127 results about "Apnea" patented technology

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to a sleep respiration detection method, a sleep respiration detection system and a sleep respiration detection cloud platform for apnea syndrome, and the sleep respiration detection system for apnea syndrome is used for collecting a respiration signal caused by thoracic movement of a wearer, and processing and uploading the respiration signal through a microcontroller. According to the sleep respiration detection method, original signals are denoised based on wavelet transform and a soft threshold function, and an apnea event is judged through the combination of a dynamic amplitude threshold and a time threshold. The sleep respiration detection cloud platform adopts an SVM and LSTM-TCN fusion model to identify respiration abnormity, calculates risk scores in combination with blood oxygen decline, event duration and frequency, and triggers early warning in a grading manner. The system supports federated learning and model issuing, and realizes personalized recognition and cloud edge collaboration. The sleep apnea monitoring system realizes continuous monitoring and intelligent identification of sleep apnea, and is suitable for home health management and remote medical treatment.

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 sleep apnea detection method and device and electronic equipment. The method comprises the following steps: acquiring target physiological signal data corresponding to a target object; extracting target respiratory signal data from the target physiological signal data; calling a reference frequency amplitude fluctuation index; according to the target respiratory signal data, multiple pieces of sub respiratory signal data are determined; determining sub-respiration frequency amplitude characteristics corresponding to the multiple pieces of sub-respiration signal data; and determining an apnea detection result of the target object according to the reference frequency amplitude fluctuation index and the sub-respiration frequency amplitude characteristics corresponding to the multiple pieces of sub-respiration signal data. The sleep apnea detection method and the sleep apnea detection device solve the technical problem that sleep apnea detection is inaccurate when sleep apnea detection is carried out according to physiological signals.

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

The present invention discloses a microecological animal model for obstructive sleep apnea syndrome, which includes steps such as constructing a fitting model for the changing trend of OSA events, selecting target animals and performing pretreatment, preparing an animal model, obtaining fecal samples, and DNA extraction and 16S rRNA high-throughput sequencing. By constructing a fitting model for the changing trend of respiratory sleep apnea events, the present invention obtains the fitting mathematical relationship between the number of apnea and time, and establishes an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply to establish an intestinal flora model for studying obstructive sleep apnea syndrome, which can simulate the actual physiological environment during sleep of patients. According to the changing trend of the duration of patients' respiratory sleep apnea events, a microecological animal model more consistent with the actual situation is established, making the detection result of the flora structure more accurate.

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.

Through the multi-sensor fusion design of the microphone, the millimeter wave radar sensor and the piezoelectric film sensor, compared with an existing single or double-sensor sleep monitoring technology, the monitoring precision, the function dimension, the environmental adaptability and the like are remarkably improved. According to the method, a weighted voting decision fusion mechanism and an improved random forest model are adopted, and after a microphone acoustic monitoring dimension is newly added, the apnea detection sensitivity is improved; acoustic characteristics such as snore, breath sound rhythm, cough and dream can be accurately captured; health risk assessment dimensions are richer, preliminary screening of diseases such as sleep apnea syndrome and periodic limb dyskinesia can be realized, snore indexes and breathing stability are newly increased, and a multi-dimensional sleep quality report is provided for a user; in the whole process, an electrode plate or a wrist strap does not need to be worn, the three sensors all adopt non-intrusive design, no foreign body sensation exists after the piezoelectric film is embedded into the mattress, the problem of wearing discomfort of traditional monitoring is solved, and the user compliance 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 relates to a sleep apnea detection method and device, embedded equipment and a storage medium. The method comprises the steps of obtaining PPG signal data of a user during sleep, preprocessing the PPG signal data to obtain a plurality of PPG signal segments, extracting a one-dimensional time sequence feature vector used for reflecting the change condition of heart rate and blood flow for each PPG signal segment, converting the PPG signal segments into two-dimensional frequency spectrum images, and outputting the two-dimensional frequency spectrum images. Extracting a two-dimensional image feature vector used for reflecting spectrum structure characteristics based on the two-dimensional spectrum image, fusing the one-dimensional time sequence feature vector and the two-dimensional image feature vector to obtain a fused feature vector, and performing standardization and dimension reduction processing on the fused feature vector to obtain a target feature vector; and inputting the target feature vector into a trained classification model for classification to obtain a classification result of whether the PPG signal segment has the apnea event. By adopting the method, the accuracy of apnea detection during sleep can be improved.

The invention discloses a sleep apnea monitoring system and method, and the method comprises the following steps: S1, collecting a multi-modal physiological signal, executing the preprocessing, and generating a standardized sequence; s2, recognizing a sleep stage label, and extracting breathing phase and thoracico-abdominal phase difference information; s3, detecting a suspected apnea event, calculating a consistency index and a confidence coefficient, and outputting a candidate event; s4, when a safety threshold is met, disturbance verification is carried out, responses before and after intervention are collected, and event types are judged; s5, constructing an upper airway digital twinborn model, simulating and predicting an intervention effect, and outputting an abnormal severity score and an early warning; and S6, fusing the event and the risk information, generating a self-evolution risk curve, and outputting a monitoring report. According to the method, through multi-modal physiological signal fusion and upper airway digital twin modeling, accurate recognition, typing judgment and individualized risk dynamic monitoring of sleep apnea are achieved.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a sound signal analysis-based event processing method, device, equipment and medium, and the method comprises the steps: obtaining a to-be-analyzed sound signal, carrying out the segmentation processing of the to-be-analyzed sound signal, and obtaining a target sound segment; extracting sound features of the target sound fragment, and comparing the sound features with reference sound features in a preset feature library to generate a comparison result; and determining an event type corresponding to the target sound segment based on the comparison result, and when the event type satisfies a preset condition, executing a processing operation corresponding to the event type. According to the method, through sound signal segmentation processing and feature extraction, event types such as snore, apnea and enzootic pneumonia are accurately recognized, the event recognition accuracy is improved by combining feature comparison, corresponding processing is automatically executed based on the event types, and self-adaptive response of multiple event types is achieved.

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 application discloses a method and system for electrocardiosignal pattern recognition for sleep respiratory state discrimination, and relates to the technical field of apnea prediction, and comprises the following steps: acquiring a chest electrode stress state and constructing a dynamic baseline, mapping the electrocardiosignal based on the dynamic baseline to distinguish dynamic noise segments, implementing rhythm rearrangement based on the mapping result to obtain a corrected electrocardiosignal, introducing a body position evolution direction correlation constraint in combination with contact impedance changes, forming an electrocardio expression result under the constraint of a body position stable window, and reconstructing a risk trigger path based on the result to realize continuous rhythm risk judgment. The application realizes effective recognition of electrocardiosignal patterns under the interference of body position changes by establishing a correlation between electrode stress and contact impedance changes, and improves the analysis stability and physiological state discrimination accuracy of electrocardiosignals in a complex sleep environment.

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.

Described herein are various embodiments of an informative display that may be used in conjunction with non-contact patient monitoring systems and methods. The informative display may include one or more patient data images providing historical and real time data relating the patient being monitored by the non-contact patient monitoring system. The patient data images may be configured to display visualizations of various patient events, such as low flow, apnea, and / or patient movement.

The invention discloses an apnea syndrome evaluation method based on a smart watch. According to the method, a photoelectric volume pulse wave signal and a three-axis acceleration signal in the sleep period of a user are collected through a built-in sensor of the smart watch, and filtering and window segmentation preprocessing is carried out on the signals. Extracting multi-dimensional features such as heart rate, heart rate variability, body movement index and respiratory rate from each analysis window, and constructing a feature vector sequence; and analyzing the feature vector by using a pre-trained machine learning classification model, outputting the apnea event probability and performing binary classification. And finally, quantitatively analyzing the event sequence, calculating an apnea hypopnea index and dividing risk levels. According to the method, the signal processing technology and the machine learning algorithm are combined, intelligent identification and risk assessment of the sleep apnea event are achieved, and an effective solution is provided for portable monitoring.

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 invention relates to modified release oral formulations of therapeutic agents, including gamma hydroxybutyrate (GHB), paracetamol, codeine or oxycodone, which are resistant to alcohol induced dose dumping. Provided are formulations that have improved resistance to rapid release of the active ingredient in the presence of increasing amounts of alcohol. Also provided are formulations that can reduce or prevent the release of the active ingredient following exposure to alcohol-containing media. The invention also relates to methods of making the formulations, and methods of their use for the treatment of sleep disorders such as apnea, sleep time disturbances, narcolepsy, cataplexy, sleep paralysis, hypnagogic hallucination, sleep arousal, insomnia, and nocturnal myoclonus.

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.