149 results about "Obstructive sleep apnea" patented technology

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.

Obstructive sleep apnea from blockage of the upper airway can result in significant health issues. Described herein is an intelligent personalized closed loop neuromodulation system and methods to prevent backwards movement of the genioglossus muscle through stimulation of certain branches of the hypoglossal nerve and muscle motor points, by using muscle feedback from electromyogram sensors to provide optimal stimulus. Alternative embodiments to treat neuropathic pain and urinary dysfunction are enclosed.

The invention relates to the field of intelligent medical treatment, in particular to an obstructive sleep apnea syndrome detection method and device based on multi-modal data and a program product. Comprising the following steps: S1, acquiring time sequence frame data of infrared thermal imaging of a tester in real time; s2, extracting chest and abdomen position data and air temperature data of a nose and mouth area based on the infrared thermal imaging time sequence frame data; s3, calculating to obtain breathing waveform data through thoracic and abdominal position data in the time sequence frame data; s4, air temperature change curve data are obtained through air temperature data calculation in the time sequence frame data; s5, performing phase correlation on the breathing waveform data and the airflow temperature change curve data to calculate a breathing ratio; comparing the respiration ratio with a preset threshold value, and judging that the obstructive sleep apnea syndrome occurs when the respiration ratio is greater than the preset threshold value. The method can effectively identify the obstructive sleep apnea syndrome, and has a good clinical value.

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.

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 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 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 present application relates to novel substituted heterocyclic carboxamides, to processes for their preparation, to their use alone or in combinations for the treatment and / or prevention of diseases and to their use for producing medicaments for the treatment and / or prevention of diseases, in particular for the treatment and / or prevention of breathing difficulties including sleep-induced breathing difficulties such as central and obstructive sleep apnoea, snoring (primary and obstructive snoring), dysphagia, peripheral and cardiac vascular disorders including diabetic microangiopathies and disorders of the peripheral and central nervous system including neurodegenerative and neuroinflammatory disorders.

The invention discloses an obstructive sleep apnea event typing method and device based on respiratory center driving characteristics, electronic equipment and a computer readable storage medium, and belongs to the technical field of medical detection technologies and sleep medicine. According to the method, body surface diaphragm electromyographic signals and respiratory airflow signals of a subject are synchronously collected in a non-invasive mode, the body surface diaphragm electromyographic signals are preprocessed to obtain diaphragm electromyographic characteristic values, and the respiratory airflow signals are segmented and subjected to integral processing according to the respiratory cycle to obtain airflow characteristic values; selecting a characteristic value corresponding to at least one respiratory cycle before the event occurs as a reference baseline, calculating a diaphragm myoelectricity change value and an airflow change value of each respiratory cycle in the event relative to the reference baseline, and calculating the diaphragm myoelectricity change value based on a coupling relationship between the airflow change value and the diaphragm myoelectricity change value, and performing central drive dependent or non-central drive dependent judgment on the obstructive sleep apnea event. The method can provide technical support for typing analysis of the obstructive sleep apnea event.

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.

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 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.

The present invention relates to devices, systems and methods for detecting disturbances that may occur during the sleep of a subject using measurements and assessments of mandibular rotation and movement of a sleeping subject. A sensing unit may be positioned onto the exterior of the subject's mandible and may generate data measurements corresponding to mandibular rotation and movement. For example, measurements by an accelerometer and gyroscope corresponding to rotation and movement of the mandible provide the necessary information to arrive at an accurate assessment of sleep stages, sleep indicators and / or other sleep related information which may be used to make recommendations regarding sleep conditions and / or disorders. A report may be generated including such sleep stages, sleep indicators and / or other sleep related information as well as any recommendations regarding sleep conditions and / or disorders for use for diagnosing sleep events such as obstructive sleep apnea (OSA).

The invention discloses an obstructive sleep breathing state detection method based on joint denoising, and the method comprises the steps: firstly carrying out the preprocessing of an input original ECG signal, obtaining a standardized feature matrix, and completing the data set division; secondly, a hybrid model based on enhanced DenseNet-Transformer is used for carrying out deep feature learning and classification processing on the extracted feature matrix; and finally, performing forward reasoning on the ECG signal to be detected through the trained model, and outputting a detection result. According to the method, the technical problems that an existing OSA detection method depends on a fixed parameter filter, and noise and effective physiological signals are difficult to distinguish are effectively solved, and accurate and efficient obstructive sleep breathing state detection is carried out.

1. The name of the design product: headband for sleep apnea electrical stimulation instrument. 2. The use of the design product: headband for sleep apnea electrical stimulation instrument, providing non-invasive treatment for patients with obstructive sleep apnea syndrome. 3. The design features of the design product: the shape of the design product. 4. The picture or photo that best indicates the design features: perspective view 1.

The invention discloses an obstructive sleep apnea detection method based on multi-modal lightweight transformation, and the method comprises the following steps: data acquisition and preprocessing: obtaining an original audio signal, and carrying out the standardization processing and clinical baseline data preprocessing of the original audio signal; audio feature extraction based on MiniRocket: using random convolution kernel transformation to replace a traditional deep convolutional neural network, realizing efficient time sequence feature extraction of audio signals, and obtaining an audio feature vector; the method comprises the following steps of: extracting clinical semantic features based on BioBERT: enhancing the understanding of a pre-training language model on specific patient data by using general medical knowledge contained in the pre-training language model to obtain a text feature vector; and multi-modal feature fusion and classification judgment: fusing the obtained audio feature vector and the obtained text feature vector in a feature splicing mode, and then carrying out classification judgment through a ridge regression classifier (RidgeClassifier).

The application relates to the field of biotechnology and health informatics, and particularly discloses a microorganism marker combination for detecting obstructive sleep apnea of children and application thereof. The microorganism marker combination comprises the following nine microorganism genera: g__Streptococcus, g__Solobacterium, g__Schaalia, g__Capnocytophaga, g__Stomatobaculum, g__Mogibacterium, g__Clostridia_UCG-014, g__Selenomonas and g__Pseudoleptotrichia. By detecting the relative abundance of the above microorganism genera in a saliva sample of a child, a classification model is constructed by using a random forest algorithm, non-invasive and objective screening of OSA of the child is realized, and the application has high accuracy and clinical application value.

This invention discloses the application of reagents for detecting biomarker levels in the preparation of products for identifying obstructive sleep apnea. It relates to the field of biomarker technology and aims to overcome the shortcomings of existing technologies that rely on complex polysomnography (PSG) procedures, poor patient compliance, and a lack of effective molecular markers. The biomarkers used in this application consist of CD36 glycoprotein, 9-octadecenal, and protocatechuic acid 3-O-sulfate. This invention enables rapid and objective identification of obstructive sleep apnea by detecting the levels of the above biomarkers in plasma. The procedure is simple and well-accepted by patients. The combined use of multiple biomarkers can improve the accuracy and stability of the identification, making it suitable for screening or auxiliary diagnosis in medical institutions at different levels.

The present invention relates to methods, devices, and systems for dynamically adjusting mandibular advancement. In certain embodiments, the devices and systems employ actuator assemblies configured to allow a subject's lower jaw to be advanced by a minimum amount (e.g., trigged by obstructive sleep apnea (OSA) or associated significant oxygen saturations), but still allow the subject to move their jaw further forward, laterally, and open / close on their own. In some embodiments, the devices and systems employ an oxygen saturation measuring sensor linked to a motor control processor that: i) advances extending shafts forward when the oxygen saturation measuring sensor detects significant changes to oxygen saturation levels or other conditions that may signal OSA (e.g. a signal from another sleep monitoring device), and ii) retract the extending shafts backwards when the oxygen saturation measuring sensor detect normal oxygen saturation levels or other condition is met (e.g. elapsed time, head position, etc.).

According to an aspect, there is provided an apparatus (100) for assessing sleep of a subject, the apparatus comprising a processor (102) configured to: receive data indicative of one or more physiological parameters associated with a subject; determine, based on the data, two or more sleep parameters relating to sleep of the subject; and determine, based on the sleep parameters, whether the subject is suffering from (a) obstructive sleep apnoea, (b) insomnia, (c) comorbid insomnia and obstructive sleep apnoea, or (d) neither obstructive sleep apnoea nor insomnia, wherein a first sleep parameter of the two or more sleep parameters comprises Wakefulness After Sleep Onset for at least X Minutes, WASOXmin, wherein WASOXmin comprises the sum of awakenings with a duration of X minutes or longer during a sleep session, wherein X is equal to, or greater than, 1.

A treatment for obstructive sleep apnea (“OSA”) of a user includes affixing a patch externally on a dermis of the user, the patch including a flexible substrate, an adhesive on a first side adapted to adhere to the dermis of the user, a processor directly coupled to the substrate, and electrodes directly coupled to the substrate. The treatment includes detecting an occurrence of OSA and activating the patch in response to the detecting, the activating including generating an electrical stimuli via the electrodes to activate the genioglossus muscle of the user.

The present disclosure relates to a method and apparatus for predicting obstructive sleep apnea. The method for predicting obstructive sleep apnea according to one embodiment of the present disclosure includes generating analysis data from facial photograph information of an analysis subject, storing response data of an OSA screening questionnaire of the analysis subject in the memory, inputting the analysis data and the response data into a pre-trained machine learning model and inferring information about the degree of OSA, and transmitting the inference result to at least one terminal or outputting the inference result to a display.

A therapeutic composition for improving the symptoms of obstructive sleep apnea, the therapeutic composition comprising an aqueous solution. the aqueous solution may comprise: acetazolamide with a final concentration between 22.5 mg / ml and 52.5 mg / ml; ibuprofen lysine with a final concentration between 4 mg / ml and 20 mg / ml; propylene glycol with a final concentration between 0.5 mg / ml and 7.5 mg / ml; sodium chloride with a final concentration between 6 mg / ml and 9 mg / ml; and a preservative with a final concentration between 0.05 mg / ml and 0.15 mg / ml.

This method for classifying obstructive sleep apnea by using multimodal data comprises the steps of: receiving, by an analysis device, inputs of multimodal data of a subject; inputting, by the analysis device, the multimodal data into a trained learning model; and classifying, by the analysis device, whether the subject has obstructive sleep apnea, on the basis of a value output by the learning model. The multimodal data includes facial images, head X-ray images, and clinical data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anatomical feature-based medical predictions. One of the methods includes capturing a set of images depicting at least a portion of a subject body. The set of images are processed to extract a subset of images that satisfy one or more feature thresholds, such as thresholds related to anatomical pose or image quality. One or more target anatomical structures, such as craniofacial or oral cavity structures, are detected within the subset of images. One or more anatomical features are generated for the target anatomical structures, for instance, by fitting a three-dimensional model to the structures and computing geometric metrics. Using the generated anatomical features and stored medical data, a prediction is generated indicating a likelihood of a medical condition, such as obstructive sleep apnea or a comorbid condition, which can be transmitted to a device.

The invention provides a children obstructive sleep apnea prediction system which comprises a data screening and preprocessing module, a feature selection and construction module, a children OSA prediction model construction module, a model prediction module and a prediction result analysis module. According to the system, based on conventional physical examination data, sleep questionnaire data, blood oxygen monitoring data and other data which are easy to obtain and not prone to interference, the robustness of biological signal collection is improved, meanwhile, initial features are established through an antagonism training strategy, and the reliability of a prediction model is improved; different input features are established through independent or random combination, the importance of each input feature in predicting children OSA is combined, an optimal basic model and hyper-parameters are determined, and a children OSA prediction model is established, so that the prediction accuracy of children OSA is remarkably improved, SHAP analysis is provided to meet the decision support requirements of clinical diagnosis and treatment, and the clinical diagnosis and treatment efficiency is improved. The early screening of the obstructive sleep apnea disease of children is realized.

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.

The invention provides an obstructive sleep apnea detection method, system and device and a storage medium, and relates to the technical field of signal processing, and the method comprises the steps: obtaining pressure signals detected by at least two bed-mounted pressure sensors in a human body sleep state, and the crowd type characteristics and the pre-sleep activity characteristics of a to-be-detected person; performing quality evaluation on each path of pressure signal, and determining a target pressure signal based on a quality evaluation result; preprocessing the target pressure signal, and performing time domain and frequency domain feature extraction on the preprocessed target pressure signal; and inputting the extracted multi-dimensional features, the crowd type features of the to-be-detected person and the pre-sleep activity features into a pre-trained machine learning model to obtain a classification result of whether the to-be-detected person suffers from obstructive sleep apnea. According to the method and the device, under the condition that PSG equipment is not used, accurate detection of the OSA is realized.

1. The name of the design product: the upper panel of a breathing machine. 2. The use of the design product: the design product is used to provide gas to treat obstructive sleep apnea disease, or to assist sleep breathing. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view. 5. The bottom surface of the design product is not easily visible or invisible during use, and the bottom view is omitted. 6. Other circumstances that need to be explained: the blue shaded area is a non-protected area.