96 results about "Abnormal breathing" patented technology

The invention discloses a pediatric abnormal breathing detection method and system based on a physiological signal, and relates to the technical field of medical information processing, and the method comprises the steps: collecting a multi-modal physiological signal, employing wavelet decomposition and a soft threshold function to eliminate noise, and outputting a preprocessing signal group in combination with morphological opening and closing operation; calculating a time domain feature, a frequency domain feature and a nonlinear feature of the preprocessed signal group, organizing the features into a three-layer pyramid structure, analyzing time sequence similarity by adopting an attention mechanism, and outputting a fusion feature sequence; and carrying out preliminary anomaly detection on the fused feature sequence, carrying out classification based on an age grouping threshold, extracting context feature vectors of windows before and after an abnormal point, and carrying out secondary discrimination to realize accurate identification and graded early warning of the respiratory anomaly. The respiration monitoring system with high reliability, high adaptability and intelligent learning ability is constructed, and comprehensive technical support is provided for early recognition, real-time early warning and intelligent diagnosis of children respiratory system diseases.

The invention relates to the technical field of health assessment, in particular to a big data-based breathing health assessment system, which comprises a breathing parameter analysis module, an interaction relationship assessment module, a breathing anomaly detection module and a breathing condition assessment module. According to the method, accurate extraction of breathing parameters and evaluation of breathing periodic changes are achieved by analyzing thoracic pressure waveform data, the breathing mode of a user is accurately recognized, physiological parameters are collected, the mutual relation between the physiological parameters and the breathing mode is analyzed, the influence of various physiological factors on breathing health is revealed, the dynamic monitoring capacity on the breathing health is enhanced, and the user experience is improved. According to the method, by combining detection and identification of abnormal breathing parameters, identification and classification of abnormal breathing reasons are achieved, medical professionals are helped to make treatment plans and health management strategies, diagnosis efficiency is improved, the misdiagnosis rate is reduced, and powerful support is provided for management of various chronic diseases by combining continuous trend analysis.

The invention relates to the technical field of pattern recognition, in particular to an AI-based breathing anomaly recognition method and system, which comprises the following steps of: extracting a time sequence analysis trend value, a local amplitude change value and a periodic change value in each group of data based on breathing frequency monitoring data and breathing depth analysis data; and comparing the time sequence trend value with the local amplitude change value to obtain a respiratory rhythm characteristic parameter set, and carrying out classification and partition statistics on the periodic change value to generate a respiratory rhythm preliminary partition result. According to the method, by carefully analyzing the respiratory frequency and depth, extracting the trend value, the local amplitude and the periodic change, deeply analyzing the respiratory rhythm, and accurately capturing the respiratory dynamic state, the cross matching fluctuation amplitude and the trend rate by utilizing a partitioning and classifying method, the comprehensiveness and the recognition precision in respiratory monitoring are improved; the abnormal interval deviation value is associated with the respiratory frequency and depth difference, abnormal points are scientifically marked, and the detection efficiency and accuracy of the respiratory anomaly are effectively improved.

The invention relates to the technical field of intelligent monitoring, and particularly discloses a lung function evaluation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining a universal dynamic threshold range of respiratory rate and respiratory amplitude; continuously collecting breathing data output by the MEMS breathing sensor, and obtaining and updating the moving average value and the moving standard deviation of the breathing frequency and the breathing amplitude in real time so as to calculate the change rate of the breathing frequency and the breathing amplitude in real time; according to the updated moving average value and the moving standard deviation, dynamically adjusting the dynamic threshold range of the breathing frequency and the breathing amplitude; comprehensively considering the adjusted dynamic threshold range and the change rate to carry out breathing abnormity judgment so as to carry out lung function evaluation; according to the method, finally, the adjusted dynamic threshold range and the breathing parameter change rate are comprehensively considered, breathing abnormity judgment is conducted, the lung function evaluation result is generated, breathing abnormity can be recognized more accurately, and the accuracy and reliability of lung function evaluation are improved.

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 child abnormal breathing early warning and remote cooperation system for a family environment, and relates to the technical field of breathing data processing, and the child abnormal breathing early warning and remote cooperation system comprises a data acquisition module, a first data calculation module, a first data judgment module, a second data judgment module, a second data calculation module, a third data judgment module and a data output module. The system sequentially performs primary risk assessment, hidden risk mining and potential decompensation index calculation on the basis of a multi-modal signal processing technology by synchronously acquiring a breathing sound signal, a non-contact thoracoabdominal movement signal and a contact thoracoabdominal movement signal, so that graded early warning is realized, and a remote cooperation mechanism is automatically triggered according to the early warning level. According to the method, the hidden risk in the early stage of respiratory compensation of children can be effectively identified, and advanced early warning and timely intervention of abnormal respiration can be realized in a family environment.

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 provides a sleep breathing condition detection method and system and electronic equipment, and relates to the technical field of human body sign monitoring, and the method comprises the following steps: obtaining a first breathing signal sampled by an optical fiber micro-vibration sensor; performing low-pass filtering processing on the first respiratory signal to obtain a second respiratory signal; envelope analysis and frequency analysis are carried out on the second respiration signal, and a target envelope width sequence and a target respiration dominant frequency sequence are determined; and according to the target envelope width sequence, the target respiration dominant frequency sequence, the reference envelope width baseline and the reference respiration frequency baseline, determining the sleep respiration condition of the user. According to the sleep breathing condition detection method, a large number of electrodes and wires do not need to be pasted on the human body, the use convenience is high, and the sleep comfort of the user is improved. In addition, the envelope width is combined with time-frequency analysis, the accuracy is improved through a multi-dimensional feature cross validation mode, and the accuracy of abnormal breathing event detection is high.

The invention relates to the field of breathing mode anomaly detection, and provides a breathing mode anomaly recognition method and system based on time series data mining, and the method comprises the steps: collecting and preprocessing an original breathing signal, and obtaining breathing time series data; processing the breathing time sequence data by adopting a multi-scale feature extraction network to obtain a multi-scale breathing feature vector; performing sample amplification on the multi-scale respiration feature vector and the marked sample through a generative adversarial network to obtain an amplified training sample set; carrying out model training on the amplified training sample set through a decision making system, and carrying out anomaly detection on a to-be-detected respiration feature vector to obtain an anomaly detection result and an uncertainty evaluation result; and outputting graded early warning information based on an abnormal detection result and uncertainty assessment in combination with clinical risk assessment. According to the method, high-precision detection, dynamic risk assessment and graded early warning of the abnormal breathing mode are realized while the feature extraction process is ensured to be highly matched with the difference of individual breathing cycles.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

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 discloses a sleep apnea detection method and system based on a multi-mode audio signal, and the method comprises the following steps: obtaining a trachea sound and an environment sound of a sleep record, and dividing the trachea sound and the environment sound into audio segments, respectively extracting trachea sound features and environment sound features from the trachea sound and the environment sound of each audio clip, calculating a corresponding trachea sound weight and a corresponding environment sound weight, and carrying out weighted fusion on the trachea sound features and the environment sound features of each audio clip to obtain corresponding multi-mode audio features; and detecting an abnormal breathing event for each multi-mode audio feature by using an enhanced branch pre-device, and evaluating the severity of sleep apnea according to the number of the detected abnormal breathing events. According to the invention, the accuracy and sensitivity of sleep apnea detection are obviously improved.

The invention relates to the field of breath sound monitoring, and discloses a tracheal intubation device and a breath sound monitoring system.The tracheal intubation device comprises a tracheal catheter used for being inserted into an airway of a patient; the first sound detection piece is an optical fiber sound sensor, the first sound detection piece is installed on the tracheal catheter, the first sound detection piece is used for detecting breathing sound wave information in the airway of a patient, and the first sound detection piece is used for being in communication connection with the monitoring host so as to transmit the detected breathing sound wave information to the monitoring host. Therefore, the first sound detection piece is installed on the tracheal catheter, the first sound detection piece can be directly located in the internal environment of the airway of the patient, the accuracy of monitoring breathing sound and sound wave information of the patient through the tracheal intubation device can be improved, and the first sound detection piece transmits the breathing sound and sound wave information to the monitoring host in real time; the medical staff can monitor the breathing condition of the patient in real time through the monitoring host, find out abnormal breathing of the patient in time and carry out treatment.

The invention relates to the technical field of medical instruments, and discloses a children respiratory training instrument with a state prompting function, which optimizes the safety and effect of children respiratory training through multi-dimensional innovation. The dynamic rainbow lighting effect system visually maps the breathing intensity through color gradient, and the training concentration of children is improved; intelligent resistance adjustment is matched with the lung function of the child in a self-adaptive mode, and training intensity and safety are balanced; a multi-mode feedback mechanism is used for recognizing abnormal breathing in real time, triggering acousto-optic warning and resistance adjustment in a grading manner, and pre-warning the sputum retention risk; the data communication module realizes wireless transmission of breathing parameters, and parents can check real-time curves, historical frequencies and risk trends through mobile phones and receive intelligent training prompts; the self-cleaning function simplifies the maintenance process, and the use convenience is remarkably improved in combination with a child-friendly structure and the wireless interaction characteristic. According to the invention, breakthroughs are made in the aspects of safety, data traceability and man-machine interaction, and a scientific and intelligent integrated solution is provided for respiratory rehabilitation of children.

The invention discloses a breathing equipment data processing method which comprises the following steps: synchronously collecting multichannel physiological signals through a built-in sensor of breathing equipment, carrying out noise processing and data standardization, and segmenting according to a time window to form data fragments; extracting breathing amplitude, frequency and morphological variability features of each data fragment, and combining to generate a multi-dimensional dynamic feature vector sequence; constructing a breathing state modeling system based on a hidden Markov model, training model parameters through a Baum-Welch algorithm, and calculating a prospective risk transition score by using a forward algorithm; and establishing an adaptive threshold strategy based on the risk transition score, carrying out structured labeling on the original respiration data, and generating processing data containing a prospective risk early warning label. Technical support can be provided for intelligent upgrading and personalized treatment of respiratory treatment equipment, and the accuracy, predictability and clinical practicability of respiratory anomaly detection are improved.

The invention discloses an intelligent ward risk assessment method and system, a product and a medium. The method comprises the following steps: synchronously monitoring breathing audio and mattress pressure signals of a patient, and identifying discrete action events with remarkable amplitude and duration from pressure data; further judging whether the breathing of the patient is abnormal or not before and after the action events occur, for example, the breathing rhythm is interrupted for a long time or the breathing cycle obviously deviates from a baseline; when the two conditions of the violent action and the abnormal breathing cooperatively occur, an effective cooperative event record is confirmed; according to the amplitude and the duration of the action in the event and the severity of the abnormal breathing, weighted summation is carried out through a preset risk weight, and a single event risk score is calculated; and accumulating all event risk scores in an evaluation period to obtain a comprehensive risk score, comparing the comprehensive risk score with a risk threshold, and outputting a risk level. By implementing the technical scheme provided by the invention, the accuracy of clinical compound action risk early warning of the patient is improved.

The invention provides an AI-based child respiratory system diagnosis assistance method and system, and relates to the technical field of diagnosis assistance, and the method comprises the steps: collecting multiple layers of breathing sound signals through a bionic sensing array, building an airflow track model, carrying out the signal enhancement, and generating an enhanced breathing sound signal. Generating three-dimensional breathing sound propagation data by using the acoustic reconstruction network, evaluating a breathing function in combination with airflow dynamics and acoustic parameters, tracking abnormal breathing sound and generating a characteristic distribution diagram; symptom evolution analysis is carried out through a medical knowledge inference engine, real-time monitoring data is output, accurate auxiliary diagnosis is achieved, and the diagnosis efficiency and accuracy of children respiratory system diseases are improved.

The invention discloses a breathing machine waveform classification and identification method based on improved YOLO target detection, and the method comprises the steps: 1, obtaining a normal breathing machine waveform image and an abnormal breathing machine waveform image as a training sample, and enabling the training sample to represent the corresponding relation between the shapes and parameters of different breathing machine waveforms and the mechanical ventilation states of the different breathing machine waveforms; step 2, performing batch labeling on the obtained training samples, obtaining mechanical ventilation state text information corresponding to the breathing machine waveform image and position information corresponding to the breathing machine waveform in the training samples, and establishing a breathing machine waveform recognition library; 3, introducing an improved preprocessing module, processing a training sample by using an improved YOLO target detection algorithm training model, and carrying out breathing machine waveform image classification and recognition training to obtain a breathing machine waveform image classification and recognition model; and step 4, completing automatic classification and identification of the breathing machine waveform image by using the breathing machine waveform image classification and identification model. According to the method, the waveform of the respirator can be detected online in real time, and the abnormal waveform classification accuracy is high.

The invention discloses an atomization control method and a net type atomizer. The atomization control method comprises the following steps: acquiring breathing data of a user; determining a breathing state of the user and an expiration end time point of each breathing cycle based on the breathing data; the breathing state comprises stable breathing and abnormal breathing; and controlling the operation state of an atomization sheet based on the breathing state and the expiration ending time point. According to the breathing state and the expiration ending time point of the user, the operation state of the atomization piece is accurately controlled, so that the medicine utilization rate is increased, and intelligent and personalized atomization treatment is achieved.

The present disclosure provides a method for assessing cardiorespiratory health. The method comprising: generating mechanical movement data associated with a torso of a subject's body using a plurality of ballistocardiography (BCG) sensors; filtering the mechanical movement data to extract respiratory signal waveforms and heart signal waveforms; decomposing the respiratory signal waveforms into thoracic, diaphragmatic, and abdominal breathing components based on waveform characteristics and anatomical location of the plurality of BCG sensors; analyzing phase differences between the thoracic, diaphragmatic, and abdominal breathing components over time to quantify a degree of synchrony or asynchrony; evaluating relative contributions of thoracic, diaphragmatic, and abdominal breathing components to an overall breathing effort; identifying a breathing abnormality based on the analysed phase differences and the evaluated relative contributions; and detecting a potential cardiorespiratory condition based on the identified breathing abnormality.

The present invention relates to an operating method of a lung monitoring system operated by at least one processor, the operating method comprising the steps of: acquiring a plurality of electrical impedance tomography (EIT) images of the chest of a patient and a plurality of respiratory sound intensity images generated on the basis of respiratory sound signals acquired from the chest of the patient; mapping, on the basis of a plurality of pieces of respiratory interval information acquired by binning a respiratory signal of the patient, an EIT image and a respiratory sound intensity image corresponding to each respiratory interval to each other; and inputting, into a respiratory anomaly prediction model, at least one pair of the EIT image and the respiratory sound intensity image included in each respiratory interval, to acquire a prediction result for respiratory anomalies of the patient.

Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.

The invention provides a radar non-inductive respiration monitoring system and method based on a passive wrinkled airbag, and the system comprises a sleep monitoring pillow and a respiration recognition and abnormity monitoring subsystem, the respiration recognition and abnormity monitoring subsystem comprises a signal monitoring and coupling module which processes original monitoring data, and transmits the processed data to the sleep monitoring pillow; a human body respiration direct measurement signal and an air bag coupling amplification signal are obtained; the state recognition and inversion module is used for carrying out in-place state recognition, sleeping posture recognition and airbag state inversion on the coupled double-domain fusion respiration candidate data to obtain state constraint data; the respiration reconstruction and feature extraction module is used for performing screening and feature extraction on the double-domain fusion respiration candidate data to obtain respiration feature data; and the abnormity identification and confirmation module is used for identifying the apnea candidate event and the abnormal event, obtaining an abnormity judgment result and carrying out graded alarm pushing. According to the invention, through the non-inductive pillow type structure and radar-air bag double-domain fusion perception, breathing abnormity intelligent monitoring with high robustness and low false alarm rate is realized.

The present application relates to the technical field of sleep intervention, in particular to an intelligent sleep instrument and a sleep instrument control method, the system comprising a respiratory mutation recognition module, a slow wave initiation linkage module, a pharyngeal cavity contraction evaluation module, an intervention section positioning module and a delay trigger control module. In the present application, the extreme points densely appearing in the continuous cycle are accurately positioned as the key nodes of respiratory abnormalities through the identification of the mutation of nasal respiratory flow, and the slow wave initiation stage closely related to the respiratory mutation is screened out and marked as the linkage period in combination with the power curve change trend of the electroencephalogram signal. The pharyngeal cavity state is dynamically evaluated through the change of acoustic echo in the period, the intelligent postponement and delay processing of the intervention behavior are realized through the comparison of the trigger point of the intervention action and the boundary of the platform section, the accuracy of the sleep intervention timeliness and the adaptability of the intervention execution are improved, and the respiratory event control effect and the sleep continuity are significantly improved.

The invention discloses a breathing difficulty degree quantitative evaluation method and system based on artificial intelligence and a medium. The method comprises the steps that breathing sound signals, basic information and medical history information of a wearer are obtained; extracting time domain features and frequency domain features in the breath sound signals based on a preset machine learning model, and analyzing and evaluating in combination with the basic information and the medical history information to obtain a current breath condition level; according to the historical breathing data and the current breathing condition grade of the wearer, whether abnormal breathing exists or not is evaluated; if yes, the current breathing condition grade is corrected according to the motion information of the wearer in the period corresponding to the breathing sound signals, and the current dyspnea grade of the wearer is obtained. The breathing sound signals are collected in real time and analyzed and evaluated by the preset machine learning model, and the historical breathing data and the motion information are combined for analysis and correction, so that the accuracy of the evaluated dyspnea grade is improved, and the breathing condition of a wearer in daily life is accurately monitored in real time from multiple dimensions.

The application discloses a mesh nebulizer. The nebulization control method of the mesh nebulizer comprises the following steps: acquiring breathing data of a user; determining a breathing state of the user and an end time point of exhalation of each breathing cycle based on the breathing data; the breathing state comprises stable breathing and abnormal breathing; and controlling a running state of a nebulization sheet based on the breathing state and the end time point of exhalation. According to the breathing state of the user and the end time point of exhalation, the running state of the nebulization sheet is accurately controlled, so that the drug utilization rate is improved, and intelligent and personalized nebulization treatment is realized.

The invention provides a sleep detection model training method and a sleep detection method.The training method comprises the steps that according to the segment signal-to-noise ratios of a plurality of initial respiration signal segments and a plurality of corresponding heart beat signal segments in the sleep period and a preset signal-to-noise ratio threshold value, the heart beat signal segments are selected from the initial respiration signal segments, acquiring a plurality of respiratory signal training fragments with the fragment type of abnormal respiration and the fragment type of normal respiration, and then taking the fragment types, the first signal fluctuation characteristics and the second signal fluctuation characteristics corresponding to the plurality of respiratory signal training fragments as sleep respiratory abnormality fragment training samples; and training a plurality of machine learning models according to the sleep breathing anomaly fragment training samples of the plurality of object individuals so as to obtain a plurality of sleep detection models for detecting sleep breathing anomaly fragments. The proportion of the fragment types corresponding to the training samples is balanced, and the accuracy of the sleep detection model obtained through training can be improved.

The invention discloses a sleep quality monitoring and abnormal breathing early warning system based on a generative adversarial network, and particularly relates to the technical field of intelligent health monitoring. The system comprises a signal acquisition module, a data processing module, a generative adversarial network model library and an early warning module, the signal acquisition module is used for acquiring a breathing vibration signal, a body movement signal, a blood oxygen signal, a photoelectric volume pulse wave signal and a wrist body movement signal in parallel through a non-contact mattress piezoelectric sensor and an intelligent bracelet; the data processing module generates comparison data through a first processing method and a second processing method, and outputs a comprehensive feature vector after integration processing; a discriminator of the generative adversarial network model library adopts a multi-task learning architecture and outputs an abnormal index; and the early warning module realizes third-level early warning according to the abnormal index. Through multi-mode signal fusion processing and a non-contact monitoring mode, the accuracy, the reliability and the user experience of sleep breathing abnormity monitoring are effectively improved.

The invention relates to the field of medical equipment, in particular to a collector for acquiring breathing information of a patient, which comprises a collection patch body and an auxiliary observation mechanism, the auxiliary observation mechanism comprises an upper fixing disc, a connecting column, a lower fixing disc, a plurality of equipment bottom fixing assemblies, a display height adjusting assembly, an equipment rear side adsorption assembly, a display frame, a display screen, a display fixing mechanism, a display protection mechanism, a single-chip microcomputer and a memory, and an alarm is fixedly connected to the outer side of the display frame. The diagnosis accuracy is improved; the real-time monitoring and alarming device can inform medical staff at the first time when the breathing of the patient is abnormal, so that the medical staff can take corresponding treatment measures in time, and the life danger caused by the abnormal breathing of the patient is effectively reduced; operation is convenient, and data are visually presented; and through the arrangement of multiple fixing modes of the auxiliary observation mechanism, the diversity of display screen fixing and the use safety of the display screen are improved.

The invention provides a bite block capable of monitoring the breathing state in real time, relates to the technical field of medical monitoring equipment, and solves the problem that an existing bite block cannot accurately monitor abnormal breathing in real time and cannot give an early warning in time. According to the bite block, a bite block body extending forwards from the lower end face of an occlusal surface is integrated with a dual-mode combined sensor, namely a pressure sensor and a temperature sensor, pressure and temperature changes of respiratory airflow are detected, signals are converted into electric signals through a signal conversion module, data fusion and analysis are carried out through a signal processing module, and respiratory mode waveforms are generated; the feedback system judges the breathing state according to the waveform characteristics and triggers the alarm device when no breathing exists or breathing is abnormal. By means of the multi-mode sensing and intelligent analysis technology, dynamic capture of respiration parameters and instant response of abnormal events are achieved, the system is suitable for the scenes of digestive endoscopy, sleep apnea syndrome monitoring, postoperative respiration monitoring and the like, and the real-time performance and reliability of medical monitoring are improved.