14 results about "Breathing Effort" patented technology

The invention belongs to the technical field of lung rehabilitation training, and particularly relates to a lung rehabilitation training effect evaluation system and method based on multi-modal data fusion. According to the method, a dynamic reference model conforming to individual differences is established through three-dimensional pulmonary respiration mobility modeling based on patient CT images, individualized comparative analysis of lung function recovery states of different patients is achieved, and meanwhile external respiration dynamics and internal physiological state parameters are collected and fused; rehabilitation quality is evaluated from the two aspects of exercise performance and physiological response, scientificity and reliability of evaluation are remarkably improved, a patient can visually perceive difference between own breathing actions and an ideal state through AR visual correction prompts, active correction of actions and muscle memory formation are promoted, effectiveness of rehabilitation training is improved, and the rehabilitation training effect is improved. The training difficulty and the resistance threshold value are dynamically adjusted according to the historical evaluation result and the real-time correction effect, it is guaranteed that rehabilitation training is gradually strengthened within the safe and reasonable range, and the rehabilitation efficiency is improved.

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 invention belongs to the technical field of data processing and machine dynamic learning, and provides a postoperative pulmonary complication early warning system and method.According to the technical scheme, a classic respiratory system model is converted into a respiratory dynamics model, and based on the respiratory dynamics model, a pulmonary complication early warning system is established; the non-stationary data change is processed by introducing forgetting factor processing in recursive least square, and the parameter vector of the system is dynamically updated; acquiring a respiration signal sampling point, performing noise reduction on the acquired respiration signal sampling point to obtain a respiration signal after noise reduction, and performing reconstruction based on the respiration signal after noise reduction to obtain a reconstructed respiration track; carrying out dynamic learning on the reconstructed breathing trajectory to carry out modeling, and carrying out visual display on a breathing dynamics result obtained after modeling to generate a breathing dynamics diagram; and obtaining a corresponding early warning strategy according to the abnormal characteristics of the respiratory dynamics diagram. And early-stage accurate early warning of postoperative pulmonary complications can be realized.

The invention relates to a breathing physical therapy device, belongs to the technical field of medical rehabilitation equipment, and solves the problem that existing breathing training equipment cannot intelligently adjust resistance according to the real-time breathing effort degree of a user. The device comprises a mouth-nose mask, an airflow resistance adjusting valve, a diaphragm myoelectricity sensor and a pressure difference sensor, a control module monitors the pressure difference rising rate and the current myoelectricity integral value in real time in the treatment stage based on a myoelectricity integral reference value calculated in the calibration stage, and accordingly resistance adjusting instructions including resistance increasing, resistance reducing and emergency resistance reducing instructions are generated; and an instruction priority and execution logic are set, so that timely intervention is ensured when the breathing of the user is abnormal. The device is mainly used for respiratory muscle strength training and rehabilitation treatment of patients with chronic respiratory diseases, and the training safety and the individuation level can be effectively improved.

The application discloses a method for obtaining an autogenous breathing effort curve under mechanical ventilation based on a breathing machine waveform, which can calculate resistance and compliance in a patient's respiratory system through airway pressure and flow rate waveforms of the breathing machine when the breathing machine ventilates the patient, and then calculate the autogenous breathing effort of the patient during each breath according to the resistance and compliance and the pressure and flow rate waveforms. For each different patient, the method can calculate the change of pressure and resistance with time and the inspiratory effort intensity curve of each breath. The content can help doctors better judge the condition of the patient, thereby better assisting clinical treatment.

The present application belongs to the technical field of holographic breath analysis, and discloses a non-contact respiratory mechanics reverse reconstruction system based on 4D holographic field perception, which comprises the following steps: receiving the echo sequence of a millimeter wave sparse aperture array to the chest and abdominal area, generating a 4D point cloud sequence of the time-varying shape of the chest and abdominal surface according to frames, and constructing a respiratory observation sequence of the chest and abdominal surface; by analyzing the correlation between the length change attribute of the geodesic line and the energy change attribute of the curvature tensor evolution sequence, a respiratory driving feature is generated; according to the time change of the chest and abdominal area in the respiratory driving feature, an observation confidence is obtained; the respiratory driving feature is input into a reverse reconstruction network containing a nonlinear viscoelastic respiratory dynamics constraint, and a mechanical parameter set of airway resistance and pleural cavity pressure change is output. The present application converts 4D morphological observation into structured boundary excitation, and uses observation confidence to weight the constraint inversion process, enhances the complete expression of inhalation and exhalation driving, and improves the stability and reliability of the mechanical parameter output.

The invention belongs to the technical field of holographic respiration analysis, and discloses a non-contact respiratory mechanics reverse reconstruction system based on 4D holographic field perception. Comprising the following steps: receiving an echo sequence of a millimeter wave sparse aperture array to a thoracico-abdominal region, generating a 4D point cloud sequence of a time-varying form of the thoracico-abdominal surface according to frames, and constructing a respiration observation sequence of the thoracico-abdominal surface; by analyzing the relevance between the geodesic length change attribute and the curvature tensor evolution sequence energy change attribute, respiration driving characteristics are generated; obtaining an observation confidence coefficient according to the time change of the thoracic and abdominal region in the respiration driving characteristics; and inputting the respiration driving characteristics into a reverse reconstruction network containing nonlinear viscoelastic respiration dynamic constraints, and outputting a mechanical parameter set of airway resistance and pleural cavity pressure change. According to the method, 4D morphological observation is converted into structured boundary excitation, and the inversion process is weighted and constrained by observation confidence, so that the inspiration driving expression integrity is enhanced, and the stability and credibility of mechanical parameter output are improved.

The application provides a pediatric respiratory tract cleaning method and system based on multi-mode vibration and physiological feedback, and belongs to the technical field of pediatric respiratory medical equipment and intelligent diagnosis and treatment. The method comprises the following steps: collecting multi-modal input data, generating physiological characteristic signals through a CNN+LSTM fusion model, and determining initial adaptive conditions; combining medical images and respiratory dynamics parameters to construct an individualized respiratory tract simulation model and perform vibration simulation; collecting real-time physiological parameters and performing noise reduction processing to obtain feedback data; comparing the preset threshold to adjust the vibration parameters, and performing actual cleaning after reaching the standard. The application realizes accurate adaptation of vibration parameters through individualized simulation and physiological feedback closed loop, improves cleaning safety and efficiency, reduces the risk of airway damage, and adapts to the physiological characteristics of young children.

The invention belongs to the technical field of breathing machines, and relates to a respiratory effort evaluation system based on a respiratory flow autopower spectrum. The system comprises a signal acquisition module used for acquiring a continuous breathing flow signal acquired by a flow sensor of the breathing machine in real time; the self-power spectrum calculation module is used for carrying out preprocessing and time-frequency conversion on the respiratory flow signals and calculating to obtain a self-power spectrum; the extraction module is used for extracting energy of a preset high-frequency band as a high-frequency energy component in the autopower spectrum, and the high-frequency band is higher than a main frequency band corresponding to the basic respiratory frequency of the human body; the statistical analysis module is used for establishing a baseline value for evaluation based on statistical data of high-frequency energy components in the historical respiratory flow data; the evaluation output module is used for comparing the high-frequency energy component at the current moment with the baseline value, when the high-frequency energy component is smaller than the baseline value, the respiratory effort state is evaluated to be abnormal, and otherwise, the respiratory effort state is normal.

This invention belongs to the field of medical testing equipment technology, specifically an intelligent anti-loosening chest breathing effort testing device with a magnetic quick-change probe. The proposed solution includes: a magnetic suction mechanism, one end of which is fitted with a sleeve tube. This mechanism enables quick magnetic attachment and disassembly of the probe to the device body, and forms an intelligent anti-loosening seal through the magnetic attraction and elastic structure, preventing the probe from falling off and leakage of the patient's exhaled air during testing; and a testing mechanism, with both ends fixedly mounted on the other ends of the two magnetic suction mechanisms. This invention utilizes a Tesla valve structure composed of a right side plate, a left side plate, and a mounting plate to create an asymmetric resistance airflow channel. During inhalation, the airflow passes through quickly; during exhalation, the airflow is guided by a triangular diverter and flows along the surfaces of the right and left side plates, creating a counter-impact. This counter-impact significantly increases exhalation resistance, ensuring the required airflow power for testing while greatly reducing the respiratory burden on the tester.

The invention provides a pulmonary nodule real-time positioning system based on patient respiratory fluctuation modeling. The system comprises a multi-source sensing unit, a signal processing module, a respiratory dynamics modeling module, a regionalization sensitivity field module, a disturbance observer, a self-healing registration module, a fusion filtering module and an output and interaction module. A dynamic model with a hysteretic operator is established through multi-source signals such as thoracico-abdominal movement, laryngeal acoustics and vibration, end tidal carbon dioxide, respiratory flow, body position drifting and instrument states, millimeter-level positioning of pulmonary nodules along with respiration is achieved in combination with regionalized sensitivity mapping, errors caused by respiratory movement, local disturbance and body position drifting are effectively solved, and the positioning accuracy of the pulmonary nodules is improved. And the safety and effectiveness of clinical operation are obviously improved.

This invention provides a method, system, and device for detecting and assisting in the regulation of sleep breathing trends. It identifies and extracts sleep breathing dynamics trend signals, and achieves a scientific quantitative evaluation of sleep breathing trend behavior through sleep breathing dynamics trend characteristics, sleep breathing trend intensity, and sleep breathing trend intensity curves. By predicting and analyzing the user's sleep state and sleep breathing trend behavior, it generates sleep breathing trend-assisted regulation strategies and sends them to the sleep breathing regulation device, dynamically optimizing and improving the efficiency and effectiveness of the device. Finally, through the quantification of sleep breathing trend detection and dynamic strategy generation, it achieves personalized, precise, and dynamic assistance in regulating the user's sleep breathing trend, thereby improving the user's sleep quality.

This invention provides a method, system, and device for the joint detection, evaluation, and auxiliary regulation of sleep-related brain and respiratory functions. It collects, detects, and dynamically analyzes sleep-related respiratory behavior signals, extracts sleep-related respiratory dynamic signals, and identifies sleep-related respiratory events through event detection and analysis. Furthermore, it combines quantitative characteristics of sleep-related respiratory and brain states with sleep phase segmentation to extract event intensity and event level, achieving scientific quantification and joint evaluation of sleep-related breathing. By predicting the development trends of respiratory dynamics, respiratory state, and brain state, it generates sleep-related brain state auxiliary regulation strategies in real time and sends them to sleep regulation intervention devices via a signal interface, thereby achieving dynamic auxiliary regulation of the user's sleep-related brain state. This invention enables scientific detection, evaluation, and efficient dynamic auxiliary regulation of sleep-related breathing.

The invention discloses a dynamic movement safety navigation system and method for a chronic obstructive pulmonary disease patient based on multi-physiological parameter fusion, and belongs to the technical field of intelligent medical treatment and rehabilitation engineering. The system comprises a wearable monitoring terminal, a processing unit and an active navigation and intervention terminal. The wearable monitoring terminal collects kinematics parameters of a patient and physiological parameters at least including blood oxygen saturation and respiratory dynamics in real time; the processing unit is internally provided with a dynamic safety threshold model and is used for dynamically calculating and updating a personalized safety exercise intensity interval of the patient at the current moment according to the real-time physiological parameter flow; and the active navigation and intervention terminal is used for receiving the safety interval and converting the safety interval into a real-time guide instruction, so that the exercise intensity of the user is maintained in the interval, and active intervention is started when a physiological risk is predicted. According to the method, the conversion from a static motion prescription to dynamic individualized navigation is realized, the efficiency and compliance of lung rehabilitation training are improved on the premise of ensuring safety through multi-parameter fusion and prospective prediction, and the method has remarkable clinical practical value.