65 results about "Apnea" patented technology

The invention discloses an electrocardiosignal-based sleep apnea detection method and system and a storage medium. The method comprises the following steps: acquiring a target electrocardiosignal to be analyzed, performing R peak identification on the target electrocardiosignal, calculating an RR interval between two adjacent R peaks, and acquiring a voltage amplitude of the electrocardiosignal at the R peak position as an R peak amplitude; on the basis of the RR interval, the RR interval is converted into an instantaneous heart rate sequence, resampling operation is carried out on the heart rate sequence, interpolation processing is carried out on non-uniform sampling points obtained after resampling so as to obtain a continuous and equally-spaced heart rate curve, and signals of a breathing-related frequency band are extracted through filtering to serve as electrocardio derived breathing signals; and splicing the RR interval, the R peak amplitude and the electrocardio-derived respiration signal to form a multi-channel feature, and inputting the multi-channel feature into a trained sleep respiration detection model to obtain sleep apnea state judgment information. Therefore, the apnea state can be detected more comprehensively, and the accuracy of sleep apnea detection is improved.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention discloses a system and a method for monitoring and analyzing a breathing mode of an individual. The system integrates a first sensing device for a nasal cavity and a second sensing device for an oral cavity through monitoring equipment worn on the head and the face. The core of the method is that synchronous quantitative calculation of mouth and nose breathing flow is achieved for the first time through the schemes of hardware decoupling (parallel ultrasonic measurement of oral geometry and flow velocity) and algorithm decoupling (calculation of nasal cavity flow and area from coupling signals in combination with user calibration). According to the invention, the core biomarker ONFR is obtained, and the problem of ambiguity of oral respiration and apnea in the prior art is solved. The invention further discloses a multi-level precision framework, artifact recognition based on double IMUs and a'digital phenotype 'risk early warning technology based on cloud AI. The invention has important application value in the fields of sleep health, sports physiology and the like.

The invention relates to the technical field of sleep respiratory disease analysis, and discloses a method for analyzing obstructive sleep apnea. The method comprises the following steps: acquiring an original physiological signal flow which is output by a multi-channel sleep monitoring device and comprises a respiratory waveform, blood oxygen fluctuation, an electrocardio rhythm and a sound vibration signal; and then, carrying out adaptive window function segmentation and multi-resolution conversion on the original signal flow to generate a standardized multi-modal signal sequence. State decoding is carried out on the sequence through a hidden Markov model, and steady state physiological mode features and transient abnormal mode features are extracted. And fusing the steady state features and clinical archive data of the patient, calculating an apnea risk index, and forming an initial evaluation report. Meanwhile, a dynamic evolution path of transient abnormal mode characteristics is monitored, and a real-time pathology indicator in the signal is detected. And finally, a risk weight coefficient in the initial evaluation report is adjusted according to the real-time pathology indicator, and a more accurate optimization evaluation report is generated.

The invention discloses a cardiovascular risk early warning method based on multi-modal time sequence data, relates to the technical field of medical health information monitoring, and aims to solve the problem of confusion of causes of dyspnea at night by constructing a cross-modal direction and time delay relation in a sliding time window. Phenotype similar phenomena such as pure blood oxygen reduction / wake-up are decomposed into a comparable time sequence interaction structure, so that a blocking chain type process and a non-blocking type heart failure related process can be distinguished on the structural level, and therefore false alarm and missing alarm caused by confusion are reduced. Meanwhile, a multi-channel signal is firstly converted into a time sequence causal diagram, and then in-window statistical characteristics are combined for judgment, so that the model not only utilizes the self change of each channel, but also utilizes the interaction evidence of the first and second channels, the influence direction and the delay length, and the expression ability of the pathophysiological chain difference is improved.

The invention provides a mattress type sleep monitoring data real-time processing method based on edge calculation, relates to the technical field of medical health data processing, and aims to perform macroscopic state layer calculation, pathological event layer calculation and stability layer calculation at the same time in a layering manner based on a constructed dynamic sensing matrix, generate a structured sleep health report, and realize real-time sleep monitoring through one-time sleep monitoring. The comprehensive information about the sleep macrostructure, the respiratory pathology event burden and the sleep stability can be synchronously output; through a sleep health assessment report, total sleep duration and sleep efficiency calculated by the macroscopic state layer, apnea and type and severity calculated by the pathological event layer, and a sleep micro-awakening state and sleep fragmentation degree calculated by the stability layer can be clearly known, and a multi-dimensional and integrated comprehensive assessment result with high medical value is realized. The information amount of the sleep health portrait is enriched, and a comprehensive and reliable data support is provided for accurately positioning the root of the sleep problem and formulating a personalized intervention or treatment scheme.

The invention relates to the technical field of remote monitoring, in particular to a chronic respiratory disease patient remote monitoring system based on the Internet of Things, which comprises a physiological baseline initialization module, an event response delay measurement and calculation module, a cardiopulmonary coupling phase quantization module and a decompensation risk early warning module. According to the method, an individual blood oxygen and cardiopulmonary coupling health baseline is established to provide accurate reference for subsequent analysis, then the blood oxygen recovery time after apnea is measured and calculated, the physiological response delay characteristic of an individual is quantified, and the characteristic is used for setting a dynamic analysis window to continuously calculate the phase difference between a breathing mode and a blood oxygen signal, so that the phase difference between the breathing mode and the blood oxygen signal is calculated. By continuously comparing the current phase difference with the individual health baseline and triggering early warning when the difference value continuously exceeds a specific range, the early sign of the instability of the cardiopulmonary system can be effectively captured, the conversion from passive response type alarm to active predictive early warning is realized, and a precious time window is provided for preventing decompensation.

The invention particularly relates to an Alzheimer's disease risk early warning system based on sleep disorder analysis, and relates to the technical field of sleep medicine and neurodegenerative disease early warning. An apnea event triggering and window locking module; a neurovascular coupling dynamics modeling module; and a parameter identification and feature extraction module. According to the method, the FOPDT model is constructed according to the cerebrovascular physiological characteristics, the nerve and blood vessel coupling function is quantified, the parameters directly reflect the cerebrovascular stiffness, the VSI is calculated through hypoxia depth weighting, the 3 * 3 double-index matrix is constructed in combination with the clinical gold standard AHI, and the accuracy is improved compared with single-index early warning; the five-color layered early warning logic is matched with targeted intervention suggestions, so that stepped guidance from low-risk follow-up visit to high-risk treatment can be realized.

To provide oral appliances that can be used to treat sleep apnea syndrome (SAS) ranging from mild to severe. [Solution] One embodiment of an oral appliance for sleep apnea is an oral appliance that is fitted into the user's oral cavity. The oral appliance comprises an upper jaw portion, a lower jaw portion, and a drive unit. The upper jaw portion has a shape that mimics the user's upper teeth and contacts the upper teeth. The lower jaw portion has a shape that mimics the user's lower teeth and contacts the lower teeth. The drive unit connects the upper jaw portion and the lower jaw portion so as to be able to change the relative position of the lower jaw portion with respect to the upper jaw portion, and drives the lower jaw portion. The drive unit has a control circuit that determines whether the user is in a state of apnea based on sensor information obtained from a sensor that detects the user's apnea state, and if the user is in a state of apnea, drives the lower jaw portion toward the user's forward position, and if the user is not in a state of apnea, drives the lower jaw portion to return to the user's resting position.

The present application belongs to the technical field of sleep respiratory disease detection, and is a sleep apnea identification method based on a hybrid neural network. The method uses a millimeter wave radar to perceive human sleep in a surrounding bed environment, adopts spatial two-dimensional analysis to locate the chest cavity position, and solves the interference problem in radar sign extraction of different targets at the same distance. Shannon entropy of the respiratory signal is constructed as an iteration index of the whale optimization algorithm, and the minimum index is used to realize iteration optimization of the variational mode extraction parameter, so that different breathing states are matched with different parameters to accurately extract the main frequency narrowband respiratory signal. The respiratory time-frequency energy graph obtained through data processing is used as input, and a CNN network and a bidirectional long short-term memory module are used to jointly construct a feature extraction module, so that the application can identify the frequency change trend over time, capture more comprehensive features, and accurately identify target sleep apnea. In addition, the hybrid network can accurately classify unmanned scenes, apnea, low ventilation and normal breathing.

This application relates to the field of sleep monitoring technology, providing a method, system, device, medium, and product for sleep apnea identification. The method includes: acquiring multimodal sensing data; acquiring sleep state identification information based on the multimodal sensing data; determining that the user is asleep based on the sleep state identification information; and acquiring sleep breathing analysis information based on the sleep state identification information and the multimodal sensing data. Multimodal sensing data can comprehensively and multidimensionally capture physiological data related to sleep apnea, improving the accuracy of sleep apnea identification. Before performing apnea analysis, it first identifies whether the user is asleep; apnea identification processing is only performed when the user is asleep. Apnea identification processing is unnecessary when the user is awake, avoiding continuous complex analysis and reducing power consumption. The two-layer processing of first identifying the sleep state and then identifying apnea improves interpretability.

“Artificial Intelligence” or “AI” technology can be applied to Wearable Cardioverter Defibrillators (“WCDs”) and other wearable medical equipment in various ways, including garment fitting and adjustment, analyzing electrocardiogram (“ECG”), other sensor data and / or other patient data in real time to detect / assess the patient's present condition and / or need for treatment for cardiac and other conditions (e.g., stroke, coughing, apnea, etc.), detect imminent failure of the wearable medical device components, capture and report data collected from the patient for presenting to clinicians, adjust thresholds for alarms and notifications based on patient's responses, improve patient compliance based on the patient's past non-compliant behavior and actions that resulted in the patient becoming compliant, provide tests to the patient, and learn the patient's responses to detect / assess the patient's present condition and / or need for treatment.

An apparatus for treating respiratory disorders in a patient is disclosed. The apparatus includes a pressure generator configured to deliver a flow of air at positive pressure through a patient interface to an airway of the patient, a sensor configured to generate a signal representative of a respiratory flow of the patient, and a controller. And a controller configured to control the pressure generator to deliver a ventilation therapy with a base pressure and pressure support through the patient interface, an apnea being detected from the signal representative of a respiratory flow of the patient, controlling the pressure generator to deliver one or more detected breaths to the patient during the apnea, determining patency of the airway of the patient from a waveform of the respiratory flow signal responsive to one of the one or more detected breaths, an effective duration of the apnea is calculated based on the patency of the airway, and a setpoint of the base pressure for the ventilation therapy is adjusted in response to the apnea based on the effective duration of the apnea.

The application discloses a CBCT-based child obstructive sleep apnea syndrome identification system and relates to the technical field of medical networks, and comprises the following steps: acquiring a CBCT image and physiological characteristic parameters of a child, performing airway segmentation and reconstructing an airway surface model; establishing an airway respiratory flow field based on the model, setting boundary conditions for simulation, extracting dynamic characteristics to construct first discriminant characteristics; inputting the first discriminant characteristics into a classification model to determine whether the first discriminant characteristics are in a fuzzy discriminant interval; if the first discriminant characteristics are not in the interval, directly outputting an obstructive sleep apnea syndrome type; if the first discriminant characteristics are in the interval, extracting airway cross-section structure information, fusing the first discriminant characteristics to obtain second discriminant characteristics; and matching the characteristics with preset rules to determine whether to adopt enhanced classification or structure registration for secondary discrimination, finally outputting a child apnea syndrome type, and improving the reliability and accuracy of the final diagnosis result.

A device may include: a bed. A sensor may be configured to configured to: sense pressure applied to the bed; and transmit a pressure stream. A plurality of temperature sensors arranged across the bed can be configured to: sense temperature in a particular location of the bed; and transmit a datastream of the sensed temperature. A computing system comprising can be configured to: receive the pressure stream; receive each of the datastreams; identify an apnea event; generate a sequence of heart rate values for the user for cardiac activity; generate a sequence of skin temperature values for the user; determine a change in heart rate; determine a change in skin temperature within the second time window; determine a relationship between the change in heart rate and the change in skin temperature; and determine a severity score for the apnea event based on the relationship.

The invention relates to the technical field of medical monitoring, in particular to a newborn nursing table with image acquisition and transmission functions. A mechanical and optical characteristic sequential sequence of each area in a video is synchronously analyzed through a respiration monitoring system, the trajectory closure degree is calculated in a phase space so as to accurately recognize a neonatal respiration activity area, and differential video compression coding is performed on a respiration area and a background area according to the trajectory closure degree, so that on the premise that the transmission bandwidth is not increased, the transmission efficiency of the neonatal respiration activity area is improved. Weak breathing details are clearly restored at the remote monitoring end, and false apnea misjudgment is effectively avoided.

This invention belongs to the field of infant and toddler care technology and discloses an active intervention system and method for infant and toddler sleep apnea. Through a dedicated algorithm and adaptive acquisition components, it accurately collects sleep audio, physiological parameters, and environmental information, and performs semantic annotation and feature association on various types of data to establish dedicated semantic correspondence rules. Simultaneously, it ensures the temporal consistency of different types of data, transforming unstructured raw data into structured semantically related data. Based on the apnea risk level and specific triggers obtained from semantic parsing, it designs differentiated, graded intervention strategies to carry out targeted active intervention operations for different triggers, replacing the traditional single alarm mode. Simultaneously, it collects real-time feedback data on the effects of intervention and dynamically adjusts intervention parameters and strategies based on feedback, making apnea intervention more targeted, improving the timeliness and effectiveness of intervention, and reducing the potential risk of infant and toddler sleep apnea.

The present disclosure relates to a nasal pharmaceutical composition for the treatment of obstructive sleep apnoea, a drug device combination for intranasal delivery of the pharmaceutical composition, and a method of treatment.

A heat exchanger is disclosed for causing cryolysis of adipose tissue of a human tongue. The heat exchanger includes a body having cooling channels for circulating fluids therein. The body forms a contact surface that contacts a portion of the dorsal surface of the tongue and a portion of the base of the tongue. The heat exchanger includes a pair of side walls extending from the body and forming a pair of side contact surfaces that are dimensioned so that they contact the dorsal and lateral surfaces of the tongue in a manner so as to constrict the tongue when the contact surface is in contact with the tongue. A method of treatment for apnea using the heat exchanger and / or administering a chemical adipolysis formulation / vasoconstriction agent is also disclosed.

The present invention relates to an integrated sleep diagnosis and treatment device, and more particularly to an integrated apnea diagnosis and treatment device. The present invention additionally relates to methods of sleep diagnosis and treatment. The sleep disorder treatment system of the present invention can use a diagnosis device to perform various forms of analysis to determine or diagnose a subject's sleeping disorder or symptoms of a subject's sleep disorder, and using this analysis or diagnosis can with or in some embodiments without human intervention treat the subject either physically or chemically to improve the sleeping disorder or the symptoms of the sleeping disorder. The diagnostic part of the system can use many different types of sensors and methods for diagnosing the severity of the symptoms of or the sleep disorder itself. The treatment part of the system can use a device to physically or chemically treat the subject's symptoms or sleep disorder itself.

A respiratory gas delivery and sampling system, a gas sampling system, a gas sampling interface, and a gas sampling tip are provided that can be used to sample exhaled and / or exhaled gases from patients, particularly patients with apnea and / or patients receiving high-flow respiratory therapy. [Solution] The gas sampling system comprises a respiratory gas monitor in fluid communication with a gas sampling interface, the gas sampling interface comprising the gas sampling tip of the present invention. The gas sampling interface comprises a gas sampling conduit, the gas sampling tip located at the free end of the conduit. The gas sampling interface can be configured to allow selective positioning of the gas sampling tip in or within the nostrils of the mouth or nose of a patient.

Techniques for improved respiratory therapy are provided. Glucose data of a user collected during a sleep session is accessed. Based at least in part on the glucose data, a plurality of transitions between sleep stages experienced by the user during the sleep session is determined. A sleep quality measure for the sleep session is generated based at least in part on the plurality of transitions. An apnea risk score for the user is generated based at least in part on the sleep quality measure, and one or more medical interventions for the user are provided based on the apnea risk score.

This invention provides a multi-dimensional monitoring and assessment method for sleep disorders at high altitudes, comprising: S1, concurrently collecting respiratory signals, blood oxygen saturation and pulse wave (PPG), heart rate / ECG, body movement and position, and environmental parameters such as altitude / air pressure and temperature and humidity during nighttime sleep; S3, performing sleep / wake discrimination and detecting apnea, hypoventilation, and blood oxygen desaturation events based on structured data, and statistically analyzing event metrics; S4, calculating the frequency domain characteristics of heart rate variability (HRV) from RR interval sequences to obtain a sympathetic nerve activity proxy index; S5, weightedly fusing the event metrics, oxygenation metrics, and sympathetic proxy into a comprehensive score R and performing hierarchical interpretation; S6, fitting an individualized risk trajectory model to the cross-night scoring sequence and outputting short-term predictions. This method enables the construction of individualized adaptation trajectories and short-term prediction models in a real-world high-altitude environment, shifting from "static results" to "process evaluation."

The utility model provides a kind of intelligent nose mask suitable for apnea patient uses, it is related to respiratory protection technical field, including nose mask main part, the outer wall of nose mask main part is provided with connecting port, the lower portion of connecting port is provided with protruding block, the bottom end of protruding block is clamped with water storage box, the face of nose mask main part contact skin is provided with silica gel pad, three independent inflatable chambers are embedded in silica gel pad;Three independent inflatable chambers (nose bridge area, left and right alar region) are embedded in the silica gel pad of nose mask, each chamber is linked with air pressure sensor by micro air valve, can detect air leakage in real time and dynamically adjust local air pressure, compared with traditional overall pressurization rigid frame, the design accurately compensates sealing pressure, avoids excessive compression face, reduces the risk of pressure sore, while improving long-term wearing comfort.

This invention belongs to the field of intelligent sleep state monitoring technology, and particularly relates to an intelligent sleep state monitoring and analysis device, pillow, and method. The device includes two sound sensors and a three-axis accelerometer, as well as a main control module with built-in signal processing. The two sound sensors acquire dual-channel snoring signals, and suppress environmental noise through time delay estimation and beamforming. The three-axis accelerometer is attached to the back of the neck to acquire three-dimensional vibrations caused by breathing and heartbeat, and separates the breathing waveform, heartbeat waveform, and motion artifact components through bandpass filtering and principal component analysis / independent component analysis. The intelligent evaluation algorithm integrates snoring characteristics, respiratory rate, heart rate, and heart rate variability to output sleep stages and apnea events. This invention also provides a pillow incorporating this device and an intelligent sleep state monitoring and analysis method. This invention has a simple structure, can be integrated into a pillow, does not interfere with natural sleep, and is suitable for long-term use in homes and rehabilitation centers.

The application provides a sleep apnea detection method and a detection device, and relates to the technical field of respiration detection. The method comprises the following steps: extracting physiological signals under each channel from a polysomnogram in a detection period, dividing the detection period according to a preset time to obtain a plurality of time segments; determining a signal identifier corresponding to each physiological signal, and dividing each physiological signal according to the signal identifier to determine a first signal type and a second signal type; for each time segment, inputting the physiological signals corresponding to the first signal type of the time segment into a first sleep apnea detection model to obtain a first set, and inputting the physiological signals corresponding to the second signal type of the time segment into a second sleep apnea detection model to obtain a second set; and identifying the proportion of patients with apnea based on the first set and the second set. Through the application, the plurality of physiological signals are processed, and the accuracy of the sleep apnea result is improved.

The invention discloses a swallowing breathing coordination evaluation method based on a millimeter wave radar, and belongs to the technical field of physiological signal non-contact detection and intelligent health evaluation. The method comprises the following steps: arranging a millimeter wave radar in front of a detected object, detecting a neck front-upper chest area of the detected object, establishing a throat swallowing area and a chest breathing area based on distance window separation and angular domain energy separation, respectively extracting throat transient displacement characteristics and chest breathing displacement characteristics, and constructing quality evaluation parameters; and synchronously identifying a swallowing starting point, a swallowing peak value, a swallowing ending point, an inspiration phase, an expiration phase and an apnea section, carrying out time sequence alignment, and constructing a swallowing-breathing event chain. And performing aggregation evaluation based on a multi-time swallowing-breathing event chain, and outputting a mistaken inhalation risk grade, a swallowing coordination score and a rehabilitation training feedback index in combination with quality gating. The method can be used for swallowing function screening, rehabilitation evaluation and follow-up visit monitoring, and has a good clinical application prospect.

Methods and systems for monitoring, preventing and / or treating upper airway disorders such as apnea, dysphagia, reflux and / or snoring are described. The methods and systems monitor the upper airway disorders by processing one or more neural signals obtained from one or more upper airway afferents. Upper airway disorders are prevented and / or treated by delivering one or more stimulations to one or more reflex-related afferents, efferents, muscles, and sensory receptors to manipulate the threshold and / or trigger an upper airway reflex including, but not limited to a swallow reflex and / or a negative-pressure reflex.

The invention discloses a breathing abnormity monitoring method and system based on electroencephalogram and pressure collaborative awareness, and relates to the field of biological data processing.The method comprises the steps that electroencephalogram signals and chest surface muscle mechanical vibration signals, synchronously collected in the breathing stage, of a user are obtained, and data preprocessing is conducted respectively; inputting the preprocessed electroencephalogram signal and the preprocessed chest vibration signal into a trained breathing type recognition model, and firstly, respectively inputting an electroencephalogram branch and a chest vibration branch in a double-branch structure to carry out feature extraction to obtain an electroencephalogram feature and a chest vibration feature; the electroencephalogram features and the chest vibration features are spliced through a splicing layer and then input into an ECA module for feature fusion to obtain fusion features, the fusion features pass through a full-connection classifier to obtain a corresponding breathing type, and if the breathing type is one of enzootic pneumonia, tachypnea, shortness of breath and apnea, it is determined that breathing is abnormal. The method solves the problem that characteristic similar states such as shallow and slow breathing and apnea are easy to misjudge.