21 results about "Nasal airflow" patented technology

An example computer system analyzes nasal airflow data to detect sleep disorders. The system can estimate the breathing cycle frequency on the pressure nasal airflow data. Based on this frequency, the system can calculate optimal smoothing window sizes. The system can perform a smoothing operation on the airflow data and auxiliary sensor data using these windows. Breathing cycles can be detected in the smoothed data streams. Cycles meeting artifact conditions but not sigh conditions can be removed as noise. Remaining clean cycles can be analyzed further. A transition segment between the peak of one cycle and trough of the next can be classified. Using this classification and the cleaned auxiliary data, the system can detect sleep disorder events.

The utility model discloses a sleep mouth and nose breathing monitoring device. Relates to the technical field of medical instruments, and comprises a fixing substrate, a mouth-nose airflow monitoring assembly used for monitoring mouth-nose respiration when a patient sleeps is mounted on the fixing substrate, and elastic bandages used for fixing through the head or neck of the patient are mounted at the two ends of the fixing substrate; a signal transmission interface used for being connected with external respiration monitoring equipment is formed in the bottom of the fixed base plate; the flexible sensor in the nasal cavity monitoring port or the oral cavity monitoring port is used for monitoring airflow generated during breathing of the nasal cavity or the oral cavity of a patient, and breathing airflow data of the patient is transmitted to external breathing monitoring equipment for monitoring through the wire. The mouth-nose airflow monitoring assembly can be installed on the fixed substrate in a plug and jack matching connection mode. The utility model has the advantages of high monitoring sensitivity, high monitoring accuracy, good stability, good comfort and strong flexibility.

The invention provides a household nose and mouth breathing detection device which comprises a framework, sensors and a controller, the framework comprises a nose clip part, a connecting part and a ventilation part, and the sensors comprise a nose airflow sensor and a total airflow sensor. Wherein the nose clip part comprises a nose clip main body, and a first side wing and a second side wing which are respectively positioned on two sides of the nose clip main body, and the side wings are provided with nose airflow sensors; two ends of the connecting part are respectively connected to the nose clip part and the ventilation part; the ventilation part is provided with a total ventilation hole and a total airflow sensor, and the airflow of the mouth and the nose can pass through the total ventilation hole and can be detected by the total airflow sensor; and the controller is respectively connected with the nasal airflow sensor and the total airflow sensor. The device is small, exquisite and portable, operation of professionals is not needed, accurate nose and mouth breathing data can be obtained through simple operation of family users, and therefore whether the users breathe mouth or not can be conveniently and accurately judged.

Described herein is a nasal airflow disruption apparatus, and method of use, comprising an elongate strip of flexible material having a central area extending between two distal ends. The central area comprises a narrow portion being defined by an elongate recess. The apparatus is configured such that when the strip is positioned across the bottom of a nose and the distal ends are secured to either side of the nose, the central area partially covers the nostrils and creates first and second airflow zones in each nostril. The first airflow zone is distal to the tip of the nose, allows unencumbered airflow into an out of the nostril and is created by the elongate recess. The second airflow zone is proximal to the tip of the nose, disrupts airflow into and out of the nostril and is covered by the narrow portion.

The application discloses a nasal airflow simulation analysis method based on a labeled voxel calculation domain, an electronic device and a storage medium. The method comprises the following steps: acquiring a three-dimensional surface model of a nasal cavity, and constructing a voxel calculation grid based on a minimum containing box of the three-dimensional surface model and a preset voxel size; performing voxelization processing and label assignment on the voxel calculation grid to automatically construct an initial labeled voxel calculation domain; performing voxel domain processing and automatic marking of an opening boundary on the initial labeled voxel calculation domain to obtain a simulation labeled voxel calculation domain; automatically determining boundary conditions and numerical simulation parameters based on the simulation labeled voxel calculation domain and a preset breathing condition, establishing a numerical simulation model and iteratively solving the numerical simulation model to generate a nasal airflow simulation result. The application realizes full-process automation from a three-dimensional surface model to a airflow simulation result, significantly reduces manual grid division and boundary setting operations, and improves the efficiency and automation degree of nasal airflow simulation.

To provide an air flow controller in the nasal cavity which enables a subject to perceive an actual smell in the oral cavity during a meal through the posterior nasal cavity route.SOLUTION: The nasal cavity air flow control device 100 for making the subject perceive the odor includes a suction tube 1 inserted from the nostril H of the subject P, and a negative pressure suction means 2 for feeding the air containing the odor in the oral cavity into the nasal cavity N by sucking the inside of the nasal cavity N by negative pressure through the suction tube 1.SELECTED DRAWING: Figure 1

This disclosure relates to a cylinder (50) for an aerosol generating apparatus (1), the aerosol generating apparatus, a method for diverting airflow in the cylinder for the aerosol generating apparatus, and a method for manufacturing the cylinder. The cylinder includes a mouthpiece body (2) having an outer surface (51) and an internal air passage (52). The internal air passage includes an inner surface (53) of the air passage and a mouthpiece opening (54) at an end (55) of the internal air passage. The mouthpiece body includes a plurality of nasal airflow passages (31) extending from the inner surface of the air passage to the outer surface of the mouthpiece body.

The invention provides a neural network model training method, a mouth-nose airflow signal reconstruction method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a plurality of thoracico-abdominal movement signals acquired by a millimeter wave radar for a target object; processing the plurality of thoracico-abdominal movement signals by using a neural network model to obtain a reconstructed mouth-nose airflow signal and an apnea prediction result; calculating a regression error according to the reconstructed mouth-nose airflow signal and the real mouth-nose airflow signal, and calculating an apnea prediction error according to an apnea prediction result and an apnea label; performing weighted calculation according to the regression error and the apnea prediction error to obtain integration loss; and performing back propagation and gradient updating on the neural network model according to the integration loss, and optimizing model parameters. The mouth-nose airflow signal reconstructed by the neural network model is finer, more comprehensive and more accurate.

This invention discloses a non-invasive rapid detection and diagnosis system for upper airway function, belonging to the field of upper airway function detection and medical diagnostic technology. It is applicable to upper airway function assessment in patients with mandibular retrusion and non-invasive diagnosis of adenoid hypertrophy in children. The system consists of a three-tiered architecture comprising a multimodal oral-nasal co-measurement instrument, a data acquisition and transmission module, and an intelligent diagnostic module. It integrates multiple types of sensors to simultaneously collect multimodal signals such as oral and nasal airflow, lip status, and neck airway breath sounds. After standardized processing, it achieves accurate grading and diagnosis of upper airway function and adenoid hypertrophy through hierarchical logic including three-dimensional feature extraction, respiratory pattern discrimination, core + auxiliary indicator grading diagnosis, and machine learning model verification. It is also equipped with multiple supporting functional modules and features non-invasiveness, dynamic operation, portability, and high diagnostic accuracy, filling a gap in related technologies.

An external nasal dilation device for enhancing nasal airflow includes a pair of pads, each of which has adhesive positioned on its lower face. The adhesive is used to removably attach the pads to opposed sides of a nose of a user. Each flap of a pair of flaps is hingedly attached to an upper face of a respective pad so that the flap is hingable relative to the respective pad. A panel is attached to each panel by an adhesive such that the panel extends between the flaps. The panel is resiliently flexible so that the panel extends arcuately over a bridge of the nose. The panel exerts opposing forces to nostrils of the user to expand the nostrils.

A cartridge is provided for an aerosol-generating device, including a mouthpiece body with an outer surface and an inner air channel including an air channel inner surface and a mouthpiece opening at a mouth end of the channel and being an outlet for a first part of a flow of the aerosol accessible to a mouth of a user; a liquid reservoir; and an aerosol generator that is a heater to heat liquid from the reservoir to form an aerosol, the mouthpiece body including nasal airflow passages extending from the inner surface to the outer surface, at least one of the passages is an outlet for a second part of the flow of the aerosol accessible to the user as aroma, and the cartridge being configured to dispense the aroma through the passages driven by a puff of the user and corresponding to a frequency and intensity of the puff.

The utility model discloses a pressure type nasal airflow snore sensor which comprises a shell, a sensor body, a connecting assembly and a nasal oxygen uptake hose, and the sensor body is arranged in the shell and used for detecting the breathing motion state and the snoring state; the connecting assembly is connected with the shell; one end of the nasal oxygen uptake hose is used for being connected with the nasal cavity of the human body, and the other end of the nasal oxygen uptake hose is hermetically connected with the connecting assembly and can guide nasal cavity gas to the detection surface of the sensor body; according to the device, nasal cavity gas is guided into the detection face of the sensor body through the nasal oxygen inhalation hose, the breathing movement waveform and the snore movement waveform are detected through the sensor body, the breathing movement waveform and the snore movement waveform can be detected at the same time, the comfort degree of a wearer in the sleep monitoring process is improved, and the sleep quality of the wearer is improved. And the contact between the sensor main body and wet mouth and nose airflow is reduced, so that the service life of the sensor can be prolonged, and the manufacturing and use costs are reduced.

AI-Powered Smart Nasal Airflow Monitoring Device

This invention provides a method and system for classifying sleep breathing events based on nasal airflow audio. The system includes an audio collection module, a signal processing module, and a classification module, configured to implement the following steps of the method: collecting nasal airflow audio; segmenting the nasal airflow audio to provide audio segments; extracting features from the audio segments; and inputting the features into the classification module to output the breathing event category corresponding to the audio segment.

The application provides an acute respiratory failure detection device based on oral-nasal airflow signals. It is applied to the medical and health field. The operation of the device includes: obtaining the airflow signals of the oral-nasal of the measured person; extracting the time-frequency domain features, time domain features and frequency domain features of the airflow signals respectively; inputting the extracted time-frequency domain features, time domain features and frequency domain features into the pre-trained feature extraction neural network respectively to obtain the deep time-frequency domain features, deep time domain features and deep frequency domain features; performing feature fusion on the deep time-frequency domain features, deep time domain features and deep frequency domain features, and inputting them into the pre-trained classification neural network to obtain the dense classification probability; selecting the disease stage category c of the acute respiratory failure with the maximum probability at each time t in the dense classification probability. The application realizes convenient, safe and accurate detection of acute respiratory failure.

The present invention provides a sleep monitoring device and a respiratory event detection method. The sleep monitoring device includes a memory and a processor, the memory being configured to store program instructions, and the processor being configured to execute the program instructions to perform the respiratory event detection method. The method comprises: extracting the maximum amplitude value and the minimum amplitude value within a preset time period from a nasal airflow respiratory signal and a chest-abdominal movement respiratory signal to be processed, respectively, to obtain four amplitude values; determining whether the four amplitude values ​​meet a first preset condition; if the first preset condition is met, calculating the nasal airflow respiratory cycle amplitude and the chest-abdominal movement respiratory cycle amplitude within the preset time period to obtain a first cycle amplitude and a second cycle amplitude; determining whether the first cycle amplitude and the second cycle amplitude meet a second preset condition; and if the second preset condition is met, determining that the event is a respiratory effort-related micro-arousal event. The present invention improves the accuracy of respiratory event detection while also reducing detection costs.

The application provides a neural network model training method, an oral-nasal airflow signal reconstruction method and equipment. The method is applied to the field of radar signal processing. The method comprises the following steps: acquiring multiple chest and abdominal movement signals collected by a millimeter wave radar on a target object; processing the multiple chest and abdominal movement signals by using a neural network model to obtain a reconstructed oral-nasal airflow signal and a prediction result of apnea; calculating a regression error according to the reconstructed oral-nasal airflow signal and a real oral-nasal airflow signal, and calculating an apnea prediction error according to the prediction result of apnea and an apnea label; performing weighted calculation according to the regression error and the apnea prediction error to obtain an integrated loss; and performing back propagation and gradient updating on the neural network model according to the integrated loss, and optimizing model parameters. The oral-nasal airflow signal reconstructed by the neural network model is more fine, comprehensive and accurate.

The utility model provides a wearable device of an oxygen supply system and a diffuse-type oxygen supply system, which relate to the field of building environment and respiratory pathology and comprise a head band, a flexible bandage, a respiratory area inhaled gas oxygen partial pressure analyzer and a nasal airflow monitoring probe, the rear end of the head band is connected with the flexible bandage, and the respiratory area inhaled gas oxygen partial pressure analyzer is connected with the nasal airflow monitoring probe. The front end of the head band is connected with a breathing area inhaled gas oxygen partial pressure analyzer, and the side edge of the head band is connected with a nose airflow monitoring probe through a branch pipe. According to the utility model, the size of the head band can be adjusted through the flexible bandage, and the applicability is wide; the oxygen partial pressure value of the inhaled gas is measured and calculated through the inhaled gas oxygen partial pressure analyzer, the oxygen supply efficiency and precision are effectively improved, meanwhile, the appearance is matched with the frontal lobe area of the brain, and the comfort degree of a wearer is improved; through cooperation of the inhaled gas oxygen partial pressure analyzer and the wall-mounted diffuse type oxygen supply equipment, central apnea and hypopnea events in the plateau area are improved, the respiratory problem is relieved, and the sleep quality is improved.

The application provides a working memory quantitative prediction method based on respiratory-electroencephalogram coupling characteristics and adaptive causal inference, and belongs to the technical field of biomedical signal processing and artificial intelligence. In order to solve the problems that the prior art cannot capture the nonlinear causal dependence between respiration and electroencephalogram and the deep learning model lacks task-specific design, the application synchronously collects sleep period electroencephalogram, nasal airflow, chest and abdominal respiratory movement and snoring vibration signal; extracts the snoring spectrum centroid frequency and calculates the slow wave suppression time constant through the exclusive index mapping; adopts the directional transfer entropy of the dynamic threshold to perform nonlinear causal inference; deeply embeds the physiological parameters and causal indicators into the coupling architecture composed of the snoring modulation graph convolution, the causal gated Transformer and the snoring perception multilayer perception machine, and designs the dynamic gated multi-task output and the ablation perception loss function. The application can realize quantitative prediction of reaction time, accuracy and comprehensive cognitive score, and has the characteristics of high precision and adaptability.

The application provides a household nose-mouth breathing detection device, which comprises a framework, a sensor and a controller, the framework comprises a nose clip part, a connecting part and a ventilation part, and the sensor comprises a nose airflow sensor and a total airflow sensor. The nose clip part comprises a nose clip main body and first and second side wings respectively located on both sides of the nose clip main body, and the side wings are provided with the nose airflow sensor; the connecting part is connected to the nose clip part and the ventilation part at both ends; the ventilation part is provided with a total ventilation hole and the total airflow sensor, and is configured to enable the airflow of the mouth and nose to pass through the total ventilation hole and be detected by the total airflow sensor; and the controller is connected to the nose airflow sensor and the total airflow sensor. The device is small and portable, and does not need to be operated by professionals. Through simple operation of a household user, accurate nose-mouth breathing data can be obtained, so that whether the user is a mouth breather can be conveniently and accurately judged.

The invention discloses a sleep apnea high-precision detection method and system based on dynamic baseline tracking. The sleep apnea high-precision detection method based on dynamic baseline tracking comprises the following steps: acquiring an original nasal airflow signal; preprocessing the original nasal airflow signal to obtain a preprocessed nasal airflow signal; and processing the preprocessed nasal airflow signal to obtain an envelope signal, a dynamic baseline and a residual signal. The method comprises the following steps: carrying out envelope extraction on the preprocessed nasal airflow signal to obtain the envelope signal; calculating the dynamic baseline based on the envelope signal; calculating the residual signal based on the envelope signal and the dynamic baseline; calculating a residual dispersion and a dynamic threshold based on the residual signal; and judging whether a sleep apnea event occurs based on the residual signal, the residual dispersion and the dynamic threshold. Therefore, according to the detection method, the dynamic baseline is constructed, the omission ratio or the false alarm rate can be reduced, and noise interference is reduced.