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

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 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.

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.

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.

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.

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.

The invention relates to the technical field of signal processing, in particular to a vital sign risk assessment method for ballistocardiogram signals, which comprises the following steps of: calculating heart rate index data, sleep quality index data and sleep apnea index data based on the ballistocardiogram signals and respiratory effort signals acquired in a user monitoring process; the vital sign risk assessment method and device are used for performing vital sign risk assessment and improving the accuracy and efficiency of vital sign risk assessment.

The invention belongs to the technical field of exhalation valves, and discloses an end-expiratory positive pressure exhalation valve which comprises a patch, an adjustable micro valve, a hole, a flow velocity sensor, a pressure sensor and a controller. An adjustable micro valve is arranged in the patch; a hole is formed in the adjustable micro valve; a controller is fixed at the left end of the adjustable micro valve; and a flow velocity sensor and a pressure sensor are fixed below the controller. The invention provides an airflow sensor and a controller, and when the airflow of a patient drops due to sleep apnea, the airflow sensor and the controller can automatically adjust, reduce the valve, increase the provided pressure and open the airway. When airflow is detected to be stable for a period of time, the valve is expanded, and the pressure is reduced to ensure comfort. The positive end-expiratory pressure device can provide adjustable and large positive end-expiratory pressure, and a treatment mode with high comfort and good compliance is provided for a patient.

A snoring and sleep apnea preventer to prevent snoring and sleep apnea from occurring during a user's sleep comprises a sensor part mounted on a body part of the user and collecting sensing information to determine snoring and sleep apnea symptoms occurring in the user; an operating part mounted in the user's mouth to move the user's mandible in response to the sensing information; and a control part for controlling the operating part based on the sensing information sensed via the sensor part, and controlling the operating part in stages to control snoring and sleep apnea symptoms occurring in the user.

The application discloses an obstructive sleep apnea device and method; the device comprises a mouthpiece carrier, an electrode stimulation module and a control unit, the electrode stimulation module comprises an integrated flexible circuit board transmission link, the transmission link is integrated with a stimulation electrode with a nickel-titanium alloy support strip on the inner side of the mouthpiece carrier, and the stimulation electrode can apply electric stimulation to the genioglossus muscle or hypoglossal nerve; the external control unit is provided with a microcontroller and an acoustic sensor; the method first collects an environmental background signal, processes the signal to generate a signal determination threshold, then collects a human respiratory acoustic vibration signal in real time, processes the signal to generate a respiratory sound energy value, compares the two to determine an abnormal respiratory event; after determining the abnormality, a gradient output electric stimulation is output, and a dynamic feedback mechanism is combined to adjust the stimulation intensity; when the stimulation intensity is zero and there is no abnormal signal, it is determined that the intervention is completed and the standby state is entered; the application can adapt to environmental noise, avoid current mutation discomfort, closed-loop control intervention intensity, and is suitable for sleep apnea auxiliary intervention.

Described herein are systems and methods for characterizing a subject's ejection fraction (EF) status, heart failure status, and / or sleep apnea status. In particular, described herein are systems and methods for characterizing a subject's EF status, heart failure status, and / or sleep apnea status through use of a wearable electrocardiogram (ECG) device having LII and LIII leads, and software configured to incorporate data from the ECG device and assess the subject's EF status, heart failure status, and / or sleep apnea status.

The invention discloses a sleep apnea regulation and control system based on a time interference array electrode, and relates to the technical field of medical instruments. The system comprises an electrical stimulation controller and an array electrode, the array electrode comprises a plurality of electrode points, and the electrode points are configured to be paired in pairs to form a plurality of stimulation channels; the electrical stimulation controller comprises a time parameter obtaining module used for obtaining time parameters, an electrical stimulation parameter obtaining module used for obtaining electrical stimulation parameters and a control module, and the control module is used for generating and regulating electrical stimulation signals of all channels according to the electrical stimulation parameters. According to the time parameter, controlling the output and stop of the electrical stimulation signal of each channel; and the electrical stimulation module is further configured to output the electrical stimulation signals of the channels to the array electrode. The system provided by the invention realizes time interference focusing electrical stimulation and closed-loop control thereof. The problems that in the prior art, percutaneous electrical stimulation is poor in precision, sleep awakening is easily caused, and parameter titration is difficult are solved.

Techniques for automated titration of CPAP device for a subject include receiving multiple ultrasound images representing a cross section of an airway in a neck of the subject at corresponding different times. Multiple different positive pressure values imposed by a device on the airway of the subject are also received at each of the corresponding times. For each of the ultrasound images, a mask of pixels associated with an air-tissue interface is automatically formed, and a value of a statistic of pixels within the mask is automatically determined. A titration pressure for a continuous positive airway pressure (CPAP) device is automatically determined based on the positive pressures and the value of the statistic for each of the ultrasound images. Output data that indicates the titration pressure for the CPAP device is presented on a display device, such as by operating the CPAP device itself at the titration pressure.

A system and method for monitoring sleep apnea in cancer patients undergoing treatment are disclosed. The system includes a device to collect SpO2 signals from a patient over multiple sleep sessions, a gateway to receive and process the SpO2 signals to generate formatted SpO2 data, an apnea monitoring service to determine an apnea measure based on the formatted SpO2 data, and a user service to provide a longitudinal progression of the apnea measure. The method involves collecting SpO2 signals, processing them at a gateway, determining an apnea measure, and providing a longitudinal progression of the apnea measure over multiple sleep sessions. The system and method enable efficient monitoring and analysis of sleep apnea in cancer patients during treatment.

Wearable devices are provided herein including wearable defibrillators, wearable devices for diagnosing symptoms associated with sleep apnea, and wearable devices for diagnosing symptoms associated with heart failure. The wearable external defibrillators can include a plurality of ECG sensing electrodes and a first defibrillator electrode pad and a second defibrillator electrode pad. The ECG sensing electrodes and the defibrillator electrode pads are configured for long term wear. Methods are also provided for using the wearable external defibrillators to analyze cardiac signals of the wearer and to provide an electrical shock if a treatable arrhythmia is detected. Methods are also disclosed for refurbishing wearable defibrillators. Methods of using wearable devices for diagnosing symptoms associated with sleep apnea and for diagnosing symptoms associated with heart failure are also provided.