355 results about "Sleep staging" patented technology

The invention relates to the field of sleep quality monitoring and evaluation, in particular to a sleep quality monitoring and evaluation method and system. Firstly, acquisition and quality enhancement preprocessing are carried out on polysomnogram (PSG) data, and then an adaptive spatial-temporal feature learning network KAN-BiLSTM-Transform is designed and is used for automatically extracting discriminative features of sleep signals. The network is composed of a Kolmogorov-Arnold network (KAN), a bidirectional long short-term memory (BiLSTM) and a Transform, and the space-time dependency relationship of a multi-mode signal can be fully mined. In the training stage, weighted Dice loss and time sequence smooth loss are introduced, and a self-supervised pre-training normal form is adopted for parameter optimization. During application, sleep signals are input into the trained network, and the sleep stages corresponding to the time window can be automatically predicted. The method is expected to become an auxiliary tool for detecting and evaluating the sleep quality of people.

The invention provides a sleep stage detection method based on a multi-modal and visual transformation network, and the method comprises the steps: extracting EEG channel signals and EOG channel signals in PSG, carrying out the feature extraction of different frequency bands through a multi-scale convolutional network, and fusing the multi-modal features to enhance the classification accuracy. Thirdly, the time-space relevance of network modeling signals is transformed through vision, and then different stages of sleep are effectively recognized; the method also provides confidence evaluation of the classification result, and the reliability of the detection result is enhanced. The automatic detection method can be used for clinical sleep staging to assist doctors in evaluating sleep quality and diagnosing sleep disorders.

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

The invention discloses a non-contact sleep staging identification and structural analysis method and system, and belongs to the field of sleep monitoring. The method comprises the steps that thoracic physiological movement signals to be staging are obtained, and the thoracic physiological movement signals to be staging are a plurality of sleep segments and comprise thoracic respiratory movement signals and heartbeat movement signals of a human body; and inputting the thoracic physiological movement signal to be staging into a pre-trained neural network model to obtain a sleep staging recognition and structure analysis result output by the neural network model. The method has an accurate sleep structure parameter estimation capability, and is of great significance to sleep diagnosis; and meanwhile, the system is low in cost and convenient to use, does not generate any constraint and sleep influence on the user, can be applied to daily intelligent sleep monitoring, and has home health monitoring application value.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

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 discloses a sleep monitoring method and system based on a non-contact video data sequence. The method comprises the steps that S1, video data streams of human body sleep are collected through a camera system; utilizing a YOLOv11 network to obtain face video data and thoracoabdominal video data; s2, the RPPG signal extraction model extracts and obtains an RPPG signal by using the face video data; s3, the breathing signal extraction model extracts thoracic and abdominal micro-motion change characteristics in the thoracic and abdominal video data by using an optical flow method, and noise filtering processing is carried out to obtain thoracic and abdominal motion signals as breathing signals; s4, the oxyhemoglobin saturation extraction model detects and outputs an oxyhemoglobin saturation signal by using the RPPG signal; and S5, performing multi-modal fusion analysis on the multi-physiological index fusion recognition model according to time slice T1 division to obtain a long-time-sequence sleep stage staging result. According to the invention, the physiological index signals are extracted and recognized by adopting the non-contact video data sequence, and the sleep stage staging result with a long time sequence is obtained, so that high-precision sleep monitoring and sleep stage recognition are realized.

The invention provides a sleep monitoring model training method, a sleep monitoring method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a data set S1 and a data set S2; a pure radar feature extractor M3 is trained by using a pre-trained teacher network model M2 and the data set S1, the teacher network model M2 fuses the radar data and the pulse wave data in the data set S1 and outputs fused feature data, and the pure radar feature extractor M3 performs feature extraction on the radar data in the data set S1 and outputs radar feature data; training a bimodal sleep monitoring model M4 and a pure radar modal sleep monitoring model M5 by using the data set S2, and determining first sleep stage and / or respiratory event information by a first recognition layer according to fusion feature data output by the teacher network model M2, and the second identification layer determines second sleep stage and / or respiratory event information according to the radar feature data output by the pure radar feature extractor M3. According to the invention, the sleep monitoring task is efficiently completed at low cost.

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

The invention provides a sleep staging analysis method and device based on electroencephalogram signals, and belongs to the technical field of data analys.The method specifically comprises the steps that staging deviation time periods in sleep analysis results are determined according to the sleep staging results corresponding to different credible monitoring positions; determining the matching conditions of the electroencephalogram signals of different credible monitoring positions at different moments and the monitoring data of other dimensions, and determining the abnormal change moments of different credible monitoring positions by combining the deviation conditions of the electroencephalogram signals at different moments and other credible monitoring positions; according to the distribution data of the abnormal change moments of the different credible monitoring positions in the staging deviation period and the early period of the staging deviation period, when it is determined that the reference monitoring position exists in the staging deviation period, the sleep staging result in the staging deviation period is determined through the reference monitoring position, and the accuracy of the sleep staging result is improved.

The invention relates to a non-contact sleep monitoring method based on cross-modal compensation. In order to overcome the defects that vital sign radar is single in physiological information and prone to being interfered, the method is achieved through multi-modal fusion of a radar sensor, a video sensor and an audio sensor. Specifically, a radar sensor obtains heartbeat, respiration and body movement physiological information through chest mechanical waves, a video sensor obtains heartbeat, respiration and body movement physiological information through a photoplethysmography (PPG), an audio sensor receives sound and the like, feature parameters are extracted, a ReliefF algorithm is used for feature optimization, a machine learning algorithm (Subspace KNN, Bagged Tres and the like) is adopted for classification, and the feature parameters are extracted. And finally, decision-level fusion is carried out through a naive Bayes classifier, and a sleep staging result is output. Experimental results show that the method is robust in a complex environment, the accuracy and reliability of sleep staging can be remarkably improved, cross-modal compensation is achieved, and measurement is more continuous and accurate.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention provides a sleep staging detection method and a sleep staging detection device.The method comprises the steps that a polysomnogram of a target object within a preset time period is obtained; inputting the plurality of physiological signals into a first model to obtain frequency signals under different frequency characteristics; the multiple frequency signals are input into a second model to obtain two intermediate layer signals, the second model comprises a character string operation module and two first channels, the character string operation module is used for integrating the multiple frequency signals into a target frequency signal, and the two first channels are used for receiving the target frequency signal; the input of each first channel is a target frequency signal received from the character string operation module, and the output of each first channel is a corresponding first intermediate layer signal; and inputting the two first intermediate layer signals into a third model to obtain a sleep staging result of the target object. According to the sleep staging method and device, the physiological signals in the polysleep map are processed and analyzed, and the accuracy of the sleep staging result is improved.

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

The invention relates to the technical field of medical diagnosis, and discloses a blood oxygen monitoring system fused with sleep rhythm modeling, which comprises the following steps: identifying a sleep stage conversion moment through a photoelectric volume pulse wave signal, detecting a blood oxygen saturation valley value in a window after conversion, and calculating response lag duration; according to the method, the physiological rhythm is converted into a natural probe for the compensatory ability of the respiratory system, dual diagnosis of sleep staging and respiratory function is realized through a single-path signal, and meanwhile, body movement interference is converted into an observation window for the respiratory stress recovery ability. The diagnosis reliability in a home monitoring scene is remarkably improved, the system completes core analysis at an edge node, and a respiratory function evaluation report which can be directly used for clinical decision making is output.

The invention belongs to the technical field of biomedical engineering, and particularly relates to a lightweight automatic sleep staging method capable of being used for on-chip migration. The method comprises the following steps: carrying out filtering and baseline correction preprocessing on an electroencephalogram signal; constructing a lightweight deep learning model composed of a multi-layer one-dimensional convolutional neural network, a channel attention module, a full connection layer and a Softmax layer; performing model training by adopting a cross entropy loss function and double optimizers, and optimizing the model in combination with a cross validation strategy; and compressing the model parameter quantity and the reasoning operation time to generate a deployable file. According to the method, the number of convolution channels is adjusted layer by layer, the dimensionality of a full connection layer is cut, the trainable parameter quantity of the model is compressed to be less than 200000, the feature selectivity is improved through the lightweight channel attention module on the premise that the calculation burden is hardly increased, the method can be deployed in embedded equipment to realize real-time sleep monitoring, and the method is suitable for popularization and application. And a solution is provided for portable medical diagnosis.

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

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

The invention discloses a sleep staging method and system based on multimode signal fusion and context awareness. The sleep staging method based on multimode signal fusion and context sensing comprises the following steps: acquiring a multimode physiological data set; performing multi-scale feature extraction on the multi-modal physiological data set to obtain a multi-scale feature set; fusing the multi-scale feature set based on a lightweight model to obtain a fused feature set; classifying the fused feature set, and obtaining a sleep staging result based on a classification result; wherein the multi-scale feature extraction of the multi-modal physiological data set comprises the following steps: segmenting the multi-modal physiological data set into a frame sequence set; and performing historical context feature extraction, future context feature extraction simulation and timestamp feature extraction on the frame sequence set to obtain a context feature set. In this way, the staging continuity can be enhanced, and the misjudgment rate caused by burst signal fluctuation is reduced.

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

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

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

The invention discloses a polymorphic sleep data fusion self-attention sleep system and method. The system comprises a data acquisition module which is responsible for acquiring environmental data and physiological data of a user; the signal preprocessing module is used for performing segmentation, normalization and de-noising processing on the collected EEG and EOG signals; the frequency division multiplexing module is used for decomposing the preprocessed signal frame into a plurality of narrowband signals by using a frequency division multiplexing technology; the feature extraction module is used for extracting features from the decomposed narrowband signals; the model training module is used for training a model by utilizing the feature set; the sleep staging module is used for performing real-time or offline sleep staging through the trained model; the sleep quality evaluation module is used for calculating a sleep quality index based on a sleep staging result; the correlation analysis module is used for performing correlation analysis on the relationship between different sleep stages and the sleep quality indexes to generate a quality evaluation model; and the early warning system module is used for setting an early warning threshold value according to the sleep quality evaluation result and the quality evaluation model.

The invention discloses a device with sleep assessment and intervention functions, which is arranged on a pillow or a back cushion, and comprises an information acquisition module for acquiring physiological data, head posture data and sound data of a human body, and sending the acquired data to a sleep assessment module, the sleep assessment module is used for extracting multi-modal sleep features according to the collected data and performing sleep assessment according to the multi-modal sleep features; wherein the source of the physiological data is analyzed by using the head posture data, and a dynamic weight is given, so that a self-adaptive sleep staging algorithm is completed; and the sleep intervention module is used for acquiring a sleep intervention scheme according to the sleep evaluation result, and activating different working modes of a vibration motor and / or a loudspeaker according to the sleep intervention scheme so as to achieve the effect of intervening the sleep of the user. According to the method, the source of the physiological data is analyzed by using the head posture data, and the dynamic weight is given, so that a self-adaptive sleep staging algorithm is completed, and the sleep staging evaluation accuracy of a real scene is improved.

The invention discloses an electrocardio sleep staging method based on electroencephalogram and electrocardio coupling learning, which is used for improving a UNet basic feature extraction structure so that the UNet basic feature extraction structure can extract multi-level and multi-scale richer information. Through a pooling layer, a convolution layer, a normalization layer and an activation layer, fusion processing and scale normalization are respectively carried out on basic electrocardio and electroencephalogram features, and a mean square error similarity function is adopted to supervise coupling learning, so that features learned by electrocardio are similar to electroencephalogram as much as possible, and more effective features used for electrocardio sleep staging are extracted. And the accuracy and efficiency of (electrocardio) sleep staging are effectively improved.

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

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

The invention discloses a plateau tourism bedside intelligent interaction microenvironment adjusting system and method, and the system comprises a signal collection and transmission unit which is responsible for collecting bedside signals, carrying out the filtering processing of the bedside signals through an MCU, and transmitting the bedside signals to a cloud platform; the intelligent eyeshade data receiving unit is responsible for receiving the electro-oculogram, the eye movement and the blood oxygen collected by the intelligent eyeshade, transmitting the electro-oculogram, the eye movement and the blood oxygen to the MCU for processing, and transmitting the processed electro-oculogram, the cloud platform is responsible for receiving data from the signal acquisition and transmission unit and the intelligent eyeshade data receiving unit, and storing, analyzing and processing the data; the artificial intelligence unit is responsible for mining the data received by the cloud platform and identifying the physiological status, sleep quality and behavior habits of the tourists through a health behavioral theory and an artificial intelligence sleep staging algorithm; the regulation and control unit is responsible for analyzing an analysis result of the cloud platform and an instruction of a user; and the terminal equipment is responsible for providing a user interface.

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.