161 results about "Respiratory status" patented technology

The invention belongs to the technical field of ablation puncture navigation, and particularly relates to an ablation puncture surgery navigation system under AI visual guidance. Through cooperative work of the phase monitoring module, the channel construction module, the needle moving control module, the displacement prediction module and the feedback control module, an intelligent surgical navigation system is constructed, breathing phase information and multi-modal medical image data of a patient can be obtained in real time, and therefore a safe and optimized needle moving path is accurately planned, and the operation accuracy is improved. Meanwhile, through real-time monitoring and quantitative evaluation of parameters such as tissue deformation and pressure distribution in the needle moving process, potential operation risks can be found and corrected in time, smooth operation is ensured, in addition, the system can dynamically adjust the needle moving path and the puncture angle according to the real-time breathing state of a patient, and the operation safety is improved. The accuracy and the safety of the operation are further improved, so that intelligent management and personalized treatment in the ablation puncture operation process are realized.

The invention relates to the technical field of atomization rate control, in particular to an intelligent atomization compliance monitoring method and system for children. The method comprises the following steps: firstly, acquiring expiration flow velocity, inspiration flow velocity and multi-direction motion acceleration time sequence data of a child in a historical monitoring period, and reflecting breathing and behavior action conditions; dynamically analyzing the numerical characteristics of the expiration flow velocity and the inspiration flow velocity to divide a respiratory cycle, and analyzing the cycle length, the inspiration flow velocity cumulant and the like to construct a respiratory characteristic vector; screening a target monitoring period based on the breathing feature vector and the period number similarity; further analyzing the motion acceleration change and accumulation characteristics in the target monitoring period, and quantifying the behavior compliance expression degree; the behavior compliance expression degree and the breathing feature vector are deeply fused, a'breathing state-behavior cooperation 'joint evaluation model is established, the breathing and behavior states of children are dynamically recognized, the medicine atomization rate is accurately adjusted, and effective deposition of lung medicine is improved.

The invention discloses an intelligent nursing body position adjusting method and system based on respiratory function protection, and relates to the technical field of artificial intelligence and medical nursing. The method comprises the steps that body pressure distribution signals and respiratory thoracic impedance signals of a patient are collected in real time, a physiological data sequence is generated through Kalman filtering and wavelet fusion noise reduction, the physiological data sequence is input into a pre-trained deep learning network, and fusion feature vectors and body position performance indexes are output. And if the body position performance index is not lower than a preset threshold value, inputting the fusion feature vector into a pre-trained classification model, and outputting a classification label of the breathing state. A preset parameter optimization mapping table is inquired according to the classification labels, candidate parameter combinations sorted according to priorities are obtained, the parameter group with the highest priority is selected, a multi-motor cooperation instruction is calculated through a feedback control algorithm, and dynamic regulation and control of the bed surface are achieved. The invention aims to realize respiratory function protection and individualized nursing by sensing the respiratory state in real time, intelligently deciding and adjusting parameters, cooperatively executing control and adopting a safety protection mechanism.

The invention relates to the technical field of general control or regulation systems, in particular to a critical patient respirator parameter adaptive optimization control method, which comprises the following steps of: acquiring and processing physiological signals; constructing a dynamic respiration mechanical model based on a sliding time window recursive recognition algorithm; a man-machine coordination index is calculated, and the desynchrony degree is quantified; generating a ventilation parameter adjustment scheme through the reinforcement learning strategy network; and executing smoothing parameter adjustment, and introducing a safety boundary and a secondary fine adjustment mechanism. The breathing state of the critical patient can be accurately perceived and intelligently regulated and controlled, the ventilation efficiency is guaranteed, meanwhile, the man-machine synchronism and safety are improved, the medical care burden is relieved, and the prognosis of the patient is improved.

The invention discloses an external diaphragm pacemaker regulation and control method and device based on a respiratory radar. The method comprises the following steps: acquiring a chest radar echo signal of a target; analyzing the radar echo signal to obtain a target breathing signal; analyzing the target breathing signal to obtain breathing parameters, and generating a synchronizing signal; the treatment parameters are adjusted by adopting the breathing parameters and the synchronizing signals; the treatment parameters are adopted to output control signals; and regulating and controlling the external diaphragm pacemaker based on the control signal. The breathing state of the target is detected and sensed in real time through the breathing radar, key parameters are extracted, and the opportunity and intensity of electrical stimulation are dynamically adjusted based on the key parameters, so that personalized synchronization with the natural breathing rhythm is realized, and the treatment efficiency and the user comfort are improved.

The invention relates to the technical field of atomization drug delivery, in particular to an anti-infection drug precise atomization drug delivery system based on respiratory physiological characteristics, comprising a respiratory parameter acquisition module used for acquiring respiratory data of a patient in real time; the breathing mode recognition module is used for classifying breathing modes of the patient and generating breathing mode labels; the dose-parameter cooperative calculation module is used for adjusting the atomization dose and the initial atomization parameter required by the patient at a time; the atomization parameter optimization module is used for monitoring the actual output effect of an atomizer in real time and adjusting dynamic atomization parameters in real time through a PID controller; and the breathing auxiliary intervention module is used for guiding the patient to perform deep and slow breathing through acousto-optic feedback. By collecting the breathing data of the patient in real time and dynamically adjusting the atomization dosage and parameters in combination with breathing mode classification, the limitation of traditional fixed dosage and parameters is broken through, administration is accurately matched with the real-time breathing state of the patient, and the deposition rate and utilization rate of the medicine at the target part are increased.

The invention provides a snore stopping method and device, and the method comprises the steps: obtaining an original vibration signal of the neck of a user, and carrying out the preprocessing of the original vibration signal, and obtaining a target vibration signal; analyzing the breathing state of the user according to the target vibration signal to obtain a breathing state analysis result; judging whether intervention conditions are met or not according to the respiration state analysis result; when the intervention condition is met, the breathing state of the user is intervened according to the breathing state analysis result and a preset self-adaptive intervention rule. The breathing behavior related to the snoring behavior is detected based on the vibration signal, flexible intervention on the breathing state of the user is achieved in combination with the detection result and the self-adaptive intervention rule, sound collection is not needed, interference of snore of other users can be avoided, user privacy is not involved, meanwhile, smooth intervention experience can be provided for the user, and the user experience is improved. Therefore, the method can be widely applied to the technical field of smart home.

The invention relates to a working parameter adjusting method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring gas pressure and gas flow rate output by a breathing machine and collected by a breathing machine sensor; determining breathing information of the target user according to the gas pressure and the gas flow rate; the breathing information comprises at least one of breathing frequency, real-time tidal volume and breathing quality data; determining physiological state information of the target user according to the breathing information and a physiological state threshold value; and adjusting working parameters of the breathing machine based on the physiological state information. By analyzing the gas pressure and flow velocity data collected by the respirator sensor in real time, the breathing frequency, tidal volume and other key parameters of a user can be accurately determined, the breathing state of a patient is intelligently evaluated, the working parameters of the respirator are automatically adjusted according to the evaluation result, and the flexibility of the working parameters is improved.

The invention discloses a transformer breathing state digital online monitoring system. Comprising a respirator online monitoring module used for collecting respirator working condition parameters, an oil conservator online monitoring module used for collecting oil conservator working condition parameters, a data collection and communication module used for receiving and transmitting the respirator and oil conservator working condition parameters, and a local processing and display module used for data processing and monitoring result displaying. And the remote management platform is used for remotely checking a monitoring result and adjusting parameters in the local processing and display module. According to the invention, comprehensive, real-time and quantitative monitoring can be carried out on the breathing state of the transformer and the internal state of the oil conservator, so that operation and maintenance personnel can intuitively and accurately master the operation state of equipment, regular maintenance is converted into predictive maintenance, the reliability, stability and economical efficiency of the operation of the transformer are remarkably improved, and the service life of the equipment is prolonged.

The utility model discloses an atomizer for providing medicine mist based on respiration detection, and belongs to the field of medical atomizers. The atomizer comprises an atomization working assembly, a breathing mask, a breathing sensor, a control module and a switching valve, the breathing sensor comprises a detection tube, the detection tube is provided with two tube openings at different positions, one tube opening is connected with the breathing mask, the other tube opening is communicated with the atmosphere, and the two tube openings are respectively provided with an airflow detection element. The airflow direction in the detection tube is determined according to the sequence of airflow detected by the two airflow detection elements, whether a patient exhales or inhales is accurately detected, the detection of the breathing state is rapid and accurate, the influence of the humidity and temperature of breathing air is small, and the working stability is good; meanwhile, the control module is used for controlling whether the mist outlet of the atomization working assembly generates medicine mist or not according to the expiration or inspiration state, medicine mist generated by inspiration of the patient and medicine mist generated by stopping expiration of the patient are accurately achieved, medicine waste is reduced, and the atomization treatment effect is guaranteed.

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 relates to the technical field of respiratory health monitoring, and discloses a remote respiratory health monitoring and management system and method. The method comprises the steps that an original respiration data set containing time sequence and frequency domain characteristics is collected through a multi-mode sensor, respiration cycle segments are divided through dynamic segmentation processing, key waveform mark points are extracted, and a respiration state transition network is constructed to form a dynamic topological structure. And generating a breathing environment coupling matrix in combination with environmental parameters, screening interfered abnormal breathing segments, training an analysis model to output a deviation score, triggering multi-level early warning and generating a report containing a backtracking path. User activity log timestamps are matched through cross-device verification, data are integrated to construct a breathing behavior association map, a model judgment threshold value is updated based on the map, and monitoring closed-loop feedback is completed. According to the method, accurate monitoring and dynamic management of respiratory health are achieved, and a complete solution is provided for remote respiratory health management and control.

According to the mode switching method, the controller, the airway clearance equipment, the storage medium and the program product provided by the embodiment of the invention, the oscillation airflow under different oscillation frequencies is output after operation is performed for the preset duration in the airway oscillation mode or the mechanical inhalation and exhalation mode, and the oscillation airflow under different oscillation frequencies is output for any oscillation frequency. The method comprises the following steps: acquiring response information of a user to oscillation airflow at oscillation frequencies, determining an impedance parameter of the user according to the response information at each oscillation frequency, determining an operation mode at the next moment according to the impedance parameter, and executing the operation mode, according to the method, the response information of the user under the oscillation airflow is obtained to determine the impedance parameter representing the blocking condition of the respiratory system of the user, so that the adopted airway clearance mode is automatically adjusted according to the real-time breathing state of the user, and the sputum discharge effect is improved.

The present disclosure provides a system for patient monitoring. The system may cause a medical device to perform a treatment or a scan on a patient who remains in a breath-hold status. During the treatment or the scan of the patient, the system may acquire one or more parameter values relating to one or more status parameters that reflect the breath-hold status of the patient using a monitoring device. The system may also predict a breaking point of the breath-hold status based on the one or more parameter value. The system may further adjust the treatment or the scan based on the breaking point of the breath-hold status.

The application discloses a respiratory motion estimation method, device and equipment and a medium, relates to the technical field of motion estimation analysis, adopts an unsupervised sparse-dense motion estimation framework with sequence motion consistency constraints, and can quickly and accurately realize motion estimation of a liver ultrasound image sequence under the influence of respiratory motion. A sparse-dense coarse-to-fine registration strategy based on sparse point guidance is used to accurately predict the motion field between adjacent respiratory state images, a sparse key point automatic detection guided rigid registration network is designed to automatically detect sparse key points from the images in an unsupervised manner, and a multi-source structured feature guided deformation densification network is constructed to predict the dense elastic deformation component of the motion between the images. In combination with the idea of motion decomposition and coincidence, sequence motion consistency constraints are constructed based on a small motion sequence between a plurality of adjacent respiratory states, the continuity of motion in the time flow is strengthened, and the motion estimation accuracy is further improved.

To more accurately detect a respiratory state of a patient by more accurately detecting pressure fluctuation by the expiration and inspiration of the patient in a respiration detector.SOLUTION: In a respiration detector of the present invention, a gas supply source 1, a nozzle part 6, and a respiration detection part 2 are arranged in series in the order of description in the upstream and downstream directions through a feed passage 7 and a connection passage 8 of a cannula 3. A first throttle part 21 is formed in a main passage part 16 of the connection passage 8 of the cannula 3, and a second throttle part 22 is formed in one branch passage part 17 of two branch passage parts 17 and 18 of the connection passage 8.SELECTED DRAWING: Figure 1

The embodiment of the present application provides a method for determining a body surface marker placement area and related products, which comprises: obtaining sample data of a plurality of patients, extracting two body surface contours of each patient according to two medical image data of each patient in the sample data, calculating a displacement change amount of each pixel point of the body surface of each patient under two different breathing states according to the two body surface contours of each patient and performing clustering processing to obtain L-class clustering results of the body surface movement of each patient; dividing the body surface of the patient into N marker placement areas according to the distribution of the L-class clustering results of the body surface movement of the plurality of patients; mapping the L-class clustering results of the body surface movement of each patient to the N marker placement areas; and obtaining M marker placement areas with larger displacement amounts according to the displacement amounts corresponding to the N marker placement areas. The accuracy of a respiratory motion correlation model established based on the body surface marker movement can be improved.

The invention discloses a breathing state detection method and system based on deep learning and a breathing machine, and the method comprises the steps: carrying out the preliminary simple filtering of airflow and air pressure data, and removing the interference of high-frequency noise on subsequent slices; judging a breathing cycle according to the waveform of the breathing data, and slicing the airflow and air pressure data according to the breathing cycle; converting the data from the time domain to the time-frequency domain, and obtaining the time-frequency domain characteristics of the signal; and inputting the obtained signal time-frequency domain characteristics into a neural network algorithm of each subtask, and identifying various states of the current equipment. According to the breathing machine control method, the accuracy of the breathing machine control method and the use experience of a user are improved by combining a multi-state recognition network of convolution, ResNet and Transform, and the robustness is enhanced through multiple recognition and a voting mechanism.

This invention relates to the field of nursing technology for disabled individuals, and particularly to a method and device for identifying the risk of aspiration in disabled individuals. The method includes: establishing a multimodal, non-invasive data acquisition system to simultaneously collect temporal information on laryngeal vibration, respiratory status, and facial movements; preprocessing to extract effective signals; constructing an individual-specific physiological baseline model using historical normal physiological samples and binding it to the individual's identity; segmenting and processing real-time signals to construct a three-dimensional feature collaboration matrix, calculating mutual information entropy, and dynamically iterating the associated baseline; temporally aligning swallowing and respiratory abnormality anchor points, marking key intervals, and screening suspected risk events; and outputting the final aspiration risk identification result after environmental interference filtering, feature weight adaptation, and verification. This invention enables accurate, real-time identification and early warning of aspiration risk without requiring any wearable devices and without affecting the daily care and rest of disabled individuals.

The application discloses a method and system for electrocardiosignal pattern recognition for sleep respiratory state discrimination, and relates to the technical field of apnea prediction, and comprises the following steps: acquiring a chest electrode stress state and constructing a dynamic baseline, mapping the electrocardiosignal based on the dynamic baseline to distinguish dynamic noise segments, implementing rhythm rearrangement based on the mapping result to obtain a corrected electrocardiosignal, introducing a body position evolution direction correlation constraint in combination with contact impedance changes, forming an electrocardio expression result under the constraint of a body position stable window, and reconstructing a risk trigger path based on the result to realize continuous rhythm risk judgment. The application realizes effective recognition of electrocardiosignal patterns under the interference of body position changes by establishing a correlation between electrode stress and contact impedance changes, and improves the analysis stability and physiological state discrimination accuracy of electrocardiosignals in a complex sleep environment.

Disclosed is a minimally-invasive sensing device for measuring or estimating respiratory biomarkers. The device contacts a target body fluid detects for at least one target analyte and outputs a signal representative of a concentration of the analyte, from which biomarker information is determined. The present disclosure also relates to methods and systems using measurements or estimations of respiratory biomarkers to assess patient respiratory state, and / or assist in providing respiratory therapy to a patient.

The present application belongs to the technical field of sleep respiratory disease detection, and is a sleep apnea identification method based on a hybrid neural network. The method uses a millimeter wave radar to perceive human sleep in a surrounding bed environment, adopts spatial two-dimensional analysis to locate the chest cavity position, and solves the interference problem in radar sign extraction of different targets at the same distance. Shannon entropy of the respiratory signal is constructed as an iteration index of the whale optimization algorithm, and the minimum index is used to realize iteration optimization of the variational mode extraction parameter, so that different breathing states are matched with different parameters to accurately extract the main frequency narrowband respiratory signal. The respiratory time-frequency energy graph obtained through data processing is used as input, and a CNN network and a bidirectional long short-term memory module are used to jointly construct a feature extraction module, so that the application can identify the frequency change trend over time, capture more comprehensive features, and accurately identify target sleep apnea. In addition, the hybrid network can accurately classify unmanned scenes, apnea, low ventilation and normal breathing.

The utility model discloses an external breathing assembly for controlling inhaled air according to a breathing state. The external breathing assembly comprises a detection control unit and a breathing unit, the detection control unit comprises a breathing sensor, a switching valve and a control module, and the breathing sensor is used for detecting the expiration or inspiration state in the breathing unit; the switching valve is connected with the breathing unit, the switching valve is further provided with an air supply connector used for being externally connected with an air supply unit, and the control module is in communication connection with the breathing sensor and the switching valve and used for controlling the switching valve to act according to the expiration or inspiration state detected by the breathing sensor; by means of the external breathing assembly, the effects of providing gas during inspiration and stopping providing gas during expiration are achieved, various gas inspiration systems can control gas inspiration according to the breathing state, universal applicability is achieved, waste of effective gas is reduced, and the device is particularly suitable for existing atomizers, oxygen inhalation devices and the like.

The invention relates to a multi-dimensional vital sign signal reconstruction method based on multi-antenna WiFi signals. The method comprises the following steps: acquiring x groups of original vital sign CSI signals; de-noising processing is carried out and the signals are decomposed into respiratory signals and heartbeat signals; segmenting into a plurality of motion segment signals; performing mathematical representation on feature data in each motion fragment signal; enhanced signal processing is carried out, and then a feature matrix C corresponding to each motion fragment signal is constructed and formed; differential calculation is carried out, and then a respiration sequence signal and a heartbeat sequence signal are obtained; clustering calculation is carried out, and the motion category corresponding to each motion fragment signal is obtained through reverse deduction based on the clustering index labels. According to the method, weak motion interference can be recognized with high precision, stable and reliable feature support is provided for fine evaluation of the breathing state and recognition of the abnormal state, and therefore the stability of breathing state analysis in different dynamic scenes is further improved.

The invention belongs to the technical field of medical apparatus and instrument control, particularly relates to an atomization control system for respiratory nursing, and aims to solve the problems of low medicine deposition efficiency, serious waste and incapability of precise medicine administration caused by the lack of the capability of sensing and responding to the real-time respiratory state of a patient in a traditional atomization device. The system comprises a breathing state real-time monitoring module, an aerosol generation and regulation module, an intelligent control center, a man-machine interaction interface and a cloud data collaboration platform. Breathing parameters are collected through a high-sensitivity sensor, the particle size and the output opportunity of fog drops are dynamically adjusted in combination with a dual-mode ultrasonic atomization technology, and accurate inspiration phase dosing is achieved; a self-adaptive learning engine is arranged in the intelligent control center, the breathing cycle can be predicted, atomization can be started in advance, and the response speed is increased; local closed-loop control and remote personalized management are supported, and the medicine utilization rate and treatment compliance are remarkably improved.

This invention discloses an AI-assisted diagnostic system for CT rib fractures based on image recognition, belonging to the field of smart healthcare technology. It includes recognizing the patient's contour using thermal imaging data, constructing a respiratory model to analyze respiratory status, and automatically triggering CT image acquisition of the ribs at the optimal inhalation moment. Subsequently, artifact detection is performed; if qualified, the system is handed over to AI for intelligent fracture diagnosis, achieving a fully automated process from image acquisition to diagnosis. This invention uses non-contact thermal imaging to monitor respiration and body movement, constructing a respiratory model that integrates chest contour and airflow temperature changes, accurately identifying the optimal inhalation moment to trigger CT acquisition, reducing motion artifacts at the source and improving image quality. The system is highly robust, automatically determining the optimal acquisition time even under conditions of clothing or weak breathing, reducing reliance on patient cooperation, and is particularly suitable for rib fracture patients, improving examination success rate and efficiency.

According to the atomization device for the breathing machine, by introducing the structural design that a driving controller, a pressure sensor, a two-way connecting pipe, a three-way connecting pipe and an atomization cup work cooperatively, on-demand atomization control based on the breathing state of a patient is achieved; the core problems of medicine waste, environmental pollution, poor use experience and the like existing in a traditional continuous atomization mode are effectively solved. According to the invention, invalid aerosol generated in a non-inspiration period in a traditional'continuous atomization 'mode is avoided, and especially for high-value drugs (such as biological agents and inhalable antibiotics), the actual inhalation dose proportion can be obviously improved, the single treatment cost is reduced, and the treatment economy and the curative effect reliability are improved.

The application relates to the technical field of biomedical engineering, and particularly discloses a closed-loop OSA stimulation control method and device based on multi-modal physiological signal fusion, which comprises a multi-modal physiological signal acquisition module, a control processing module, a stimulation output module and a feedback correction module; by fusing related physiological signals such as respiratory muscle electricity, blood oxygen saturation and snore sound acoustic spectrum characteristics, the blocking risk in the sleep process is evaluated in real time, and the corresponding stimulation time, stimulation channel and stimulation parameter are determined in combination with the breathing phase information and the feedback result after stimulation, so that the precise, real-time and individualized closed-loop control for OSA intervention is realized under the premise of meeting the preset safety constraint. The application can more comprehensively reflect the respiratory state and the upper airway blocking risk in the acquired data, improve the accuracy of OSA related event identification, and determine the stimulation triggering time according to the current respiratory state, thereby improving the matching degree between the stimulation intervention and the actual respiratory demand.

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 discloses a non-contact breathing state monitoring method based on a millimeter wave radar, and belongs to the technical field of non-contact human body vital sign monitoring. The method comprises the steps that radar echo signals of a human body target are collected through a millimeter wave radar, a phase sequence is obtained through frequency mixing, filtering and phase demodulation, and breathing signals are extracted through phase unwrapping and displacement conversion; fourier transform is carried out on the respiratory signals, and the respiratory frequency and amplitude are calculated; a plurality of slope extraction algorithms are adopted, derivation is conducted on the respiration signals in the inspiration and expiration stages, and the respiration slope is obtained; and finally, integrating the respiratory rate, the amplitude and the slope, setting a threshold value and a scoring system, and quickly and accurately evaluating the respiratory distress degree. The method does not need to contact a human body, can penetrate through clothes, is suitable for various scenes such as clinical monitoring and emergency triage, and has the advantages of being easy and convenient to operate, high in real-time performance and high in reliability.