34 results about "Hypopnea" patented technology

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 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;

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 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.

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 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 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.

The invention discloses a sleep data acquisition and analysis method of an intelligent sleep monitoring pillow, which comprises a monitoring pillow main body, a sensor module, a data processing module and a data analysis module, and is characterized in that the sensor module is used for acquiring head movement data, respiratory rate data and snore decibel data of a user; the data processing module is electrically connected with the sensor module and is used for carrying out preprocessing, time-frequency domain conversion and sleep stage classification on collected data, and the data analysis module is used for processing time sequence signals, predicting sleep stages and utilizing the sleep latency period, the awakening frequency, the sleep efficiency and the sleep stage proportion as sleep continuity evaluation indexes; head movement data, respiratory rate data and snore decibel data are integrated through cooperative work of the three-axis acceleration sensor, the piezoelectric film sensor and the microphone sound receiver, a multi-dimensional sleep quality analysis model is constructed, and respiratory disorder is accurately warned early by monitoring apnea and hypopnea events and pathological snore in real time.

The application provides a cardiovascular and cerebrovascular disease risk prediction method and system, relates to the technical field of data processing, and the method comprises the following steps: acquiring real-time physiological parameters of a target user, including dynamic blood pressure fluctuation rate, serum lipoprotein level, heart rate variability frequency domain index and sleep apnea hypopnea index; preprocessing the real-time physiological parameters, extracting the instantaneous waveform features of the blood pressure signal by using wavelet transform, and extracting the features related to the health condition, including blood pressure level, blood lipid level, blood glucose level, electrocardiogram abnormality index and cardiac ultrasound abnormality index, to form a feature vector set. Through the acquisition of the real-time physiological parameters, the preprocessing to form the feature vector set, the optimization by the phosphorus shrimp swarm algorithm, the construction of a three-level fusion model, the final accurate evaluation of the cardiovascular and cerebrovascular disease risk degree of the target user and the output of the early warning information, the accuracy and timeliness of the cardiovascular and cerebrovascular disease risk prediction are effectively improved.

This invention discloses a device for precise detection and treatment of sleep apnea under continuous airflow support. This device can simultaneously perform precise identification and treatment of sleep apnea. It integrates precise detection of apnea and hypopnea events during sleep, accurately distinguishing between central and obstructive apnea events, and effectively treating obstructive sleep apnea and hypopnea. It generates a constant positive pressure airflow through a positive pressure airflow generating component consisting of a blower, pressure control assembly, and pressure airflow sensor. This positive pressure airflow is continuously input into an open-face mask equipped with a respiratory flow sensor, enabling the identification of sleep apnea-hypopnea events and the determination of the nature of the apnea. Continuously inputting positive pressure airflow into a nearly sealed nasal mask through the positive pressure airflow generating component can treat or adjust the pressure required for treating sleep apnea.

Provided is a novel mechanism for estimating apnea hypopnea index (AHI). This AHI estimation system comprises: an AH information acquisition unit that acquires temporal pulse wave information from a subject, and acquires, on the basis of the pulse wave information, AH information indicating the time ratio in which the subject experiences apnea (A) and hypopnea (H) during sleep; and an AHI estimation unit that estimates an apnea hypopnea index (AHI) on the basis of the AH information. The AH information acquisition unit: acquires acceleration sensor information; evaluates, on the basis of the acceleration sensor information, whether the subject is in a sleeping state or a wakeful state; and if it is evaluated that the subject is in a sleeping state, acquires the AH information on the basis of the pulse wave information for when it is evaluated that the subject is in the sleeping state.

The present application belongs to the field of biotechnology and medicine, and particularly relates to diagnostic value of Claudin3 and family related molecules for obstructive sleep apnea hypopnea syndrome. Specifically, the present application provides a technical solution for detecting Claudin3 expression quantity to diagnose obstructive sleep apnea hypopnea syndrome; obstructive sleep apnea hypopnea syndrome can be accurately diagnosed through the Claudin3 expression quantity in urine. Moreover, the expression quantity of Claudin3 in urine is negatively correlated with sleep apnea hypopnea index (AHI) and time of blood oxygen saturation lower than 90% (T90).

The present application relates to the technical field of muscle chip, in particular to a chronic intermittent hypoxia muscle injury model and its preparation and application. The present application first sets a channel layer on the surface of a polytetrafluoroethylene plate to obtain a male mold; then polydimethylsiloxane is poured on the male mold, and a female mold is obtained after post-processing; a silica gel frame matched with the female mold is prepared by using silica gel material; then the female mold and the silica gel frame are superimposed, and a muscle chip is obtained after post-processing; finally, myoblasts, cell culture solution and a substitute for extracellular matrix are mixed to obtain a suspension, and then the suspension is placed in the pretreated muscle chip to induce differentiation, and a chronic intermittent hypoxia muscle injury model is obtained through a precise oxygen control system. The present application provides a new research platform by using microfabrication technology, simulates the core tissue structure of muscle, realizes the key physiological functions of muscle, and can be more effectively applied in the research of obstructive sleep apnea hypopnea syndrome.

The invention discloses a sleep hypopnea type automatic discrimination method and system based on a multi-modal signal, and aims to solve the technical bottlenecks of strong subjectivity, poor consistency and limited single signal feature discrimination capability of the existing visual scoring method. The method comprises the following steps: collecting multi-modal signals in a polysleep pattern (PSG), wherein the multi-modal signals comprise nasal ventilation airflow, thoracico-abdominal breathing movement, blood oxygen saturation and other signals; extracting multi-dimensional features related to hypopnea typing, wherein the multi-dimensional features comprise an inspiration flow limited index, thoracico-abdominal movement coordination, an event termination mode, an awakening sequential relationship and a sleep stage associated feature; the features are fused and optimized based on a machine learning algorithm, a high-precision classification model is constructed, and automatic discrimination of obstructive and central hypoventilation is realized. According to the method, the key problems that a traditional method depends on manpower in hypopnea typing, feature utilization is not comprehensive, and judgment precision is insufficient are solved, and a reliable basis is provided for accurate diagnosis, typing and treatment strategy formulation of sleep breathing disorder.

A device detects and treats signs of apnea or hypopnea. The device includes a respiratory detection unit configured to detect signals related to respiratory movement in a subject. The device also includes a sleep detection unit configured to detect signals related to the subject's sleep state. The device includes a control unit. The control unit is configured to receive the detected signals related to the subject's respiratory movement from the respiratory detection unit and determine the amplitude of the respiratory movement; and to receive signals related to the subject's sleep state from the sleep detection unit and determine the subject's sleep-wake state. The control unit or device determines whether the subject is in a state of hypopnea or apnea based on the amplitude of the respiratory movement and the sleep-wake state.

Devices and systems provide methods of detecting a heart failure condition of a patient that may be based on one or more respiratory parameters of a patient. In an example embodiment, a monitoring device determines one or more heart failure condition indicators based on a measure of the patient respiratory airflow and / or a measure of treatment pressure. Respiratory parameters such as respiration rate, hypopneas, apneas, Cheyne-Stokes breathing patterns or apnea-hypopnea counts may be compared to thresholds that are selected to represent a change in the condition of a heart failure patient such as an onset of a decompensation event. Results of the comparisons may trigger a pressure treatment change and / or one or more warnings or messages to notify a patient or physician of a pending change to the patient's heart failure condition so that the patient may more immediately seek medical attention to treat the heart failure condition.

A wearable device can include one or more sensors including one or more motion sensors. The data from the one or more motion sensors can include an indication of respiration, and the data can be processed to enable screening for physiological events, such as breathing disturbances associated with apnea / hypopnea events. In some examples, one or more features are extracted from the data. In some examples, to improve performance, one or more masks can be applied to the data from the one or more sensors. For example, the one or more masks can be used to perform processing on data from the one or more sensors when a user is determined to be sleeping and when the data from the one or more sensors is determined to be indicative of a quality respiration signal.

This utility model relates to the field of medical and health care technology, and discloses a nursing pillow for patients with sleep apnea-hypopnea syndrome who are wearing a ventilator. The pillow includes a pillow body, with connectors fixedly connected to both sides of the pillow body. Fixing members are slidably connected to the exterior of each connector, and protective shells are fixedly connected to the top of each fixing member. Moving plates are slidably connected to the opposite sides of each protective shell. A limiting plate is fixedly connected to the right side of the moving plate, and a limiting rod is slidably connected inside the limiting plate. A spring is fixedly connected to the top of the limiting rod. In this utility model, medical staff pull the tension band, causing the limiting rod to engage with the protective shell, thus fixing the breathing tube. The patient rotates the rotating plate, causing the limiting rod to move, allowing the tension band to disengage from the breathing tube under the action of a retractable roll, achieving the placement and fixation of the breathing tube.

The invention is suitable for the field of sleep monitoring, and provides a non-contact sleep respiration monitoring method, device and system. The method comprises the steps that a chest vibration signal and an abdomen vibration signal are obtained through a first optical fiber sensor arranged below the chest of a flat-lying and supine object and a second optical fiber sensor arranged below the abdomen of the flat-lying and supine object respectively, and pulse wave signals and blood oxygen saturation values are collected from fingers of the flat-lying and supine object synchronously through a PPG sensor; blind source separation is carried out on the chest vibration signal and the abdomen vibration signal, then a respiration source signal, a heart beat source signal and a body movement noise source signal are extracted, and template enhancement is carried out on the heart beat source signal to obtain a heartbeat event time sequence; constructing a multi-feature fuzzy inference system, and judging an event type; and calculating the sleep apnea hypopnea index according to the total number of effective breathing events and the total sleep time. According to the method, the sleep apnea and hypopnea events are judged in an undisturbed home scene, and the sleep apnea hypopnea index is automatically calculated.

A wearable device can include one or more sensors including one or more motion sensors. The data from the one or more motion sensors can include an indication of respiration, and the data can be processed to enable screening for physiological events, such as breathing disturbances associated with apnea / hypopnea events. In some examples, one or more features are extracted from the data. In some examples, to improve performance, one or more masks can be applied to the data from the one or more sensors. For example, the one or more masks can be used to perform processing on data from the one or more sensors when a user is determined to be sleeping and when the data from the one or more sensors is determined to be indicative of a quality respiration signal.