43 results about "Respiratory pattern" patented technology

The invention discloses a respiration monitoring system based on a flexible strain sensor and a monitoring analysis method, and belongs to the technical field of sensors and respiration monitoring equipment. The respiration monitoring system comprises a flexible strain sensor, a connecting line, a data acquisition card and an upper computer; the flexible strain sensor is a band-aid type flexible strain sensor and can be directly adhered to the skin surface of a human body, and the deformation of muscles related to respiration is monitored and converted into the change of a resistance signal. And the flexible strain sensor is connected with the data acquisition card through a connecting line. And the data acquisition card is connected with the upper computer and is used for transmitting the acquired resistance signals to the upper computer. The quantitative breathing mode judgment standard is provided through dynamic measurement of the respiratory muscle group, and the defect that judgment is mainly conducted through naked eyes of doctors for a long time can be overcome; the relation between the respiratory muscle group deformation and the lung function parameters is associated, and the problem that the actual respiration condition of part of patients does not conform to the detection condition in conventional lung function detection can be solved.

The invention relates to the technical field of intelligent medical monitoring, and discloses a breathing abnormity identification method, system and device and a medium, and the method comprises the steps: obtaining a multi-modal original data set; extracting a spectrum feature of the audio signal, a periodic feature of the physiological motion signal and a change feature of the environmental monitoring data from the multi-modal original data set, and constructing a multi-dimensional feature matrix based on the extracted features; performing cross-modal fusion processing on the multi-dimensional feature matrix by adopting a multi-channel convolutional network to generate a joint feature vector; and in combination with a pre-constructed knowledge graph, identifying an abnormal feature mode from the joint feature vector through a density clustering algorithm, screening an abnormal mode in combination with a medical diagnosis rule, and outputting a breathing abnormality identification result. According to the method, the defects that a single signal is sensitive to noise and a non-stationary signal is insufficient in analysis capability are overcome; meanwhile, a medical knowledge graph and a density clustering algorithm are combined, non-pathological breathing modes are screened out, and the accuracy and clinical credibility of complex breathing anomaly recognition are remarkably improved.

The invention discloses a breath anomaly detection method and system based on gas monitoring, and relates to the technical field of breath anomaly detection.The breath anomaly detection method comprises the steps that mouth and nose gas signals in the breath process of a user are collected and preprocessed, and gas component separation is conducted through a microfluidic gas separation module; and performing differential analysis on separated gas components, calculating fluctuation of nasal cavity and oral cavity gas components, performing breathing mode recognition through 1D SNN, and establishing a personalized breathing mode library in combination with long-term health data of a user. Compared with the prior art, the method has the advantages that more accurate breathing mode recognition and anomaly detection are realized by combining differential analysis of multi-dimensional gas components and a deep learning model with a personalized breathing mode library, so that a more intelligent health management scheme is provided, and particularly, the method has remarkable advantages in coping with complex personalized differences and long-term health trends.

The invention relates to the technical field of neonatal respiration monitoring, and discloses a neonatal abnormal respiration early warning system. A physiological parameter acquisition module of the system acquires physiological parameter information of a target newborn in real time, and an analysis instruction is triggered after a physiological parameter abnormal signal is obtained; the breathing mode recognition module recognizes breathing mode characteristics and analyzes the breathing mode characteristics to obtain a breathing mode evaluation result; the abnormal feature extraction module extracts abnormal breathing features and analyzes the abnormal breathing features to obtain an abnormal feature evaluation value; the early warning grade evaluation module receives the result and the evaluation value, and obtains a low-risk early warning signal and a high-risk early warning signal through evaluation; the early warning signal generation module selects an early warning type according to the received signal, and obtains parameters of sound early warning and acousto-optic early warning which should be triggered. According to the system, dynamic monitoring and graded early warning of the neonatal breathing abnormity are achieved through multi-module cooperation, and support is provided for timely intervention of the neonatal breathing abnormity.

The invention relates to the technical field of remote monitoring, in particular to a chronic respiratory disease patient remote monitoring system based on the Internet of Things, which comprises a physiological baseline initialization module, an event response delay measurement and calculation module, a cardiopulmonary coupling phase quantization module and a decompensation risk early warning module. According to the method, an individual blood oxygen and cardiopulmonary coupling health baseline is established to provide accurate reference for subsequent analysis, then the blood oxygen recovery time after apnea is measured and calculated, the physiological response delay characteristic of an individual is quantified, and the characteristic is used for setting a dynamic analysis window to continuously calculate the phase difference between a breathing mode and a blood oxygen signal, so that the phase difference between the breathing mode and the blood oxygen signal is calculated. By continuously comparing the current phase difference with the individual health baseline and triggering early warning when the difference value continuously exceeds a specific range, the early sign of the instability of the cardiopulmonary system can be effectively captured, the conversion from passive response type alarm to active predictive early warning is realized, and a precious time window is provided for preventing decompensation.

The invention discloses a respiration monitoring method based on ribs, a surgical robot and related products. The method comprises the following steps: acquiring at least one first two-dimensional ultrasonic image, wherein an image in the at least one first two-dimensional ultrasonic image comprises a first object; and determining an area corresponding to the rib of the first object from the at least one first two-dimensional ultrasonic image to obtain at least one first rib area. Based on the at least one first rib region, at least one second rib region is obtained, and the at least one second rib region corresponds to a first rib of the first object. And on the basis of the position of the at least one second rib region in the at least one first two-dimensional ultrasonic image and at least one acquisition moment when the at least one first two-dimensional ultrasonic image is acquired, acquiring the respiration law of the first object. The breathing rule of the first object is obtained based on the method, the accuracy of the breathing rule can be improved, and then the accuracy of monitoring breathing of the first object can be improved.

The invention provides a breathing mode classification method and system based on a CNN-LSTM neural network, and the method comprises the steps: collecting body surface point cloud data in a non-shielding state and an arm shielding state in a human body breathing process, and extracting a breathing motion feature on the basis of extracting the breathing motion feature; the invention provides an optimization method for abnormal respiratory movement characteristics of a significant respiratory movement area, solves the interference of environmental noise and newborn clinical characteristics on respiratory signals, and comprises the following steps of: firstly, partitioning a thoracic and abdominal voxel model, and extracting the respiratory movement characteristics of the significant area based on KPCA (Kernel Principal Component Analysis); motion artifacts are removed through a Savitzky-Golay filter, body motion interference is inhibited by using a method of fusing a peak threshold method and a local variance threshold, and baseline drift is removed by using a grey wolf optimization algorithm. According to the method, a CNN-LSTM neural network is constructed to classify four breathing modes of normal, rapid, slow and pause, model parameters and evaluation indexes are determined, effective classification and recognition of the breathing modes are achieved, and the monitoring precision and clinical application value are improved.

The invention relates to the technical field of medical instruments, in particular to a self-adaptive respirator parameter optimization system and method based on artificial intelligence, and the self-adaptive respirator parameter optimization system is constructed by combining real-time collection of a high-precision sensor, depth time sequence modeling and intelligent prediction based on TimesNet at a cloud end and closed-loop self-adaptive parameter adjustment. Specifically, one-dimensional breathing signals are reconstructed into multi-cycle 2D representation at the cloud end, multi-scale feature extraction is carried out, short-term and medium-term breathing parameter changes can be predicted more accurately, and predicted values serve as feed-forward input to work cooperatively with traditional PID closed-loop control. Therefore, smooth and real-time adjustment of key parameters such as airway pressure, tidal volume, respiratory rate, humidifier, fan and the like is realized. According to the method, the adaptive capacity and parameter adjustment precision of individual breathing modes of different patients are remarkably improved, and manual intervention and error risks are reduced.

The utility model relates to the technical field of asthma treatment instruments, and discloses an asthma inhaler training detection device, which comprises an asthma inhaler, a test board and a dosage measuring structure, the dosage measuring structure is arranged on the test board, the asthma inhaler is placed on the dosage measuring structure, and the dosage of medicine in the asthma inhaler is measured through the dosage measuring structure; the respiration detection structure is arranged on the test board, and after the trainee uses the asthma inhaler, respiration is detected through the respiration detection structure; the controller is fixed on the test board and is used for recording and analyzing a detection result; and the storage structure is arranged below the test board. Compared with the prior art, the device has the advantages that the breathing frequency, rhythm and strength of a trainee can be analyzed and compared, so that the breathing mode of the trainee after using the asthma inhaler is effectively trained and improved, and the efficiency of using the asthma inhaler by the trainee is improved.

[Problem] In a breath-synchronized breathing gas supply device that supplies breathing gas in accordance with a breathing cycle, a detection error of the start of an inspiration phase caused by the influence of erroneous detection due to the continuous non-detection of inspiration, external disturbance, or the like is reduced. [Solution] The present invention is provided with a pressure sensor and a control unit which determines, as an inspiration detection point, a point at which the value of pressure data calculated on the basis of a signal from the pressure sensor becomes less than an inspiration determination threshold value, and starts the supply of breathing gas. The present invention quickly determines the occurrence of non-detection and erroneous detection of inspiration on the basis of changes in the inspiration detection cycle, and automatically optimizes the determination conditions for inspiration detection corresponding to the current breathing pattern of the user.

The present application provides a method and apparatus for switching oxygen supply mode, a device, and a storage medium. The method comprises: detecting a first respiratory pattern of a user when the turn-on of an intelligent mode is detected; determining a first oxygen supply mode according to the first respiratory pattern, and supplying oxygen according to the first oxygen supply mode; and detecting a second respiratory pattern of a user in the first oxygen supply mode, and determining whether to switch the first oxygen supply mode to the second oxygen supply mode according to the second respiratory pattern. Supplying oxygen according to the first oxygen supply mode comprises: determining a target operating level when the first oxygen supply mode is a pulse oxygen supply mode, and operating at the target operating level. The method achieves automatic switching between two oxygen supply modes according to user requirements and the automatic determination of the target operating level suitable for the user in the pulse oxygen supply mode, thus meeting the oxygen inhalation requirements of a user in different scenarios and improving the effect of use. The method can further augment the oxygen utilization rate, prolong the service life of the oxygen production device, and improve the cost-efficiency.

The utility model belongs to the technical field of breathing device, especially a double breathing device, including breathing mask, be equipped with fixed buckle on the breathing mask, be equipped with the exhalation valve fixedly on the breathing mask, the one end intercommunication of breathing mask is fixed with transparent cover, the one end of transparent cover is connected with first hose, one end of first hose is connected with double -end tube, the bottom of double -end tube is equipped with connecting plate, the other end intercommunication of double -end tube has second hose. The utility model, through fixed buckle and elastic band connection, ensure that breathing mask wears stable and firm, connecting plate is convenient for oxygen delivery pipe connection, guarantees oxygen supply stability, breathing nozzle flat and has fender, fits oral cavity, prevents falling off, provides comfortable and safe breathing mode, flow channel inclination design promotes gas flow and distribution, prevents liquid backflow, improves the whole breathing auxiliary effect, improves patient comfort and safety, provides more optimal choice for medical breathing support.

This invention relates to the field of image enhancement technology, specifically to a real-time intelligent image monitoring system for tubeless thoracic surgery. The invention obtains the structural distortion degree at each moment based on the gray-level difference of the optimal local region and the difference in edge pixel position distribution of each thoracic structure at adjacent moments within each respiratory cycle. It obtains the respiratory abnormality degree of each signal segment based on the stability of the respiratory pattern and the signal fluctuation of the signal segment. Based on the correlation between structural distortion degree and respiratory abnormality degree, the smoothing process during edge detection of thoracic images during the surgical period is optimized to obtain optimized edge images. This invention dynamically adjusts the filtering intensity of thoracic images during edge detection based on the correlation between respiratory pattern and dynamic changes in thoracic structure, which helps edge detection to more clearly capture subtle changes in thoracic structures and improves the accuracy of image monitoring in thoracic surgery.

The utility model discloses a wearable device for assisting in adjusting breathing frequency, which comprises an upper piece of cloth, a lower piece of cloth, a first elastic piece and a second elastic piece, wherein the bottom of the upper piece of cloth is connected with the lower piece of cloth; the adjusting assemblies are arranged on the upper piece of cloth and the lower piece of cloth and used for assisting in adjusting the breathing frequency; the wearing assembly is connected with the upper piece of cloth and the lower piece of cloth and is used for wearing; the adjusting assembly comprises a base; the first air pump is fixedly connected to one side of the upper piece of cloth, and the output end of the first air pump is fixedly connected with a first distributed air pipe; the first air bag is fixedly connected to one side of the upper piece of cloth, and the first air bag is in through connection with the first distributed air pipe. According to the breathing training device, by arranging the adjusting assembly, contraction and inflation of the first air bag and the second air bag can be controlled, different positions of the chest and the abdomen can be extruded, a user is assisted in breathing training, and the breathing mode and the breathing frequency of the user are adjusted.

The invention discloses a child medicine atomization guiding device which comprises an atomizer body, and an atomization catheter is arranged on the atomizer body. A guide assembly is arranged at one end of the atomization guide pipe and used for adjusting the atomization mode. According to the child medicine atomization guiding device, independent control over the mouth control mechanism and the nose control mechanism is achieved through the control box, atomization ways can be flexibly selected according to specific requirements of children, and in the mouth control mode, the children only need to hold the guide-in pipe and naturally breathe to complete the atomization process; in the nasal control mode, the mist enters the nasal cavity through the shunt tube, so that the problem of poor quality of different breathing modes caused by low adaptability of children is avoided, the comfort and compliance of treatment are remarkably improved, accurate administration can be realized no matter in mouth breathing or nose breathing, effective components of the medicine are ensured to directly reach a target part, and the treatment effect is improved. And the treatment effect is improved.

The present application belongs to the technical field of respiratory monitoring, and particularly relates to a respiratory-related myoelectric signal online extraction method, system and storage medium. The system comprises: an input module configured to input a surface myoelectric signal in real time; an electrocardiogram artifact suppression and envelope extraction module configured to input the surface myoelectric signal into a CNN-LSTM hybrid model, remove ECG noise, and extract an EMG envelope; and a respiratory motion quantitative analysis module configured to input the EMG envelope into a prediction model to obtain a quantitative respiratory motion index. The system and method of the present application can extract respiratory signals from sEMG in real time and non-invasively, are superior to conventional methods in terms of respiratory EMG signal processing, can track dynamic respiratory patterns with reasonable accuracy, and have high application potential in real-time prediction interventions such as ventilator triggering, respiratory monitoring and tumor motion tracking radiotherapy.

The invention discloses a respiratory movement simulation experiment device and an application method thereof, and aims to provide a reliable experiment platform for the fields of medical research, equipment test, respiratory disease research and the like by accurately simulating the respiratory rate, tidal volume and thoracic cavity movement of a human body. The device comprises a water bag / hot-water bag, a bionic tissue model, a stepping motor, a push rod, a high-capacity injector, an in-vitro mark point, bionic skin, a binocular camera system and the like. The stepping motor drives the push rod to move in a reciprocating mode, the breathing frequency is simulated, the inflation volume of the hot-water bag is controlled through the injector, and the tidal volume of the human body is simulated. The bionic tissue model simulates the movement of the thoracic cavity, and the movement of the in-vitro mark point is captured in real time through the binocular camera so as to dynamically reflect the movement change of the thoracic cavity and the abdominal cavity. The device can accurately control and monitor the human body breathing mode in real time, is suitable for various applications such as medical experiments, medical equipment verification and respiratory disease diagnosis, and has wide research and application values.

An IoT-based respiratory measuring and training system according to one embodiment of the present invention may include an IoT-based respiratory measuring device including a respiratory measuring module for measuring a user's respiration and an electromyography measuring module for measuring electromyography of a user's body part; and a user terminal for analyzing data on the measured respiration and electromyography to determine and output the user's respiratory pattern and breathing pattern.

The application relates to the technical field of respiratory signals, in particular to a wearable human-computer interaction system and method based on a respiratory signal and application thereof, the system comprising a respiratory signal acquisition module, a signal processing module and an instruction generation module; the respiratory signal acquisition module comprises a head-mounted double-channel respiratory detection device provided with a mouth-nose double-channel and a high-sensitivity humidity sensor of silk; the respiratory signal acquisition module respectively collects respiratory data of a mouth and a nose of a user, and converts gas humidity in the respiratory data into a capacitance signal; the signal processing module comprises a respiratory signal denoising unit, a capacitance-digital conversion chip and a Bluetooth sending chip; the instruction generation module comprises a feature extraction unit, a respiratory pattern recognition unit and a control instruction generation unit. The human-computer interaction system provided by the application is suitable for the fields of medical health, life assistance of physiological defect groups, entertainment games, intelligent wearable devices and the like, and has the advantages of convenience, universality, high precision and expansibility and the like.

This invention discloses a real-time intelligent respiratory signal monitoring method and system based on physiological signals, belonging to the field of intelligent medical monitoring technology. The method includes: acquiring the user's raw multimodal physiological signals; generating a high signal-to-noise ratio respiratory-related signal stream; extracting respiratory waveform segments and calculating multidimensional respiratory feature parameters; performing respiratory pattern recognition and classification on the multidimensional respiratory feature parameters; generating a real-time respiratory health score and risk level; generating a graded early warning report and providing respiratory training guidance suggestions; and optimizing the monitoring and training closed loop through an adaptive learning engine. In this invention, by fusing multimodal physiological signals and using graph neural network pattern recognition, combined with personalized Bayesian correction and adaptive optimization mechanisms, real-time, accurate, and personalized intelligent monitoring and intervention of respiratory status is achieved, solving the problems of traditional respiratory monitoring methods such as single signal, weak anti-interference ability, and lack of personalized assessment and dynamic adjustment capabilities.

The present invention relates to the technical field of image enhancement, and particularly relates to a method for removing artifacts from upper respiratory tract images. The present invention selects an optimal analysis region from the local regions of upper respiratory tract images in the respiratory cycle, and obtains the edge detail loss degree according to the length, the concentration degree of the direction distribution, and the richness of details of the edge lines in the corresponding optimal analysis region of the local region adjacent to each optimal analysis region, as well as the tightness of the edge lines in each optimal analysis region. Then, in combination with the richness of details of the optimal analysis region and the complexity of the respiratory pattern in the respiratory cycle, a fusion adaptation factor is obtained; the filtering intensity for filtering the optimal analysis region is adjusted by using the fusion adaptation factor to obtain an artifact correction region, and these regions are fused to obtain an enhanced upper respiratory tract image. The present invention performs targeted filtering adjustment on the optimal analysis region, enhancing the effect of removing respiratory artifacts in the artifact correction region.

The invention discloses a biological feedback equipment data processing method based on artificial intelligence, relates to the technical field of biological feedback data processing, and realizes accurate decoupling of respiratory rhythm and autonomic nerve regulation signals in a nonlinear feature space through a submerged space orthogonal decomposition mechanism. The problem of HRV low-frequency-band information loss caused by traditional frequency domain filtering is avoided; the respiration component estimation based on the variational auto-encoder does not depend on an external sensor, the hardware cost and the user burden are reduced, and meanwhile, the stability of long-term analysis is guaranteed by self-adaption to individual respiration mode difference and frequency drift through a dynamic orthogonal constraint and online fine adjustment mechanism.

The utility model discloses a bronchus blocking catheter capable of preventing displacement blocking, and belongs to the field of medical equipment. A bronchus plugging catheter capable of preventing displacement and plugging comprises a main catheter and further comprises a plugging unit arranged on the main catheter and used for plugging a main trachea and one bronchus of a patient; the anti-biting piece is arranged on the main catheter and used for reducing deformation of the main catheter when a patient bites; the adjusting unit is arranged on the main catheter and the plugging unit and used for adjusting the breathing mode of the other bronchus; the utility model can solve the problems that the catheter is easy to displace to cause blockage, the comfort of a patient is poorer, and the lung ventilation of a healthy side is influenced.

Method for detecting and localizing living beings in a vehicle interior, wherein at least one camera (1) and one radio sensor (2) each detect at least one section of the vehicle interior, sensor data generated by the camera (1) and the radio sensor (2) are processed by a computing unit (3), and the issuing of a warning and / or the control of a vehicle function depending on a living being detected in the vehicle interior can be initiated by the computing unit (3), wherein: - the computing unit (3) examines the sensor data generated by the radio sensor (2) for the presence of a breathing pattern and / or a heartbeat, locates a breathing pattern and / or heartbeat in the vehicle interior if one is detected, and marks the respective location as a region of interest (4.1); - the computing unit (3) in the sensor data generated by the camera (1) for the respective region of interest (4.1) determines correlating areas (5) and examines these areas (5) for the presence of an adult, a child, and an animal using image recognition methods; and- issues a warning and / or activates the vehicle function if at least one child and / or animal but no adult is detected in the vehicle interior, and suppresses the issuing of the warning and / or the activation of the vehicle function if at least one adult is detected in the vehicle interior, characterized in that- the computing unit (3) marks an installation position of the radio sensor (2) as a further region of interest (4.2) and deactivates the radio sensor (2) if the computing unit (3), by processing the sensor data generated by the camera (1), detects an object or living being in the further region of interest (4.2).

The utility model discloses a wearable respiratory training device, which comprises two groups of bandages for respectively fixing the chest and the belly of a user; the driving assembly is used for driving the bandages to deform or displace so as to adjust the tightness of the bandages, so that the two groups of bandages can apply pressure to the chest and the abdomen of a user respectively; the control assembly is provided with a pressure sensor and a processor; the pressure sensor is arranged on the inner side of the bandage so as to obtain the pressure between chest and abdomen expansion and the bandage when a user breathes, a data signal is fed back to the processor, and the processor alternately drives the bandage acting on the chest and the abdomen of the user to deform according to a preset pressure value. The device is used for assisting a user in completing conversion between an expiration state and an inspiration state so as to carry out precise training of thoracico-abdominal respiration, dynamically correct a respiration mode and improve the training effect and the training efficiency.

The invention discloses a respiration type determination method, system and device during anesthesia, a medium and a product, and belongs to the technical field of medical data processing. The method comprises the following steps: acquiring initial breath sound data of a target object in a general anesthesia state under a current time window, wherein the initial breath sound data is acquired by a breath sound acquisition device arranged in a first preset area of the chest of the target object or a sound sensor arranged in a second preset area of the back of the target object; performing noise reduction processing on the initial breath sound data to obtain target breath sound data, and determining a statistical distribution feature combination corresponding to the target breath sound data, the statistical distribution feature combination comprising statistical distribution feature data under at least two signal domains; inputting the statistical distribution feature combination into a predetermined diagnosis model to obtain an initial diagnosis result; and displaying at least one breath sound type identifier in the initial diagnosis result. According to the technical scheme, the breathing sound type identifier of the target object can be displayed in real time.

The application provides a respiratory motion measuring device, which comprises a plurality of marker points, a marker point position change capturing unit and a respiratory motion calculating unit; the plurality of marker points are arranged on the body surface of a subject; the marker point position change capturing unit is used for capturing the position changes of the plurality of marker points during the breathing of the subject; and the respiratory motion calculating unit is used for calculating the respiratory motion condition of the subject according to the position changes of the plurality of marker points during the breathing of the subject. Through the respiratory motion measuring device and the measuring method, the subject can realize low-intrusion respiratory motion measurement without wearing circuit sensors and power sources on the body; and the measurement of the respiratory motion can be further subjected to quantitative analysis and respiratory pattern analysis.

The present invention belongs to the field of respiratory monitoring technology and discloses a non-contact respiratory detection method based on rapid alignment of CSI dynamic components. First, wireless signal data transmitted by a WiFi transmitter is collected in an indoor environment, and CSI raw data is extracted from the WiFi signal. Second, the raw CSI data is preprocessed to obtain a CSI ratio signal, remove static components, and perform noise reduction. Then, the covariance matrix of the preprocessed data is calculated, and the main direction is obtained using the eigenvalue decomposition method. The angle between the main direction and the positive direction of the I axis is then calculated. Next, the angle required to rotate the CSI ratio signal to align it is calculated based on the polarity of the sum of the CSI ratio signal samples. Finally, all CSI ratio signals are combined to enhance the dynamic component and thus extract the respiratory pattern. The present invention solves the problem of being unable to detect respiratory signals due to the distance between the target and the WiFi device, thereby improving the perception range of respiratory detection using WiFi signals.

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.

The present invention relates to the technical field of respiratory monitoring, and specifically relates to a method and system for identifying critical respiratory states in early childhood. The present invention first obtains historical respiratory signals and segments them to obtain respiratory segments; further, according to the differences in respiratory amplitude and signal components between the respiratory segments, the respiratory patterns are divided; further, the respiratory frequency and respiratory amplitude range of each respiratory pattern are obtained, and the normal respiratory pattern is screened out; further, the real-time respiratory signal of the current patient is obtained and segmented in real time; according to the difference characteristics between the current respiratory segment and the normal respiratory pattern at the current moment, combined with the respiratory pattern of the current moment and the respiratory patterns of adjacent respiratory segments, and the difference in the number of respiratory segments, the respiratory abnormality coefficient is obtained; finally, based on the respiratory abnormality coefficient, it is determined whether to issue an early warning, reducing the influence of modal mixing, reducing the influence of physiological differences among different age groups or individuals, and improving the accuracy of respiratory state identification.