55 results about "Hypopnea" patented technology

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

The invention 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 belongs to the technical field of sleep respiratory disease detection, and relates to a sleep apnea identification method based on a hybrid neural network, which comprises the following steps of: sensing human sleep in a bed surrounding environment by using a millimeter wave radar, and positioning a thoracic cavity position by adopting spatial two-dimensional analysis; the problem that interference exists in radar sign signal extraction of different targets at the same distance is solved. And a respiration signal Shannon entropy is constructed as an iterative index of the whale optimization algorithm, and the index is minimized to realize iterative optimization of variational mode extraction parameters, so that different respiration states are matched with different parameters, and thus a dominant frequency narrowband respiration signal is accurately extracted. A breathing time-frequency energy diagram obtained by data processing is used as input, and a CNN network and a bidirectional long-short-term memory module are used together to construct a feature extraction module, so that the method can recognize the change trend of frequency along with time, capture more comprehensive features, and recognize target sleep apnea more accurately. In addition, the hybrid network enables accurate classification of unmanned scenarios, apnea, hypopnea, and normal respiration.

The invention discloses a method for diagnosing obstructive sleep apnea hypopnea syndrome, and relates to the technical field of medical diagnos.The method comprises the steps that an airflow curve and an oxyhemoglobin saturation curve of a patient during sleep are collected, all respiratory events are traversed and retrieved from the airflow curve, and according to the occurrence time of each respiratory event, all the respiratory events are determined; finding out a corresponding search window from the blood oxygen saturation curve, calculating the area of the search window of each apnea event, accumulating the areas of all closed areas to obtain an area sum, dividing the area sum by the total sleep time to obtain an index value of the sleep apnea specific hypoxia load, and determining the sleep apnea specific hypoxia load based on the index value and an index threshold value. Obtaining the symptom level of apnea of the patient; the method has the advantages of simple and convenient indexes, rapidness and high accuracy, and can be well applied to screening and diagnosis of the obstructive sleep apnea hypopnea syndrome.

Monitoring sleep disorders in a subject using a mobile device, such as a smartphone or other wearable device, includes: co-locating the mobile device with the subject; acquiring an audio signal indicative of the respiration of the subject by means of a microphone of the mobile device; acquiring an accelerometer signal including posture, angle and motion information of the tested object by means of an accelerometer of the mobile device; measuring thoracic movements associated with respiration and respiration of sleep disorders by means of an accelerometer of the mobile device; identifying a plurality of silence events by means of the audio signal; classifying the silence event as apnea or hypopnea; classifying the apnea into a plurality of apnea types by means of thoracic motion recorded with the accelerometer sensor; and associating the classified silence events with respective angle information by combining audio and accelerometer signals.

The present invention relates to a system and method for the enhanced detection and quantification of respiratory events, such as apneas and hypopneas, particularly useful in diagnosing and managing sleep-disordered breathing conditions. This system integrates sophisticated machine learning algorithms, specifically one-dimensional convolutional neural networks (1D CNNs), with advanced signal processing techniques to analyze physiological signals. It focuses on the detecting respiratory event with a localized portion of the input segment, a novel approach that increases specificity in event detection. The system segments physiological signals into overlapping segments, each analyzed by the machine learning model to generate a prediction score. A unique aspect of this invention is the application of a dual-threshold mechanism: a ‘Model threshold’ for initial event identification and a ‘Vote threshold’ for confirming events through an aggregate voting process of overlapping segment predictions. This innovative approach ensures high accuracy and reliability in event detection and counting.

The invention provides a cardiovascular and cerebrovascular disease risk prediction method and system, and relates to the technical field of data processing.The method comprises the steps that real-time physiological parameters of a target user are obtained, and the real-time physiological parameters comprise the ambulatory blood pressure fluctuation rate, the serum lipoprotein level, the heart rate variability frequency domain index and the sleep apnea hypopnea index; the method comprises the following steps: preprocessing real-time physiological parameters, extracting instantaneous waveform features of blood pressure signals by adopting wavelet transform, and extracting features related to health conditions, including blood pressure level, blood fat level, blood sugar level, electrocardiogram abnormal indexes and cardiac ultrasound abnormal indexes, to form a feature vector set; according to the method, the real-time physiological parameters are acquired, the feature vector set is formed through preprocessing, the krill swarm algorithm is used for optimization, the three-level fusion model is constructed, finally, the cardiovascular and cerebrovascular disease risk degree of the target user is accurately evaluated, early warning information is output, and the accuracy and timeliness of cardiovascular and cerebrovascular disease risk prediction are effectively improved.

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.

The invention discloses a sleep apnea monitoring system based on non-contact intervention, and belongs to the technical field of medical health monitoring. The method solves the problems that in the prior art, a PSG method is tedious in operation and high in cost, household contact equipment brings discomfort to a user, and natural sleep is affected, physiological signals are collected through a non-contact biological radar sensor, an initial threshold value, an auxiliary threshold value and an environment factor are synthesized to calculate an optimal threshold value, and the optimal threshold value is obtained. The detection accuracy of the apnea and hypopnea events is improved; in combination with the apnea hypopnea index, the event duration factor, the blood oxygen decreasing factor and the event type factor, severity scores are calculated, and the patients are divided into mild patients, moderate patients and severe patients; according to the severity, the grade and the snore type of the real-time event, the optimal intervention measure is selected from the intervention strategy library, personalized intervention is achieved, interference to sleep of the patient is reduced, and the sleep quality and the life quality are improved.

The invention relates to a sleep monitoring and early warning method and system based on respiration data analysis and a medium, and belongs to the technical field of .The respiration data of a user is predicted through a sleep state and sleep apnea hypopnea index prediction model, the sleep state of the user and the sleep apnea hypopnea index are estimated, and the sleep apnea hypopnea index is obtained. And finally, early warning is performed according to the sleep state of the user and the sleep apnea hypopnea index, and meanwhile, a related treatment scheme is generated according to early warning information. According to the method, a deep learning model is pre-trained, sleep staging and sleep apnea hypopnea index estimation are carried out on the model in a unified framework at the same time, and internal correlation between a sleep macrostructure and a respiratory event is effectively decoupled. Then, through a domain adversarial training mechanism, sleep respiration characteristic knowledge learned in the contact type respiration signals is migrated to millimeter wave radar signals, and the problem that the generalization ability of a model is insufficient due to scarcity of radar data labels is solved;

The invention provides a sleep apnea and hypopnea intelligent monitoring and auxiliary diagnosis system and method based on a flexible sensor, and belongs to the technical field of sensors and respiration monitoring equipment. The system comprises a resistive flexible strain sensor, a wireless data acquisition card and a portable data processing terminal, a resistance-type flexible strain sensor is adhered to the skin surface of a main muscle group and an auxiliary muscle group related to human respiration, resistance signals related to the respiration frequency and amplitude are obtained, respiration data of a patient are collected in real time, and further, self-adaptive amplitude alarm upper and lower limits are constructed through statistical modeling of the individual resting respiration amplitude, so that the respiration alarm accuracy is improved. Therefore, the individual adaptability and robustness of the respiration detection system are improved.

An apparatus detects a sign of apnea or hypopnea and treats it. The apparatus includes a respiration detection unit configured to detect a signal related to a respiratory movement of a subject. The apparatus includes a sleep detection unit configured to detect a signal related to a sleeping status of the subject. The apparatus includes a control unit. The control unit is configured to receive from the respiration detection unit the detected signal related to the respiratory movement of the subject and determine an amplitude of the respiratory movement; receive from the sleep detection unit the signal related to the sleeping status of the subject and determine a sleep-wake state of the subject. The control unit or the apparatus determines, based on the amplitude of the respiratory movement and the sleep-wake state, determines whether the subject is in a state of hypopnea or apnea.

Medical equipment for detecting abnormal respiration of a patient during sleep includes an identification unit that identifies respiratory amplitudes of a plurality of times of respiration conducted by the patient in a respiratory period from elimination of apnea or hypopnea to a next occurrence of the apnea or the hypopnea, a calculation unit that calculates an approximation function that approximates the respiratory amplitudes of the plurality of times of the respiration, and a determination unit that determines that the abnormal respiration occurs in the patient, in a case in which a predetermined condition relating to the approximation function is satisfied.

The application discloses a sleep apnea monitoring system based on non-contact intervention and belongs to the technical field of medical health monitoring.The application solves the problems of the PSG method in the prior art, such as complicated operation, high cost, and the discomfort brought to users by household contact devices, and influences natural sleep, physiological signals are collected by a non-contact biological radar sensor, an optimal threshold is calculated by comprehensively considering an initial threshold, an auxiliary threshold and environmental factors, and the accuracy of apnea and hypopnea event detection is improved, in combination with an apnea hypopnea index, an event duration factor, a blood oxygen drop factor and an event type factor, a severity score is calculated, patients are divided into mild, moderate and severe, according to the severity, grade and snoring sound category of real-time events, an optimal intervention measure is selected from an intervention strategy library, individualized intervention is realized, the interference on patient sleep is reduced, and the sleep quality and life quality are improved.

The present application provides a kind of real-time apnea and hypopnea prediction method based on snoring, comprising: obtaining the snoring audio and sleep index data of target object, pre-processing and feature extraction are carried out on the snoring audio, the respiratory event time node marked in polysomnography data is used as a label to extract positive and negative samples, based on sleep index data, positive and negative sample extraction results, predict the probability of occurrence of apnea and hypopnea in subsequent preset time;For obtaining the snoring audio and physical examination data of target object and polysomnography data, based on the feature fusion mode, it is modeled in the unified feature space and combined with the principle of feature importance, the occurrence and nonoccurrence of apnea and hypopnea and the corresponding probability are accurately predicted in real time, monitoring is carried out before the respiratory event occurrence node, and corresponding warning signal is sent according to the detection result, and the target object is actively intervened.

The invention relates to a computer implemented screening method for Sleep Apnea Disorder, SA, based on a peripheral oxygen saturation, SpO2, signal comprising the steps of: receiving a SpO2 signal (100) of a user to perform preprocessing operations on the SpO2 signal (100); extracting representative SpO2 features; forming a Features Sequence (303); detecting respiratory events within the Features Sequence (303) using a modified Temporal Convolutional Network, TCN, (400) backbone; generating a Respiratory Events Sequence (402) of probabilities; aggregating the Respiratory Events Sequence (402) to generate an estimation of an Apnea-Hypopnea Index, eAHI, (105). The present invention also relates to a system for screening for Sleep Apnea Disorder, SA, based on a peripheral oxygen saturation, SpO2, signal comprising: a Signal Preprocessing Module (101) to perform preprocessing operations on a SpO2 signal (100); a Feature Extraction Module (102) configured to extract representative SpO2 features and form a Features Sequence (303); a Respiratory Event Detection Module (103) configured to detect respiratory events within the Features Sequence (303) by using a modified Temporal Convolutional Network, TCN, (400) backbone and to generate a Respiratory Events Sequence (402) of probabilities; and a AHI Estimation Module (104) configured to aggregate the Respiratory Events Sequence (402) to generate an estimation of an Apnea-Hypopnea Index, eAHI, (105).

This disclosure provides methods of treatment, prevention, and / or amelioration of obesity, obstructive sleep apnea / hypopnea syndrome (OSAHS), and related comorbidities using antibodies and antigen binding fragments thereof that binds to cannabinoid receptor 1 (CB1) receptor alone or in combination with GLP-1 agonists, e.g., semaglutide or incretins.

The invention relates to the technical field of medicines, in particular to application of a GLP-1 receptor agonist in preparation of a medicine for treating obstructive sleep apnea. The medicine is used for treating obstructive sleep apnea by activating Glp1r < + > neurons in vagus ganglions, the hypopnea index of apnea can be remarkably reduced, the longest apnea duration is shortened, and a new treatment approach is provided for treating obstructive sleep apnea.

This invention relates to the field of sleep monitoring data fusion processing technology, and discloses a sleep monitoring method based on the fusion of non-invasive brain oxygen saturation (AHI) and multiple physiological parameters. The method includes: S1, simultaneously acquiring multiple physiological parameter signals during the subject's sleep; S2, extracting and analyzing the AHI signal to generate multiple characterization markers; S3, identifying respiratory events related to the subject's apnea information from the respiratory signal and pulse oximetry signal, calculating the apnea-hypopnea index (AHI); and performing correlation analysis on the identified respiratory events and multiple characterization markers to calculate a correlation coefficient; S4, obtaining a comprehensive sleep monitoring assessment report based on the correlation coefficient and the AHI to determine the severity of the subject's current respiratory disturbance and the risk of brain tissue hypoxia. By improving the effectiveness of AHI in non-invasive brain oxygen saturation monitoring, the confirmatory nature of non-invasive brain oxygen saturation information in the respiratory correlation diagnosis during sleep monitoring is ensured.

Devices, systems, and methods are disclosed. The devices, systems, and methods detect, during a sleep session of a user, one or more parameters related to movement of the user, heart activity of the user, audio associated with the user, or a combination thereof; process the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or a sleep stage; and calculate an apnea-hypopnea index of the user during the sleep session based at least in part on the sleep state.

The invention relates to the technical field of medical equipment, in particular to a system and method for switching modes and regulating pressure based on respiratory events and electronic equipment. The system comprises a breathing machine monitoring module, a breathing event identification module, a breathing machine control module and a breathing machine alarm module. Wherein the breathing machine monitoring module collects multi-dimensional breathing parameters such as airway pressure and flow in real time; the respiratory event identification module accurately judges three types of events including obstructive sleep apnea, hypopnea and airflow limitation based on the parameters, and quantifies the airflow limitation degree; the breathing machine control module switches ventilation modes according to a preset progressive sequence, pressure parameters are dynamically adjusted, and pressure is reversely adjusted when breathing of the patient is recovered; the alarm module monitors the equipment state and the parameter safety range, and triggers sound-light alarm when the equipment state and the parameter safety range are abnormal. According to the invention, accurate identification of respiratory events, personalized mode adaptation and pressure regulation are realized, the curative effect and comfort are balanced, and treatment safety and long-term compliance are guaranteed.

The invention discloses a non-invasive obstructive sleep apnea awareness cause assessment method and a non-invasive obstructive sleep apnea awareness cause assessment system. The method comprises the following steps: acquiring an end position of each OSA event of an OSA patient; acquiring a lower jaw myoelectricity signal and an electroencephalogram signal of the OSA patient, and based on the ending position of each OSA event, calculating the proportion of the lower jaw myoelectricity change prior to the electroencephalogram change in the ending period of all the OSA events as an awakening pathogenesis index of the OSA patient; and obtaining anatomical clinical parameters, PSG parameters, CT parameters and awakening cause indexes of an OSA patient, and inputting the obtained parameters into a preset OSA operation curative effect prediction model to output a postoperative AHI prediction result of the patient. The invention provides an awakening disease cause index capable of quantifying muscle functions and an awakening threshold value at the same time, the awakening disease cause index is used for establishing a subtype suitable for an operation, an OSA operation curative effect prediction model is constructed in combination with anatomy related parameters, and the operation effect can be effectively predicted.

Monitoring of sleep disorders of a subject using a mobile device such as a smartphone or other wearable device, including placing the mobile device in juxtaposition with the subject, acquiring an audio signal indicating the subject's respiration by a microphone of the mobile device, acquiring an accelerometer signal including the subject's body position, angle, and motion information by an accelerometer of the mobile device, measuring chest movements related to respiration and respiratory sleep disorders by the accelerometer of the mobile device, identifying a plurality of silent events by the audio signal, classifying the silent events into apnea or hypopnea, classifying the classified apnea into a plurality of apnea types by chest movements recorded by an accelerometer sensor, and associating respective angle information with the classified silent events by combining the audio signal and the accelerometer signal.

The present application relates to sleep monitoring and early warning method, system and medium based on respiratory data analysis, belong to technical field, the present application is through sleep state and sleep apnea hypopnea index prediction model to the respiratory data of user is predicted, the sleep state and sleep apnea hypopnea index of user are estimated, finally, according to the sleep state and sleep apnea hypopnea index of user, early warning is carried out, simultaneously, according to early warning information, relevant treatment scheme is generated.The application is through pre-training a model named deep learning model, the model is in unified framework simultaneously carries out sleep staging and sleep apnea hypopnea index estimation, effectively decouples the internal correlation of sleep macrostructure and respiratory event.Then, through the domain adversarial training mechanism, the sleep respiratory feature knowledge learned from the contact respiratory signal is migrated to the millimeter wave radar signal, and the problem of insufficient model generalization ability caused by the scarcity of radar data labeling is solved.

The present invention provides a readable storage medium, an electronic apparatus, and an implantable medical device. The readable storage medium stores a computer program. The computer program, when executed by a processor, implements the following steps: acquiring a preset number of pulmonary respiratory impedance values of a target subject; calculating a sum of absolute pulmonary respiratory impedance values of the target subject on the basis of the absolute values of the preset number of pulmonary respiratory impedance values of the target subject within a preset duration; and determining whether the target subject experiences an apnea event or a hypopnea event on the basis of the sum of the absolute pulmonary respiratory impedance values of the target subject. The present invention enables accurate detection of sleep respiratory abnormality events including sleep apnea and sleep hypopnea.

The invention relates to an appliance for treating obstructive sleep apnea-hypopnea syndrome, and belongs to the technical field of medical auxiliary instruments. In order to improve the using comfort, the appliance for treating the obstructive sleep apnea-hypopnea syndrome comprises a supporting plate assembly and two telescopic assemblies, the supporting plate assembly is connected with the two ends of a lower tooth socket through connecting rods, and an arc-shaped supporting plate is configured to move in the direction close to or away from the lower tooth socket; two ends of the upper tooth socket are hinged with the connecting rod, and a first elastic piece is arranged at the hinged part; the telescopic assembly is arranged between the lower tooth socket and the connecting rod and used for driving the lower tooth socket to move in the direction close to or away from the supporting plate assembly. When the oral cavity is closed, supporting force can be provided for soft palate arch muscles through the arc-shaped supporting plate to prevent the soft palate from falling to block the airway, and when the oral cavity is opened, the lower tooth socket moves forwards relative to the upper tooth socket to drive the lower jaw of a patient to further move forwards to expand the airway, so that the obstructive sleep apnea hypopnea syndrome is treated.

An apparatus acquires vital information related to a cardiovascular disorder. The apparatus includes a radar system configured to transmit a radio wave to a subject and receive the radio wave reflected by the subject. The apparatus includes a controller. The controller configured to: (a) calculate a respiratory amplitude, based on the radio wave reflected by the subject, and (b) determine a sleep status of the subject, based on the radio wave reflected by the subject. The controller is configured to: (c) judge an occurrence of sleep apnea or hypopnea, based on the respiratory amplitude and the sleep state, (d) obtain a frequency of the occurrences of sleep apnea or hypopnea, and (e) obtain a cardiovascular disorder related index (CVDI), based on a relationship between the frequency of the occurrence of sleep apnea or hypopnea and the cardiovascular disorder.

The present disclosure relates to a nasal cavity memory stent and a deformable nasal cavity support system, the stent body is formed by a shape memory wire having a first shape and a second shape at different temperatures, in the second shape, the shape memory wire is in a spiral structure to be used for nasal cavity packing and support to improve nasal ventilation by a spring-like structure. When needed, the stent body is only needed to be inserted into the nasal cavity, and the inserted stent body can be gradually deformed to fit the inner wall of the nasal cavity to support the nasal cavity by the temperature of the nasal cavity. When disassembled, the stent outer end is only needed to be cooled, and the initial shape can be gradually recovered from the outer end to be removed from the nasal cavity. The deformable nasal cavity support system can be used for postoperative nasal cavity packing of nasal septum correction surgery, nasal endoscopy and the like, and can also be used for improving hypoxia caused by adenoid hypertrophy blocking the posterior naris and improving the obstruction of the oropharynx of patients with obstructive sleep apnea hypopnea, smooth breathing and sleep aid.

The present invention relates to the field of respiratory warming manikins, and more specifically, to a respiratory warming manikin for teaching obstructive sleep apnea-hypopnea syndrome. The respiratory warming manikin includes a manikin body and an exhalation component, an inhalation component, and a breathing tube disposed within the manikin body. The manikin body is provided with a heating wire. The outlet end of the exhalation component and the inhalation component are respectively connected to one end of the breathing tube. The other end of the breathing tube is provided with an oral breathing tube and a nasal breathing tube. The breathing tube is provided with a breathing regulating valve for simulating upper airway collapse and obstruction. The oral breathing tube is provided with an oral breathing valve, and the free end thereof extends into the mouth of the manikin body. The nasal breathing tube is provided with a nasal breathing valve, and the free end thereof extends into the nostrils of the manikin body. The present invention can effectively simulate the symptoms of obstructive sleep apnea-hypopnea syndrome, and is beneficial for visualizing and vividly teaching medical teaching of obstructive sleep apnea-hypopnea syndrome.

The invention discloses a traditional Chinese medicine composition formula which comprises the following raw material medicines: astragalus membranaceus, ligusticum wallichii, peach kernels, gastrodia elata, rhizoma acori graminei and fructus viticis. In addition, the invention further discloses a traditional Chinese medicine preparation which is prepared according to the traditional Chinese medicine composition formula, and the traditional Chinese medicine preparation is at least one of water decoction, powder, tablets, granules or capsules. Correspondingly, the invention also discloses a preparation method of the traditional Chinese medicine preparation, which comprises the following steps: boiling and extracting the raw materials in a closed container for at least two times to obtain a decoction; and preparing at least one of water decoction, powder, tablets, granules or capsules from the decoction. The traditional Chinese medicine preparation prepared by adopting the traditional Chinese medicine composition formula designed by the invention can be used for effectively treating qi deficiency, blood stasis and phlegm stagnation type hypertension combined with obstructive sleep apnea hypopnea syndrome.