks pertinence and individuation is solved, and individualized health management is provided by combining the female physiological cycle data and the sleep state data. And the sleep quality of women in different physiological cycle stages is improved.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

The invention discloses a forehead electroencephalogram rhythm-driven sleep state staging method based on multi-dimensional features, and belongs to the field of electroencephalogram signal processing in the field of digital signal processing, and the method comprises the following steps: step 1, preparing a sleep physiological signal data set; 2, preprocessing the forehead electroencephalogram signals; 3, rhythm wave extraction is conducted on the forehead electroencephalogram signals; 4, time domain feature values, feature values based on power spectral density and feature values based on multi-scale entropy of the forehead electroencephalogram signals and the electrooculogram signals are calculated respectively; step 5, sending the characteristic values calculated in the step 4 into an SVM (Support Vector Machine) for automatic staging, and finally optimizing a characteristic value group to obtain an automatically predicted sleep staging result; the method has high accuracy, can provide visual feedback for the user, and helps the user to know the sleep quality and state of the user so as to improve the sleep habit of the user.

The invention relates to the technical field of intelligent bed early warning, and discloses an intelligent bed integrated AI monitoring and early warning method and system, and the method comprises the steps: collecting sleep-related data, and carrying out formatting and quantity stiffening processing to obtain target sleep data; constructing a sleep stage thermodynamic diagram, analyzing the sleep cycle characteristics of the intelligent bed user, analyzing the body movement frequency condition of the intelligent bed user in the sleep process by using the target sleep data, and analyzing the overall sleep characteristics of the intelligent bed user; analyzing the sleep quality of the intelligent bed user, and when the sleep quality is poor, analyzing cardiovascular health characteristics and respiratory system health characteristics of the intelligent bed user so as to analyze preliminary health state characteristics of the intelligent bed user; and collecting sleep adjustment related data after the intelligent bed user uses the adjustment equipment, analyzing the health state adjustment characteristics of the intelligent bed user, and performing sleep health early warning on the intelligent bed user in combination with the preliminary health state characteristics. According to the invention, the early warning reliability of the intelligent bed integrated AI can be improved when the number of influence factors is large.

The invention relates to the technical field of intelligent monitoring and control, and discloses a sleep environment monitoring device and a monitoring system.The sleep environment monitoring device comprises a multifunctional integrated sensor used for monitoring environment parameters in real time; the intelligent interaction and display unit is used for displaying environment parameters and sleep quality analysis reports and providing personalized settings; the environment adjusting equipment linkage unit comprises an air conditioning system, a fresh air system, a light module capable of adjusting color temperature and a driving device of an electric curtain; and the communication module is used for data transmission. Through the multifunctional integrated sensor module, accurate monitoring of environmental parameters is realized. The intelligent interaction and display module simplifies the operation process through a touch screen and a voice recognition technology. The personalized sleep environment optimization algorithm module calculates optimal environment parameters according to personalized information and real-time data, and the multi-mode environment interference early warning and processing module monitors and processes interference factors in the environment and cooperates with the environment adjustment equipment linkage module to achieve automatic adjustment of the environment parameters.

The invention discloses a personalized sleep intervention and first aid method and system based on multi-modal data fusion. The method comprises the steps that physiological index data and motion data of a user are collected in real time through wearable equipment; generating a sleep quality score based on the physiological index data in combination with a preset sleep quality evaluation model; generating a personalized sleep intervention scheme according to the sleep quality score and the motion data; the sleep intervention module is started by manually triggering or automatically detecting the insomnia state of the user; according to the physiological index data monitored in the sleeping process, the indexes exceeding the standard are recorded, and a prompt is given out after waking up the next day, or a first-aid mode is triggered. The personalized dynamic sleep intervention scheme is generated by quantifying the physiological indexes of the user, and the limitation of a single sensor is broken through; sleep intervention can be triggered in multiple modes, the intervention intensity can be dynamically adjusted according to the real-time physiological state, the first-aid mode can be automatically started, and first-aid delay is avoided.

The invention belongs to the technical field of probiotics, and particularly relates to lactobacillus reuteri ShuiMian with a sleep aiding effect and application of the lactobacillus reuteri ShuiMian. A new ShuiMian strain is separated from feces of a healthy infant in Guangzhou city, Guangdong province, PTZ is adopted to construct a zebra fish insomnia model, and research finds that the strain can inhibit eye movement frequency, inhibit brain discharge, reduce dopamine secretion, promote GABA secretion and promote Gabra gene expression, and the zebra fish insomnia model can be used for treating insomnia. The effect of promoting sleep is achieved. Therefore, the strain has a unique sleep-promoting mechanism, can assist in developing novel sleep-promoting medicines in the field of medicines, and provides a safer and more effective treatment scheme for insomnia patients. In the health care industry, a nutritional supplement can be prepared, the mass sleep quality is improved, and the development of sleep health related industries is promoted.

There is provided a computer-implemented method for determining sleep misperception of a subject during a sleep session. The method comprises receiving sleep condition information. The sleep condition information comprises at least one of SDB information and insomnia information. The SDB information is representative of a sleep disordered breathing (SDB) condition of the subject based on a number of SDB events of the subject per unit of time. The computer-implemented method comprises receiving sleep quality information representative of a quality of sleep of the sleep session experienced by the subject. The computer-implemented method comprises determining a degree of sleep misperception based on the sleep condition information and the sleep quality information. The sleep misperception is representative of a difference between a subjective total sleep time of the sleep session experienced by the subject and an objective total sleep time of the subject in the sleep session.

The invention discloses a compound composition for improving sleep disorders, which is characterized by comprising the following components: an inhibitory neurotransmitter component, melatonin and a nano-lipid carrier, the nano-lipid carrier comprises phospholipid and cholesterol, the weight ratio of phospholipid to cholesterol is (1-6): 1, the weight ratio of phospholipid to cholesterol is (1-6): 1, and the weight ratio of melatonin to cholesterol is (1-6): 1. The inhibitory neurotransmitter component is wrapped in the nano-lipid carrier to form lipid nanoparticles, and the weight ratio of the inhibitory neurotransmitter component to the nano-lipid carrier is 1: (1-15). The compound composition can be prepared into a compound oral mucosa preparation, can be rapidly disintegrated when encountering saliva in the oral cavity, is good in patient compliance and small in side effect, all the components play a synergistic effect from different mechanisms through multiple target points, and the compound oral mucosa preparation can improve the sleep quality, prolong the sleep time and shorten the sleep latency; and patients with sleep disorders are helped to improve insomnia from the pathologic root.

The invention relates to an exosome composition for improving sleep quality. The exosome composition comprises a lavender exosome, a chamomile exosome and a saffron crocus exosome. The exosome composition disclosed by the invention can be used for improving the expression level of GABA (gamma-aminobutyric acid) related proteins of neuronal cells, promoting the expression of brain-derived neurotrophic factors (BDNF) of the neuronal cells, promoting the synaptic growth of the neuronal cells, improving the CL <-> level of the neuronal cells and inhibiting the inflammatory factor level of microglial cells. The exosome composition disclosed by the invention can be used for improving sleep quality reduction caused by various body discomfort, and has a more obvious using effect than that of the same Chinese herbal medicine extract.

The invention belongs to the technical field of sleep quality analysis, and particularly relates to a sleep quality analysis system and method based on a medical bed sensor, so as to solve the technical problem that an existing method excessively depends on duration statistics and neglects sleep continuity and periodic rhythmicity. The analysis method comprises the following steps: S1, acquiring sign signal data acquired by a sensor on a medical bed in a sleep cycle, and dividing the sign signal data into a plurality of continuous time slices; s2, extracting physiological indexes of respiratory rate, heart rate and body movement amplitude in each time slice, and calculating variation depth of the corresponding time slice; s3, generating a variation depth baseline; calculating an unsteady state disturbance factor according to the occurrence frequency and the duration of the micro-awakening disturbance fragment; and S4, generating a sleep quality comprehensive score. According to the analysis method, the comprehensiveness and objectivity of the evaluation result are improved, and the sleep health condition is reflected more truly.

The invention discloses a probiotic preparation, the preparation is composed of an effective amount of anti-TGF-beta1 monoclonal antibody, an effective amount of probiotics and other auxiliary materials, and amino acid sequences of a heavy chain variable region and a light chain variable region of the anti-TGF-beta1 monoclonal antibody are shown as SEQ ID NO.1 and SEQ ID NO.2. The invention further discloses a preparation method of the probiotic preparation. The probiotic preparation disclosed by the invention can effectively improve sleep quality and solve the problem of insomnia generally faced by modern people. By combining the probiotics with the anti-TGF-beta1 monoclonal antibody, the preparation not only can regulate the intestinal microecology, but also can promote sleep by influencing the nervous system, and has a remarkable improvement effect. Meanwhile, the preparation method disclosed by the invention is simple, economical and suitable for large-scale production, and has a good market application prospect.

The invention discloses a sleep quality improving system based on big data analysis, and relates to the technical field of big data. According to the system, a multi-dimensional sleep state monitoring matrix is constructed, effective intervals of monitoring points are optimized in combination with dynamic adjustment factors and a deep learning algorithm, and redundant data interference is eliminated; generating a sleep intervention regulation factor based on the environmental influence function and the physiological rhythm constraint factor, and training the sleep intervention function through a genetic algorithm to dynamically adjust the intervention intensity; and generating an intervention instruction by utilizing the reinforcement learning model and combining the real-time physiological data of the user, the environmental parameters and the sleep stage prediction result, and controlling the starting sequence and parameter adjustment of the monitoring points until preset ideal sleep state parameters are met. According to the method, the problems of insufficient monitoring precision, poor environment adaptability, intervention lag and the like in a traditional method are solved, the accuracy and the real-time performance of sleep intervention are improved, and the method is suitable for the fields of smart home, medical health and the like.

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 belongs to the technical field of bedding, and discloses a mattress supporting performance self-adaptive adjusting method based on pressure comfort and a mattress, the method is applied to a mattress provided with a pressure detection assembly and an air pressure adjusting assembly, and the method comprises the following steps: obtaining pressure index data and sleep quality data of each partition of a body; calculating a pressure comfort comprehensive value of each partition of the body based on each pressure index data of each partition of the body; based on the intermediary factors influencing the sleep quality, constructing a correlation model of the pressure comfort comprehensive value of each partition of the body and the sleep quality; real-time data of pressure indexes of all the partitions of the body are obtained, the real-time sleep quality is calculated through the correlation model, and based on the real-time sleep quality and a preset sleep quality threshold value, the air pressure of the mattress areas corresponding to all the partitions of the body is independently adjusted at the same time. The overall comfort is defined by calculating the comprehensive value of the pressure comfort of each partition of the body, the overall dynamic balance adjustment of the supporting performance of the mattress is realized by utilizing a correlation model, and the overall supporting performance unbalance caused by local adjustment is avoided.

The invention discloses a sleep management optimization system and method based on image recognition and environmental perception, and the system comprises an image recognition module which collects image data, and obtains first sleep state data from the image data through optimizing a bionic neural network; the environment sensing module is used for collecting sleep environment data, obtaining time dimension features and space dimension features from the sleep environment data through a space-time environment sleep quality correlation model, and adaptively adjusting weights through a small neural network to obtain second sleep state data; the sleep management module is used for generating a continuously optimized sleep management strategy through an improved reinforcement learning strategy based on the first sleep state data and the second sleep state data, personalized suggestions can be provided for the sleeper according to different sleep states and environment conditions, and diversified requirements of different users are met.

A sensor system, a sleeping space, and a method for optimizing sleep quality and comfort. The sensor system for sleep monitoring is incorporated within household appliances like ceiling fans or lighting fixtures. It allows precise detection and classification of human movements and behaviors during sleep. The system not only significantly enhances the quality of sleep tracking but also optimizes the operation of ceiling fans, leading to a more comfortable and safer sleep environment. The system may be integrated into other similar indoor electronic devices, including lighting fixtures, lighting bulbs, mirrors, and air conditioning units with full functionality. The system may be integrated into a standalone radar device with full functionality. This system has wide-reaching implications for personalized healthcare, early disease detection, and improved sleep quality, ultimately contributing to the well-being of individuals.

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 an intelligent bed control method and system and an intelligent bed. The method comprises the steps that N groups of breathing sound signals sent by an object are collected according to a preset time interval through sound signal collection equipment, and the object is on an intelligent bed; detecting whether a snore signal exists in the N groups of breathing sound signals or not through snore detection equipment; when it is detected that in the N groups of breathing sound signals, the group number of the breathing sound signals with snore signals is larger than M, the intelligent bed is controlled to execute the snore stopping action, and M is an integer smaller than or equal to N. The technical problems that an intelligent bed in the related technology does not have a snore stopping function, the user experience feeling is poor, and the sleep quality of a user is not improved are solved.

The utility model relates to the field of medical instruments, in particular to an auxiliary sleep device with a sound wave hypnosis function, which comprises a base, a support block, a seat, a backrest, a headrest, an electric push rod and the like, the upper side of the middle of the base is connected with the support block, the upper side of the support block is connected with the seat, the left part of the seat is rotatably connected with the backrest, and the upper side of the backrest is connected with the headrest. The left part of the backrest is rotationally connected with an electric push rod. The window shade is put down by pushing the connecting frame to move and then reversely pulling the pull bead chain, and then sound waves are sent out by the ultrasonic sleep aiding device for hypnosis to assist sleep, so that the sound waves can be used for hypnosis of a user to assist sleep, relax the brain and accelerate falling asleep, and meanwhile, the window shade can be used for shading. A user is in a dark environment, light stimulation is avoided, the sleep quality is improved, and use is convenient.

The invention discloses an intelligent control method for a sleep cabin lighting system, and the method is high in adaptability: through monitoring the physiological indexes (such as heart rate, skin conductance and the like) and subjective pressure feeling of a user in real time, lighting parameters, such as brightness, color temperature and the like, can be dynamically adjusted, so as to meet individual demands of different users. The system can adaptively cope with environment changes and user state changes, and the optimal lighting effect is ensured. Reasonable illumination adjustment is beneficial to adjustment of the physiological rhythm of the human body, and the user is promoted to enter a good sleep state. The sleep quality can be improved and the rest experience of the user can be improved by reducing interference and pressure of illumination on the user to the maximum extent. The intelligent control system can adjust lighting parameters in real time according to user requirements, and unnecessary energy consumption waste is avoided. By optimizing the lighting strategy, the overall energy consumption can be greatly reduced, and the energy-saving target is achieved. And real-time feedback and optimization: the system can collect physiological feedback data of the user in real time and perform analysis and optimization in combination with subjective feelings of the user. Through online learning and algorithm iteration, the system can continuously improve the control precision and the user satisfaction. The user does not need complex operation, and the system can automatically provide the best lighting experience according to the user state. The control burden of the user is relieved, and the usability and friendliness of the system are improved.

The invention relates to the field of sleep medicine, in particular to a wearable monitoring system for sleep-awakening rhythm disorder and a method thereof. Comprising a monitoring module, a signal processing module, a signal analysis module and an intervention scheme execution module. The monitoring module is used for acquiring collective acceleration, PPG signals and emotional state data; the signal processing module is used for filtering and down-sampling; the signal analysis module extracts time-domain and non-time-domain features by using a pulse neural network; and the intervention scheme execution module selects a personalized phototherapy and vibration intervention scheme by adopting a Q-learning algorithm according to the feature data. According to the system, through technical innovation and ingenious design, high intelligence and individuation are achieved, the sleep quality and the daytime function of the user are effectively improved, and a new tool and method are provided for sleep medical research.

The invention relates to the technical field of sleep quality evaluation, in particular to a sleep monitoring optimization system based on artificial intelligence. The system comprises the steps of collecting sleep monitoring data of a user at different collection moments, extracting dominant frequency components in snore audio data through spectrum envelope analysis, extracting audio primitives based on the dominant frequency components, inputting the audio primitives into a snore classification model, outputting a snore category corresponding to the snore audio data of the user, and outputting the snore category corresponding to the snore audio data of the user. Carrying out Bayesian inversion analysis on the brain wave data to obtain a posterior sample of random field model parameters, executing reliability analysis based on the posterior sample to calculate a posterior failure probability, obtaining a first sleep quality index of the user based on the posterior failure probability, obtaining a second sleep quality index of the user based on vital sign data analysis, and sending the second sleep quality index to the user; and obtaining a user sleep quality evaluation result based on the snore type, the user first sleep quality index and the user second sleep quality index. The accuracy and efficiency of user sleep quality evaluation can be improved.

A breathable and comfortable inflatable mattress includes a top layer, a surrounding band and a bottom layer, which together define a cavity of the inflatable mattress. The top layer includes a breathable layer and a ticking layer, the ticking layer and the surrounding band are connected by welding or bonding. The breathable layer is arranged on the ticking layer, and the breathable layer is connected to the surrounding band via a sealing layer. The inflatable mattress according to the present invention is replaced the traditional flock / PVC or PVC material with comfortable fabric for the top layer, which can greatly improve comfort and breathability, avoid noise generated by friction between the user's body and PVC, and improve user' s sleep quality, thus meeting needs of users widely.

The invention relates to the technical field of deep sleep management, in particular to a deep sleep staging management method and system based on electroencephalogram feedback. Comprising the steps that electroencephalogram feedback of a target user is collected, an actual deep sleep waveform is extracted and judged with an expected spectral domain of an ideal state transfer stage, and an unreached stage is locked; in an unreached stage, constructing a signal mode prediction model, and determining an optimal guide window stage by combining with intervention entry point migration analysis; performing wavelet basis decomposition to obtain an excellent wave band and a clutter band of the optimal guide window period; when the signal-to-noise ratio is high, crosstalk coupling analysis is executed, and white noise is generated by using the rhythm coding library for guide management; when the signal-to-noise ratio is low, light environment and temperature response regulation is planned, and deep sleep is managed in combination with a color coding chain and a signal excitation result. According to the method and the system, guide management of different decisions can be executed in the stage of entering the deep sleep of the target user, and the deep sleep quality, stability and continuity are improved.