294 results about "Sleep staging" patented technology

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 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.

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 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 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 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 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 provides a sleep environment temperature management method and system based on galvanic skin, and belongs to the technical field of temperature control, and the method specifically comprises the steps: determining a temperature change abnormal device of an internal monitoring device according to the change condition of the monitoring data of the internal monitoring device; according to the distribution data of the temperature change abnormal device and the change condition of the monitoring data, when it is determined that the first temperature adjustment strategy does not need to be adopted for temperature adjustment processing, the sleep staging result of the monitoring target is obtained, and the temperature adjustment strategy of the sleep environment of the monitoring target is determined in combination with the monitoring data of different internal monitoring devices; and the interference on the sleep of the monitored target is reduced.

The invention discloses a multi-modal unsupervised cross-domain sleep staging method, which adopts adversarial learning and designs a multi-modal convolution feature extractor module, a domain generalization feature enhancement module and a domain attention module. The method comprises the following steps: firstly, designing a multi-mode convolution feature extractor for physiological signals of two modes of electroencephalogram and electro-oculogram; for the electroencephalogram signals, convolution kernels of different scales are adopted to extract multi-scale features; for the electro-oculogram signal, firstly, the electro-oculogram signal is converted into a two-dimensional frequency spectrum through Fourier transform, and then feature extraction is carried out through two-dimensional convolution, so that unique physiological information of the electro-oculogram signal is fully captured. Thirdly, adaptively adjusting data distribution of a source domain and a target domain by using a domain generalization feature enhancement module, reducing inter-domain differences, and adaptively enhancing high-discrimination-force features; the domain attention module reserves key domain specific features in the adversarial learning process, the classification precision and generalization ability of the model are remarkably improved, and the method shows excellent performance in an unsupervised cross-domain sleep staging task.

The invention provides a sleep staging method based on spatial-temporal feature coding and multi-source fusion, and belongs to the technical field of big data analysis. According to the method, collected multi-channel multi-source physiological data including electroencephalogram, myoelectricity and electro-oculogram signals are utilized to construct a sleep staging neural network model fusing the multi-channel multi-source physiological data; establishing a graph space encoder for modeling multi-channel spatial features, and encoding interaction and position information among different channels; establishing a multi-signal source fusion module for fusing multi-source signals, and identifying different contributions of multi-signal sources to stages based on an attention mechanism; a time sequence Transform encoder used for modeling time features is established, and local and global time features are encoded at the same time. According to the method, automatic staging of sleep can be realized, the staging accuracy is improved, personal help can be provided for individuals, a convenient tool is provided for doctors, and heuristic auxiliary guidance is provided for precision medical treatment.

The embodiment of the invention discloses a personalized dynamic sleep staging method and system. The method comprises the following steps: determining a user category of a user based on feature information of the user; and taking a basic sleep staging algorithm corresponding to the user category of the user in the branch algorithm library as a target sleep staging algorithm. And generating sleep staging data suitable for different user groups based on the target sleep staging algorithm and the current sleep data of the users. In addition, the method further comprises the steps that feedback information of the user is obtained, and an optimal sleep staging algorithm is determined based on the feedback information of the user, the current sleep data and the historical sleep data; and updating the target sleep staging algorithm into the optimal sleep staging algorithm, so that the target sleep staging algorithm can consider the real sleep state fed back by the user while considering the sleep data of the user, thereby ensuring the real-time accuracy of the generated sleep staging data.

The invention discloses a sleep staging detection method and system based on a smart watch, and the method comprises the steps: S1, obtaining the heart rate variability, triaxial accelerometer data and body movement intensity index signals of a wearer through a sensor of the smart watch, and carrying out the multi-source signal fusion and time synchronization; s2, carrying out noise reduction processing on the synchronized multi-source signal, and constructing a feature vector; s3, calculating a time sequence self-correlation feature of the feature vector; s4, based on a bidirectional long and short time memory network structure, performing time sequence modeling on the input feature vector and the time sequence self-correlation feature to obtain fused bidirectional feature representation; and S5, performing multi-classification processing on the fused bidirectional feature representation based on a Softmax classifier to realize accurate classification of sleep stages. According to the method, the heart rate variability signal and the three-axis acceleration signal can be fused, and the sleep state of the human body can be accurately recognized and classified in combination with the bidirectional long-short-term memory network and time sequence self-correlation feature analysis.

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.

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

The invention discloses a sleep quality evaluation method and system and a storage medium, and the method comprises the steps: inputting electrocardiosignal segmentation data into a trained sleep staging and breath detection multi-task model, and outputting a preliminary sleep staging result and an apnea detection result; based on the acceleration segmented data, extracting posture features of each corresponding time period, inputting the posture features into a decision tree posture classifier to obtain sleeping posture categories of each time period, and correcting a preliminary sleep staging result and an apnea detection result based on a preset posture and heart rate combined correction rule; and finally, calculating the corrected sleep staging result and the apnea result based on a preset sleep scoring rule to obtain a sleep quality evaluation result. Therefore, accurate quantitative evaluation of sleep staging and sleep quality is realized, and a reliable basis is provided for accurate intervention and monitoring of subsequent sleep health.

The present invention discloses a sleep apnea monitoring method, comprising: acquiring EEG signals, EOG signals, and photoplethysmography signals; automatically staging sleep and calculating sleep duration based on the EEG and EOG signals; counting apnea and hypopnea events based on the photoplethysmography signals; and calculating the AHI index based on the calculated sleep duration (ST) and the number of apnea and hypopnea events (BSN). Furthermore, a corresponding head-mounted sleep apnea monitoring system and electronic device are also disclosed. The present invention enables real-time and accurate sleep staging and apnea diagnosis.