214 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 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 relates to the technical field of medical instruments, and discloses a wearable obstructive sleep apnea early warning device which comprises a mask, an air pipe connector and two sets of fixing bandages, the air pipe connector communicates with the mask and is installed at the position, close to nostrils, of the mask, and the two sets of fixing bandages are installed on the two sides of the mask correspondingly; a nasal cavity rod is arranged at the position, close to the air pipe connector, in the mask. A rotating shaft is arranged on the outer side of the end, away from the airflow sensor, of the nasal cavity rod. Through the arrangement of a torsion assembly, a first limiting assembly, a nasal cavity rod and an airflow sensor, the airflow sensor is rapidly and accurately inserted into the nasal cavity, so that rapid and accurate wearing of the mask is achieved, the monitoring accuracy of the airflow sensor is improved, the data monitoring accuracy is improved, and by combining the design of a fixing bandage of the mask, the practicability is high. Meanwhile, the mask can be prevented from falling off due to turning of the patient in the sleeping process, and monitoring continuity and accuracy are improved.

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.

The invention discloses an obstructive sleep apnea detection method and system based on a multi-scale convolutional neural network, a storage medium and electronic equipment. The obstructive sleep apnea detection method specifically comprises the following steps: loading single-lead electrocardiogram (ECG) data, and preprocessing the data; a P-R interval is determined; calculating by using Euclidean distance to obtain a two-dimensional distance array of the five-minute segment; further intercepting the 5-minute fragment into a 3-minute fragment and a 1-minute fragment; constructing three different CNN models, and respectively inputting the maximum distance and the minimum distance in the three scale fragments into the three CNN models; the convolution feature map of each model is optimized, and important information in the features is enhanced through a residual attention module; applying a channel attention module to optimize the attention capability of the model to the key features; and integrating the feature maps subjected to residual error and channel attention optimization, performing final classification output through a full connection layer, and predicting whether OSA (Obstructive Sleep Apnea) exists or not.

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.

Methods, devices, and systems are disclosed for controlled delivery of a therapy, such as a stimulant, to a mouth of a subject via an oral device positioned in a secured configuration in the mouth. At least one of a tongue position stimulator (TST) and tongue position sensor (TSE) is provided, according to certain aspects. According to another aspect, a stimulus is delivered to the mouth and / or tongue via a mouthpiece secured to the subject's teeth. In another regard, a stimulus is delivered that generates a natural response to eliminate or reduce sleep disorders, such as for example at least one of snoring and obstructive sleep apnea.

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.

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 disclosure generally relates to systems and methods for monitoring and / or the sleep stage of an individual using one or more sensors, and methods of treating medical conditions related thereto (e.g., obstructive sleep apnea).

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 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 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 relates to a device for the diagnosis of obstructive sleep apnea.

The present application relates to adrenergic receptor ADRAC2 antagonists, to novel substituted heterocyclic carboxamides, to a method for the production thereof, to the use thereof alone or in combination for the treatment and / or prophylaxis of diseases and to the use thereof for producing medicaments for the treatment and / or prophylaxis of diseases, in particular for the treatment and / or prophylaxis of dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea. Such as central and obstructive sleep apnea, snoring (primary and obstructive snoring); dysphagia; peripheral and cardiovascular conditions, including diabetic microangiopathy; and peripheral and central nervous system disorders, including neurodegenerative and neuroinflammatory disorders.

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 present application relates to novel substituted heterocyclic carboxamides, to a method for the production thereof, to the use thereof alone or in combination for the treatment and / or prophylaxis of diseases and to the use thereof for producing medicaments for the treatment and / or prophylaxis of diseases, in particular for the treatment and / or prophylaxis of dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea, including sleep-induced dyspnea. Such as central and obstructive sleep apnea, snoring (primary and obstructive snoring); dysphagia; peripheral and cardiovascular conditions, including diabetic microangiopathy; and peripheral and central nervous system disorders, including neurodegenerative and neuroinflammatory disorders.

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.

The invention described herein refers to a method for measuring a degree of airway opening, e.g. of muscle displacement, to a system for implementing said method as well as to a use of the method and system in obstructive sleep apnea therapy.

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.