34 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 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.

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 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.

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 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.

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

The invention relates to the technical field of medical and health care equipment, in particular to a mouth-inhalation and nose-exhalation separated inhalation device and method capable of improving the hydrogen utilization rate, and the device comprises a hydrogen generator, a first one-way valve, a buffer storage air bag, a second one-way valve and a mixed inhalation module which are sequentially connected through a gas pipe. And the mixed suction module is provided with an air inlet with a third one-way valve. When a user inhales in the mouth through an air outlet suction nozzle of the oral mixed inhalation module, a hydrogen and air channel is opened at the same time, and mixed gas is inhaled; and when the hydrogen is converted into nasal expiration, the second one-way valve and the third one-way valve are closed due to negative pressure, and the hydrogen is temporarily stored in the buffer storage air bag and is not wasted. Through the synergistic effect of the mouth-inhalation and nose-exhalation separation breathing mode and the slow storage air bag, the problems that a traditional hydrogen absorption device is low in hydrogen utilization rate, dead space exists, carbon dioxide is repeatedly inhaled, and the structure is complex are fundamentally solved, and the device has the advantages of being high in utilization rate, simple and reliable in structure and good in user experience.

The invention relates to the technical field of wearable medical equipment, in particular to an intelligent respiration monitoring device and method based on multi-modal sensing and posture compensation. A thermistor array, a pressure differential sensor and an inertial measurement unit are used for synchronously collecting multi-source signals, a coupling analysis model of breathing signals and human body motion characteristics is constructed, an LSTM network is used for achieving time sequence modeling of a breathing mode, abnormal breathing detection under motion interference is carried out in combination with an isolated forest algorithm, and the detection accuracy is improved. The technical limitations that single-mode sensing is easily interfered by the environment and the false alarm rate of a complex action scene is high in a traditional scheme are broken through. Through multi-modal data fusion and edge computing architecture, real-time abnormal early warning is realized while respiratory rate detection precision is ensured, and a closed-loop processing link from signal acquisition and dynamic compensation to intelligent decision is formed.

The present application relates to the field of respiratory mode anomaly detection, and proposes a respiratory mode anomaly recognition method and system based on time series data mining, which comprises: collecting original respiratory signals and preprocessing to obtain respiratory time series data; using a multi-scale feature extraction network to process the respiratory time series data to obtain a multi-scale respiratory feature vector; using a generative adversarial network to perform sample augmentation on the multi-scale respiratory feature vector and labeled samples to obtain an augmented training sample set; using a decision system to train a model on the augmented training sample set and perform anomaly detection on the respiratory feature vector to be detected to obtain an anomaly detection result and an uncertainty evaluation result; and based on the anomaly detection result and the uncertainty evaluation, combining clinical risk assessment to output graded early warning information. The present application ensures that the feature extraction process is highly matched with the individual respiratory cycle difference, and realizes high-precision detection, dynamic risk assessment and graded early warning of respiratory mode anomalies.

The present application relates to the technical field of radio waves, and particularly relates to a human respiration recognition method based on a millimeter wave body area network, which comprises the following steps: adopting the movement state of a chest cavity and an abdominal cavity of a human body during various respiration movements; tagging the collected movement state to obtain a training data set; constructing a neural network model and training the neural network model by using the training data set; collecting the movement state of the chest cavity and the abdominal cavity during a to-be-recognized respiration movement and inputting the movement state into the trained neural network model, so that the neural network model recognizes the type of respiration; the present application fully utilizes the advantage of fixing a millimeter wave antenna on a body, does not rely on the reflection of electromagnetic waves to collect respiration data, and the collected respiration data will not change with the change of the environment, and meanwhile, the present application can recognize various different forms of respiration modes.

This invention relates to the field of medical artificial intelligence-assisted diagnostic technology, and in particular to an auxiliary diagnostic system for the prodromal phase of tic disorders in children. Addressing the problem that existing technologies cannot identify the prodromal impulse phase preceding tic movements, this invention, for the first time, extracts facial microtremor features and respiratory stress features from ordinary videos. Using the predicted time of the tic movement as an anchor point, it fuses these two types of features through a gated attention mechanism to generate a prodromal impulse index. The auxiliary diagnostic output module uses this index to determine the prodromal phase, output confidence levels and audiovisual warning signals, and generate a diagnostic report containing the frequency of prodromal events and assessment suggestions. This invention achieves automatic identification of the prodromal impulse phase through cross-modal collaboration of facial microtremors and respiratory patterns, significantly improving the early warning capability of tic disorders and providing an objective time window for clinical behavioral intervention.