186 results about "SLEEP APNEA (device)" patented technology

An in vitro training method includes scanning a user's oral cavity to create a first oral cavity model. An articulator body is selected according to the first oral cavity model, and plural first customized electrode sheets are formed and assembled onto the articulator body to obtain an in vitro training device. The user bites the in vitro training device during a non-sleep period and uses the plural first customized electrode sheets to proceed with electrical stimulation on a top surface and two sides of a tongue of the user, thereby training a muscular endurance of infrahyoid muscles of the user for a training period. The user's oral cavity is scanned again to create a second oral cavity model. The first oral cavity model is compared with the second oral cavity model to timely renew the customized electrode sheets when the image over rate is lower than 80%.

The invention discloses a sleep apnea type distinguishing method and device based on a millimeter wave radar, and a computer readable storage medium. The method comprises the following steps: acquiring a millimeter wave radar echo signal of a monitored object in a monitoring area; extracting body movement signal characteristics, thoracic cavity reflection signal energy characteristics and respiratory signal amplitude characteristics from the echo signals; on the basis of the extracted body movement signal features, the chest reflection signal energy features and the respiratory signal amplitude features, constructing a feature sequence with a time sequence; and inputting the feature sequence into a pre-trained classification model based on a self-attention mechanism to perform dynamic correlation analysis, and determining the sleep apnea type of the monitored object. The method has the advantages of non-contact type, accurate distinguishing of sleep apnea types and the like.

The invention relates to a sleep apnea monitoring method and device, electronic monitoring equipment and a storage medium. The method comprises the following steps: acquiring PPG signal data of a user in a sleep period, performing segmentation processing on the PPG signal data, performing quality scoring on each PPG signal fragment data obtained by the segmentation processing, screening out target PPG signal fragment data of which the quality score is lower than a quality score threshold value, and sending the target PPG signal fragment data to the user; the quality score threshold value dynamically changes based on statistical characteristics of quality scores in different time periods, blood oxygen detection is conducted on the target PPG signal segment data to obtain blood oxygen index data, effective respiratory wave detection is conducted based on the target PPG signal segment data to obtain respiratory index data, and the effective respiratory wave detection is conducted based on the blood oxygen index data and the respiratory index data to obtain the target PPG signal segment data. Whether a sleep apnea event or a hypopnea event is present during sleep of the user is identified. By adopting the method, more accurate sleep apnea monitoring can be realized.

The invention belongs to the technical field of medical monitoring, and particularly discloses a sleep apnea syndrome automatic detection device which comprises a signal acquisition module, a data preprocessing module, a feature extraction module and a fusion decision module. The signal acquisition module is used for acquiring an RR interval signal and a respiratory wave signal; the data preprocessing module is used for removing interference areas through preprocessing and obtaining preprocessed RR interval signals and breath wave signals; the feature extraction module is used for determining sleep apnea events represented by the RR interval signals and the respiratory wave signals through statistical analysis on the basis of the preprocessed RR interval signals and the respiratory wave signals; and the fusion decision module is used for analyzing relevance between sleep apnea events represented by the RR interval signals and the respiratory wave signals and determining a sleep apnea detection result. According to the application, the sleep apnea syndrome can be detected stably and efficiently under the condition that the detection accuracy is guaranteed.

The invention relates to the technical field of medical instruments, and discloses an intelligent device for monitoring the cardio-pulmonary function of a heart failure patient, the intelligent device comprises a multi-mode sensor module, an AI analysis module, a biological feedback module and a wearable carrier, the multi-mode sensor module is used for collecting heart rate, respiratory rate, oxyhemoglobin saturation and body movement data in real time, and the AI analysis module is used for analyzing the heart rate, the respiratory rate, the oxyhemoglobin saturation and the body movement data; monitoring modes can be dynamically switched in rest, motion and sleep scenes; the AI analysis module is used for detecting abnormity based on real-time data and predicting a heart failure deterioration risk in combination with historical data; and the biological feedback module is used for providing personalized intervention in a vibration or audio mode according to the analysis result. The heart rate, the respiration rate, the blood oxygen saturation and the body movement data are collected at the same time through the multi-mode sensor module, sleep apnea or periodic respiration is recognized in combination with the AI analysis module, the defect of separation of cardiopulmonary function monitoring and sleep mode analysis in the prior art is overcome, and therefore early intervention and treatment adjustment are supported.

The invention belongs to the technical field of medical signal processing, and discloses a sleep apnea detection method and system, electronic equipment and a storage medium, and the method comprises the following steps: obtaining synchronous ECG signals and respiratory signals, and processing the signals into bimodal data fragments containing a plurality of time scales; a parallel neural network architecture is constructed, ECG features and respiratory signal features are extracted from the data segments of all scales, and cross-modal feature pairs are formed; designing a cross-scale dynamic weight correction attention mechanism, and carrying out dynamic weighting and information fusion on cross-modal feature pairs among different scales by introducing a correction factor based on an Euclidean distance among feature vectors so as to enhance the relevance between local details and a global context; and inputting the fused multi-scale features into a classifier, and outputting the probability of the sleep apnea event. Through multi-scale collaborative analysis and a dynamic weight correction attention mechanism, physiological information of different time dimensions is effectively fused, and the detection accuracy is improved.

The invention discloses a sleep apnea type analysis device and method, and the device comprises a preprocessing module which is used for collecting an original signal through a piezoelectric film sensor, and carrying out the preprocessing of the original signal, and obtaining a framed breathing signal, a framed original signal, and a snoring signal; the first determining module is used for determining a breathing attenuation descending signal based on the framing original signal and determining a breathing effort ascending signal based on the framing breathing signal; the second determination module is used for determining an autocorrelation degree characteristic value of the apnea segment based on the framing original signal; and the sleep apnea type analysis module is used for determining the sleep apnea type based on the respiratory attenuation drop signal, the respiratory effort rise signal, the snoring signal and the self-correlation degree characteristic value, apnea fragments can be rapidly positioned based on the steps, and then the sleep apnea type is rapidly and accurately analyzed.

Systems and methods for monitoring and staging sleep using cardiac and respiration information are disclosed. An exemplary system comprises a storage device to store a trained hybrid sleep detection and classification model that comprise a plurality of trained machine-learning models each trained to map input cardiac and respiratory data into one of multiple distinct model-specific awake or sleep classes, and a trained regression model to combine the model-specific awake or sleep classes to a composite awake or sleep classification. A sleep detector applies patient cardiac and respiratory information to the trained hybrid sleep detection and classification model to determine a composite awake or sleep classification for the patient. The composite awake or sleep classification is used to diagnose medical conditions including sleep apnea.

The invention provides an obstructive sleep apnea syndrome monitoring device and a storage medium, and relates to the technical field of sleep monitoring. The device comprises an electrocardio acquisition assembly used for acquiring electrocardio signals of a user in the sleep period; and the processor is used for determining a waveform feature set of the electrocardiosignal in a target time period, determining sleep apnea features of the target time period based on the waveform feature set, and determining an obstructive sleep apnea syndrome monitoring result of the target time period based on the sleep apnea features. According to the invention, the operand of the processor can be reduced, so that the monitoring efficiency of the obstructive sleep apnea syndrome is improved, and the power consumption of the processor is reduced, so that equipment with relatively low electric quantity or operand such as wearable equipment can comprise the processor, whether a user is in an obstructive sleep apnea state or not can be monitored in real time, and the user experience is improved. And the timeliness and the efficiency of monitoring the obstructive sleep apnea syndrome are improved.

The invention relates to the technical field of biomedical signal processing, and discloses a sleep apnea detection method, device and equipment, a medium and a product, and the method comprises the following steps: obtaining a target vital sign signal; performing wavelet transformation on the target vital sign signal to obtain a respiratory effort signal and a wavelet level sub-band signal corresponding to the respiratory effort signal; constructing a feature matrix according to the respiratory effort signal and the wavelet level sub-band signal; the feature matrix comprises a plurality of dimensions of signal features; the signal features are obtained by performing feature extraction on the respiratory effort signal or performing relative feature extraction between sub-band signals on the wavelet level sub-band signal; inputting the feature matrix into a detection model to obtain the sleep apnea times of the target population; the detection model is obtained by training with vital sign signals as samples and signal features as labels. The sleep apnea event can be accurately detected.

The application belongs to the technical field of medical signal processing, and discloses a sleep apnea detection method and system, an electronic device and a storage medium. The method comprises the following steps: acquiring a synchronous ECG signal and a breathing signal, and processing the synchronous ECG signal and the breathing signal into a bimodal data segment containing multiple time scales; constructing a parallel neural network architecture, extracting ECG features and breathing signal features from each scale data segment respectively, and forming a cross-modal feature pair; designing a cross-scale dynamic weight correction attention mechanism, introducing a correction factor based on the Euclidean distance between feature vectors, dynamically weighting and information fusion on the cross-modal feature pair between different scales, so as to enhance the relevance of local details and global context; inputting the fused multi-scale features into a classifier, and outputting the probability of a sleep apnea event. Through multi-scale collaborative analysis and a dynamic weight correction attention mechanism, the application effectively fuses physiological information of different time dimensions, and improves the detection accuracy.

A medical device for the treatment of Obstructive Sleep Apnea (OSA), the medical device including: a set of electrical stimulation electrodes; a stimulation circuitry configured to generate excitation waveforms; a set of sensors; a power source; and a controller running an adaptive feedback algorithm to monitor and modulate the physiology of the upper airway muscles and the diaphragm by modifying excitation waveforms generated by the stimulation circuity.

The invention discloses a sudden physiological danger detection method and system used in a plateau sleep scene, belongs to the technical field of medical health monitoring, and solves the problems that existing PSG equipment is expensive and complex in operation, needs professional personnel to carry out data analysis and is not suitable for long-term or large-scale monitoring in a plateau environment. The method comprises the steps of collecting physiological monitoring data in real time by adopting bracelet detection equipment, performing feature extraction on a preprocessed data set, performing blood oxygen, heart rate and sleep apnea detection on a feature set, performing sudden physiological danger detection based on a Transform model, and outputting a sudden physiological danger detection result. According to the embodiment of the invention, PPG and 6-axis IMU data are utilized, a deep learning algorithm is combined, the detection accuracy of the sleep apnea event is remarkably improved, compared with a traditional PSG method, false alarm and missing alarm are reduced, and meanwhile, through a multi-task learning strategy of a Transform model, prediction of the blood oxygen saturation degree and the heart rate and detection of sleep apnea are optimized.

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.

A hose retaining device is disclosed herein. The hose retaining device includes a strap assembly coupled to a padding portion having a top surface, a protruding member, and a retaining loop. The strap assembly further includes hook portion, a loop portion, and a fastening slot for turning the strap assembly back upon itself to couple the hook portion to the loop portion. The retaining loop includes an underside configured to receive and retain a corrugated tubing having a plurality of sunken portions and a plurality of protruding portions. The hose retaining device is useful for keeping a corrugated tubing item, commonly used in conjunction with a breathing assistance device, out of the way of the user to increase comfort and stability.

The present invention discloses a sleep apnea monitoring method, comprising: acquiring EEG signals, EOG signals, and photoplethysmography signals; automatically staging sleep and calculating sleep duration based on the EEG and EOG signals; counting apnea and hypopnea events based on the photoplethysmography signals; and calculating the AHI index based on the calculated sleep duration (ST) and the number of apnea and hypopnea events (BSN). Furthermore, a corresponding head-mounted sleep apnea monitoring system and electronic device are also disclosed. The present invention enables real-time and accurate sleep staging and apnea diagnosis.

Improved systems, methods, and devices for treating sleep apnea are provided herein. In one aspect, an intraoral appliance for treating sleep apnea in a patient comprises an appliance shell comprising a plurality of cavities shaped to receive teeth of a jaw of the patient. The appliance shell can comprise an advancement structure arranged to interact with an opposing jaw of the patient so as to displace the lower jaw anteriorly relative to the upper jaw. The plurality of cavities can comprise cavity geometries shaped to reduce repositioning of one or more received teeth elicited by displacement of the lower jaw anteriorly relative to the upper jaw.

A medical device may be configured to determine heart rates of the patient based on the cardiac signal sensed during a first period. The medical device may detect one or more sleep apnea episodes of the patient occurring during the first period. The medical device may determine whether one or more verification conditions are satisfied. Responsive to determining that the one or more verification conditions are satisfied, the medical device may measure impedances of the patient during a second period subsequent to the first period, and use impedance to the measured impedances to detect one or more sleep apnea episodes of the patient occurring during the second period.

The invention discloses a sleep apnea monitoring system based on non-contact intervention, and belongs to the technical field of medical health monitoring. The method solves the problems that in the prior art, a PSG method is tedious in operation and high in cost, household contact equipment brings discomfort to a user, and natural sleep is affected, physiological signals are collected through a non-contact biological radar sensor, an initial threshold value, an auxiliary threshold value and an environment factor are synthesized to calculate an optimal threshold value, and the optimal threshold value is obtained. The detection accuracy of the apnea and hypopnea events is improved; in combination with the apnea hypopnea index, the event duration factor, the blood oxygen decreasing factor and the event type factor, severity scores are calculated, and the patients are divided into mild patients, moderate patients and severe patients; according to the severity, the grade and the snore type of the real-time event, the optimal intervention measure is selected from the intervention strategy library, personalized intervention is achieved, interference to sleep of the patient is reduced, and the sleep quality and the life quality are improved.

Sleep apnea prediction using electrocardiograms and machine learning is described. In one or more implementations, a wearable monitoring device produces electrical potential measurements of a heart of a user during an observation period spanning multiple days. A sleep apnea classification of the user is predicted by providing the electrical potential measurements to one or more machine learning models as input. The one or more machine learning models are trained based on historical electrical potential measurements and historical outcome data of a user population to correlate patterns in electrical potential measurements to sleep apnea classifications. The sleep apnea classification may then be output, such as in a health report, via a user interface, as notification on a computing device, and so forth.

The invention is suitable for the technical field of sleep apnea detection, and provides a sleep apnea detection method based on wavelet transform. According to the method, the discrete wavelet decomposition technology is adopted, electrocardiogram signals are decomposed into multi-scale information to serve as model input, the supervised comparative learning concept is fused, an end-to-end deep learning network model is constructed, and efficient and accurate classification of sleep apnea is achieved. Compared with a traditional detection method which depends on an expert manual analysis method or needs complex preprocessing steps, the detection efficiency is improved, and the complexity of signal processing is simplified. More importantly, the time sequence signals are directly used as input, the complex secondary feature extraction process is avoided, and therefore the method can be converted into actual hardware equipment more easily to be implemented and applied. In addition, a disclosed Apnea ECG data set is used for testing, and the prediction accuracy of the method exceeds that of a current automatic sleep apnea detection model based on an electrocardiogram.

Exemplary systems, methods, and devices are disclosed which provide at-home, portable, wireless sleep sensors and wearable electronics with embedded machine learning. Such devices have applications in assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system at a sleep center using numerous bulky sensors, the soft all-integrated wearable platform offers natural sleep in a familiar setting. The face-mounted patches that detect brain, eye, and muscle signals show comparable performance in sleep monitoring with polysomnography in a clinical study. Furthermore, deep learning is embedded in an exemplary device, offering automated high-precision sleep scoring, which demonstrates the wearable system's portability and point-of-care usability. The at-home wearable patches help to support portable sleep monitoring and home healthcare.

The invention relates to the training of an artificial neural network model to be used for the detection of sleep apnea, a prediction method, and a system to perform said prediction.

The invention discloses a sleep respiration detection method and device, terminal equipment and a storage medium, and belongs to the technical field of terminals. The method is applied to terminal equipment and comprises the steps that corresponding sleep data of a user in the sleep process are obtained, and the sleep data comprise body movement signals collected through an inertial sensor IMU of the terminal equipment; according to the sleep data, extracting sleep feature data; calculating a first apnea hypopnea index AHI according to the sleep feature data; and determining a first risk level corresponding to the sleep apnea hypopnea syndrome SAHS according to the first AHI. According to the method, when the user sleeps, the IMU can be used for collecting the body movement signal of the user on the bed in a non-contact mode, the sleep feature data are extracted based on the collected body movement signal, and the AHI is calculated, so that the risk level corresponding to the SAHS is determined in time, the method does not need to be carried out in a special sleep monitoring room, the measurement process is convenient and simple, and the measurement efficiency is improved. And the efficiency of detecting the SAHS of the user is improved.

Introduced are methods and systems for monitoring a person's sleeping patterns, and detecting episodes of sleeping disorders such as snoring and sleep apnea. In one embodiment, a sensor strip attached to the mattress monitors the user's breathing, and detects signature frequencies corresponding to snoring and sleep apnea. Once a sleeping disorder is detected, a notification can be sent to a device associated with the user, or the user's bed can be automatically adjusted to alleviate the sleeping disorder.

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 present technology is generally directed to wearable devices for wearable devices for sleep apnea treatment systems. The wearable devices of the present technology can be configured to be worn comfortably and securely around at least a portion of a wearer's neck and / or one or more other portions of the wearer's anatomy. At least some of the wearable devices described herein include one or more power transmission devices configured to transmit power to one or more implantable devices positioned within a patient. Additionally, or alternatively, the wearable devices of the present technology can include one or more sensors configured to obtain data associated with a breathing obstruction experienced by a wearer.

Embodiments provide device for treating sleep apnea, the device including: a stand, a chin and neck support, an applicator integrated into the chin and neck support configured to provide High-Intensity Focused Electromagnetic Technology (HIFEM) therapy and Radio Frequency (RF) therapy, and a control panel configured to adjust an intensity of the delivered HIFEM and RF therapies in real-time. The applicator is configured to provide HIFEM therapy by delivering HIFEM energy to a patient's mouth, throat, and neck to stimulate and strengthen muscles associated with the patient's breathing, and further configured to provide RF therapy by delivering RF energy to reduce fat tissue in the patient's airway.

The utility model relates to a sleep apnea monitoring device based on snore, which belongs to the technical field of snore monitoring and comprises a snore collecting component, a sleeping posture adjusting component and a cushion used for arranging the snore collecting component and the sleeping posture adjusting component. The snore collecting assembly comprises an inflatable pillow, a first microphone, a second microphone, a third microphone, a signal adjusting circuit and a controller, the first microphone, the second microphone and the third microphone are all connected with the controller through the signal adjusting circuit, the first microphone, the second microphone and the third microphone are all arranged on a support, and the inflatable pillow is arranged on the support. The support is rotationally connected with the inflatable pillow, and a supporting piece is further arranged between the support and the inflatable pillow. The snore collecting assembly is arranged, so that a microphone for collecting snore is close to the nose of the patient, and the snore of the patient can be conveniently collected; the sleeping posture adjusting assembly is arranged, so that the sleeping posture of the patient can be adjusted through inflation or deflation, and the snore condition is relieved.

The invention provides a sleep apnea monitoring method, device and equipment and a storage medium. According to the sleep apnea monitoring method and device, shallow and deep physiological data are captured through multiple wavelength signals corresponding to multiple light sources, and blood oxygen signals are obtained through the obtained physiological data; then, analyzing local features and different weights of various physiological data and blood oxygen signals to obtain global features representing a change time sequence of a sleep breathing event of the user, and judging whether the user has a hypopnea event or not by analyzing continuous fragment features of a first preset duration in the global features; according to the scheme, whether the hypoventilation event is accurate or not is verified again in a mode of secondary confirmation of the abnormal signals according to the condition that whether the hypoventilation event occurs or not and whether continuous abnormal signals still occur or not after the hypoventilation event occurs, and the scheme can be completed through the wearable equipment, so that the equipment has the advantages of miniaturization, low power consumption and low cost; and the technical scheme is obtained through comprehensive analysis of various data, so that the accuracy of the result can be improved.