260 results about "Sleep apnea" patented technology

The invention discloses a physiological signal fragment analysis method based on an adaptive bidirectional selection state space, and the method specifically comprises the following steps: 1, carrying out the preprocessing of an original ECG signal X in a data set, and dividing the preprocessed data set into a training set Itrain and a test set Itest; step 2, constructing a physiological signal fragment analysis model based on an adaptive bidirectional selection state space, wherein the model comprises a multi-scale feature extraction module, a context encoder module and a classification module; 3, training the model constructed in the step 2 by using the training set Itrain processed in the step 1; and step 4, sending the test set Itest processed in the step 1 into the model trained in the step 3, and finally outputting a classification detection result. By adopting the method provided by the invention, the accuracy of analyzing whether the sleep apnea event occurs in the physiological signal fragment can be improved.

One aspect of the present disclosure relates to a system for treating obstructive sleep apnea in a subject. The system can include a power source and a neuromuscular stimulator in electrical communications with the power source. The neuromuscular stimulator can include a controller and at least one electrode. The controller can be configured to receive certain power and stimulation parameters associated with a therapy signal from the power source. The at least one electrode can be configured to deliver the therapy signal to a target tissue associated with control of a posterior base of the tongue of the subject.

The invention discloses an artificial intelligence-based obstructive sleep apnea judgment method, which comprises the following steps of: S1, acquiring the respiratory rate, the heart rate and the oxygen saturation of a user in a sleep period, and constructing a physiological data set; s2, filtering, denoising and cleaning the collected original physiological data set to remove noise and abnormal values; s3, key physiological features are extracted from the preprocessed physiological data set; s4, training a judgment model by utilizing improved deep learning, and performing comprehensive analysis by the judgment model according to the extracted physiological features to judge whether obstructive sleep apnea occurs or not; s5, the system monitors the sleep state of the user in real time, and provides instant feedback or alarm when obstructive sleep apnea is detected; and S6, the system performs analysis according to the physiological features of different users, and provides judgment results and health suggestions for individuals. The accuracy and real-time performance of obstructive sleep apnea judgment are improved, and accurate sleep apnea monitoring and judgment are achieved.

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 discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

A method of identifying a patient with obstructive sleep apnea implants a phrenic nerve stimulator in the patient or connects the phrenic nerve stimulator to the diaphragm, and adjusts the stimulation energy applied by the phrenic nerve stimulator to the phrenic nerve of the sleeping patient based on airway occlusion in the airway of the patient.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The disclosure provides systems and methods for treating obstructive sleep apnea using an acoustic sensor configured to detect acoustic sounds generated by the heart and lungs. Sensory data from the acoustic sensor is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

A method of reducing or treating snoring, sleep apnea and other forms of sleep disordered breathing, comprising having a patient in need thereof sleep in contact with or in the proximity of an article containing copper. A fabric having anti-snoring, sleep apnea and other forms of sleep disordered breathing properties, the fabric comprising a copper.

The invention discloses a system for predicting the occurrence risk of obstructive sleep apnea, and belongs to the technical field of digital medical treatment. The system for predicting the obstructive sleep apnea occurrence risk comprises a data acquisition module, a perioperative period risk assessment module, a risk prediction module and a result output module. The problem that in the prior art, it is difficult to accurately predict the OSA risk is solved, the risk can be comprehensively and accurately evaluated by comprehensively collecting basic physiological data, clinical symptom data and common disease information of the user and generating accurate feature data through processing, a reliable basis is provided for the user and medical staff, and by combining perioperative period risk evaluation, the risk prediction accuracy of the OSA risk is improved. According to the method, operation related risks can be identified, feature data and perioperative period risk data are fused through the constructed risk prediction model, the risk level of obstructive sleep apnea of the user can be evaluated, and a risk evaluation result and early warning can be output in time, so that intervention can be performed in advance, and the risk of a patient can be reduced.

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 utility model discloses a wireless intraoral genioglossus myoelectricity acquisition device and a functional electrical stimulation system thereof. According to the myoelectricity acquisition device, the electrode, the myoelectricity acquisition circuit and the wearable tooth socket are fixedly assembled and are subjected to waterproof packaging, and the technical scheme of wireless transmission and wireless charging is matched, so that the accuracy of signal acquisition and the wearing comfort of a user are effectively improved, and the myoelectricity acquisition device is suitable for being worn in a humid oral environment for a long time. According to the corresponding closed-loop functional electrical stimulation system, electromyographic signal monitoring and electrical stimulation are integrated, the activity state of genioglossus can be monitored in real time, and electrical stimulation is provided when necessary, so that the smoothness of the airway is maintained, and the breathing problem caused by airway collapse is effectively prevented. The system can also automatically adjust stimulation parameters according to real-time feedback of electromyographic signals, improves the accuracy and curative effect of treatment, and is particularly suitable for treating obstructive sleep apnea.

The invention discloses an intermittent hypoxia load prediction method for obstructive sleep apnea, which comprises the following steps: 1) data acquisition: acquiring oxyhemoglobin saturation information; 2) calculating an intermittent hypoxia index IHI, wherein IHI = S / TST or IHI = S / TRT; wherein S represents the total area under the desaturation curve, TST represents the total sleep time with minute as the unit, and the method is used for polysleep monitoring; tRT represents the total recording time and is used for portable sleep monitoring; iHI represents an intermittent hypoxia index; and 3) determining the intermittent hypoxia load of the obstructive sleep apnea based on the intermittent hypoxia index IHI. The invention provides a new method, namely a Newton-Kittius formula, to calculate the OSA patient nighttime hypoxia load (namely the area under a desaturation curve), and the calculation method has the characteristics that different algebraic precision operation can be performed on data according to the characteristics of the data, so that the result error is small.

Techniques for addressing sleep disorders are provided. A system includes a nerve stimulator that is configured to deliver stimulation energy to a nerve of a sleeping patient. A system includes a sensor for gather data from the sleeping patient and a controller for processing the data. The controller is configured to cause the stimulation energy that is provided to be adjusted based on the sensed data.

The invention discloses a sleep apnea detection method and system based on a multi-mode audio signal, and the method comprises the following steps: obtaining a trachea sound and an environment sound of a sleep record, and dividing the trachea sound and the environment sound into audio segments, respectively extracting trachea sound features and environment sound features from the trachea sound and the environment sound of each audio clip, calculating a corresponding trachea sound weight and a corresponding environment sound weight, and carrying out weighted fusion on the trachea sound features and the environment sound features of each audio clip to obtain corresponding multi-mode audio features; and detecting an abnormal breathing event for each multi-mode audio feature by using an enhanced branch pre-device, and evaluating the severity of sleep apnea according to the number of the detected abnormal breathing events. According to the invention, the accuracy and sensitivity of sleep apnea detection are obviously improved.

The invention provides a system for early predicting the incidence of obstructive sleep apnea diseases based on sleep behaviors. Specifically, the invention provides a sleep behavior-based early obstructive sleep apnea (OSA) disease occurrence risk assessment system, which comprises (a) an input module configured to input sleep behavior data of a certain object; (b) an evaluation module which is configured to perform preliminary evaluation on the input sleep behavior data, obtain a sleep score and perform discrimination processing; and (d) an output module configured to output the evaluation result value. The system provided by the invention can predict chronic obstructive sleep apnea more effectively, more early and more accurately, and provides effective prevention suggestions.

The invention provides a multi-modal fusion obstructive sleep apnea (OSA) identification method based on deep canonical correlation analysis (DCCA). For multi-modal characteristics, a whole process from data preprocessing, feature extraction and screening to feature fusion and classification is designed. Firstly, AHI indexes, voice data and craniofacial image data of a subject are collected and preprocessed, and DeepSpectrum voice features and geometric morphology image features based on MediaPipe are extracted. Then, feature screening is carried out by adopting a competitive group optimizer (CSO) and a ReliefF algorithm respectively; for a class imbalance problem, an oversampling (AMDO) method based on an adaptive mahalanobis distance is introduced to balance data. Non-linear mapping and deep fusion are carried out on high-dimensional features of the two modals through DCCA, low-dimensional high-correlation features are generated, and information complementarity between the modals is improved. And finally, inputting the fused features into a classifier and evaluating the performance by using five-fold cross validation. According to the invention, effective fusion between modals is realized by using DCCA, and a new method is provided for early screening and auxiliary diagnosis of OSA.

The invention relates to the technical field of biology, in particular to a construction method of an obstructive sleep apnea animal model. The invention provides a construction method of an OSA animal model, which comprises the following steps: respectively injecting 50% glycerol or LPS (lipopolysaccharide) on both sides of the palatopharyngeal muscle of a rabbit to construct the OSA animal model. The method not only can simulate the OSA comprehensively from dissection and pathophysiological features, but also has the advantages of high safety, high success rate and no need of high-end equipment and special operation. The method accords with a clinical practical OSA animal model, has a great promotion effect on revealing OSA pathogenesis, developing new drugs and the like, and has a good application prospect.

A method and system for detecting sleep apnea in patients using an electronic healthcare record (EHR) system. An API configured to communicate with the EHR system is installed and relevant data inputs for predicting sleep apnea determined. An initial risk assessment for sleep apnea is automatically performed for a patient based on their health history is performed when the patient is seen by a healthcare provider. A visual prompt is displayed to the healthcare provider if the patient is at an elevated risk for sleep apnea. The visual prompt may include a listing of additional tests needed, a numerical risk level, or allow the healthcare provider to schedule sleep apnea related appointments. The method may also involve calculating a patient risk level, providing feedback to improve the accuracy of the risk assessment, determining co-factors, and incorporating cost-benefit analysis in the risk assessment.

The invention provides a sleep breathing state monitoring method, a sleep monitoring system, equipment and a medium. The method comprises the following steps: generating a time-frequency spectrogram of a normal mixed heart impact signal; screening out an area associated with the heart rate in the time-frequency spectrogram based on the mask matrix to obtain time-frequency spectrum information; decomposing an intrinsic mode function of a normal mixed heart impact signal according to the information of the trained mask; time domain waveform information in the intrinsic mode function is extracted; and performing fusion analysis on the time-frequency spectrum information and the time-domain waveform information to generate a sleep apnea classification result. According to the sleep breathing state monitoring method provided by the invention, the time-frequency spectrum information with high quality and extremely low noise is obtained, the time-domain waveform information without distortion is also obtained, and further through fusion analysis of the time-frequency spectrum information and the time-domain waveform information, comprehensive analysis of sleep apnea events is realized, so that the sleep apnea state monitoring accuracy is improved. An accurate sleep apnea classification result is generated, and powerful technical support is provided for diagnosis and treatment of sleep disorder diseases.

The invention discloses a non-contact vital sign monitoring system and method based on a mechanical programmable metasurface, and belongs to the technical field of intelligent medical monitoring. According to the system, 360-degree continuous phase regulation and control are achieved through the mechanical programmable metasurface, the three-dimensional dynamic beam focusing technology and the improved variational mode decomposition algorithm are combined, limb movement noise is effectively restrained, and high-precision multi-person non-contact respiration monitoring is achieved. The system adopts a modular detachable metasurface architecture, supports the fast reconstruction of wavefront coding, and can reduce the average error of respiratory rate monitoring of a human body in a double-arm swinging / walking state to 0.5 times / minute without continuous power supply. Experiments show that when the system is used for synchronously monitoring breathing signals of the motion state of two persons in a complex indoor environment, the signal-to-noise is improved by 14 dB or above compared with that of a traditional scheme, and the manufacturing cost is reduced by 77.5%. The method is suitable for the fields of intelligent old-age care, infectious disease isolation monitoring, sleep breathing disorder screening and the like, and the clinical requirements of medical privacy protection and long-term health monitoring are met.

The invention provides a sleep apnea subtype detection method based on multi-modal information fusion, and the method comprises the steps: obtaining an original multi-modal physiological signal, carrying out the preprocessing of the original multi-modal physiological signal, obtaining a preprocessed signal window, carrying out the node definition operation of the preprocessed signal window, obtaining a node set, and carrying out the detection of the sleep apnea subtype. Performing homogeneous edge construction and heterogeneous edge construction based on the node set to obtain a homogeneous edge set and a heterogeneous edge set respectively, and performing feature extraction on signal windows corresponding to nodes by using a residual convolutional neural network based on the node set to obtain node embedding features; the node embedding features, the homogeneous edge set and the heterogeneous edge set form a sleep heterogeneous graph. According to the method, the drawing edge rule is designed based on the physiological mechanism, so that the decision basis can be traced to specific physiological features, the clinical interpretability is higher, an intuitive diagnosis basis is provided for clinicians, and the pain point of poor interpretability of a traditional black box model is solved.

Provided herein are quinazolinone compounds for modulating muscle myosin to treat obesity and related disorders such as cardiovascular diseases, type 2 diabetes, obstructive sleep apnea, cancer, and osteoarthritis.

The invention discloses a sleep apnea detection method based on multi-view acoustic spectral clustering, and belongs to the technical field of digital signal processing, and the method comprises the steps: collecting sleep respiration audio data of a user, and carrying out the framing processing; performing fast Fourier transform on the audio data after framing processing to generate a time-frequency graph of each frame; filtering each frame of audio signal by using a Mel filter, and superposing energy in each filter frequency band to obtain a Mel spectrogram; performing continuous wavelet transform on each frame of audio signal, and obtaining a wavelet map according to a wavelet coefficient; constructing a multi-view three-dimensional tensor according to the time-frequency graph, the Mel spectrogram and the wavelet graph; clustering the multi-view three-dimensional tensor to obtain a feature vector of each time step; using the feature vector as input, and using a pre-trained convolutional attention Utime network to perform apnea detection on sleep respiration audio data of the user; aiming at the low sleep apnea detection precision in the prior art, the invention provides the precision.

The disclosure provides systems and methods for treating obstructive sleep apnea using an inertial measurement unit (IMU) comprising an accelerometer and a gyroscope, wherein the IMU is configured to detect chest and / or abdominal movement by a patient during the inspiration and expiration stages of a respiratory cycle and to generate positional data based on the detected movement. Positional data generated by the IMU is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

A wireless intraoral genioglossus electromyography collection apparatus, and a closed-loop functional electrical stimulation system comprising the apparatus. In the electromyography collection apparatus, electrodes (5, 50), electromyography collection circuits (2, 3, 4) and a wearable dental appliance (1) are fixedly assembled and are subject to waterproof packaging, so that the accuracy of signal collection and the wearing comfort of a user are effectively improved, and the apparatus is suitable for being worn for a long time in an intraoral humid environment. The closed-loop functional electrical stimulation system integrates electromyographic signal monitoring and electrical stimulation, can monitor the activity state of the genioglossus in real time, and provides electrical stimulation when necessary to maintain airway patency, and thus effectively prevents breathing problems caused by airway collapse. The system can also automatically adjust stimulation parameters according to real-time feedback of electromyographic signals, such that the treatment accuracy and the treatment effect are improved, and the system is particularly suitable for treating obstructive sleep apnea.

Intelligent systems and methods for facilitating insomnia therapy are disclosed. Sensor data (e.g., non-contact sensor data) can be used to determine physiological parameter(s), such as sleep-related physiological parameter(s), which can be used to generate a sleep disorder prediction. The sleep disorder prediction can be used, along with an identified sleep therapy plan, to generate and facilitate application of (e.g., present to a user or automatically apply) a sleep therapy recommendation. When sleep apnea is predicted in concert with an identified cognitive behavioral therapy for insomnia (CBTi) plan, a warning can be presented to the user to not engage in certain CBTi therapies. Sensor data can also be used to automatically update therapy parameter(s) of an ongoing sleep therapy plan, such as in realtime.

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.

An oral aligning appliance uses upper and lower aligning trays to align a user's dentition while treating sleep apnea. The upper and lower aligning trays may be connected to each other by a strut removably connected to each of the upper and lower alignment trays and configured for moving the user's lower jaw forward for treating sleep apnea while the upper and lower alignment trays align the user's dentition. One or more of the upper and lower alignment trays and strut may be periodically replaced as part of progressive alignment treatment or adjustments to the sleep apnea therapy carried out at the same time.