136 results about "Pain assessment" patented technology

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention discloses a patient pain prediction method and device based on wearable equipment. The method comprises the steps that historical physiological data and activity data of a patient are collected through the wearable equipment; acquiring a pain assessment report of the patient through an electronic medical record system, wherein the pain assessment report comprises a pain intensity score, the frequency and duration of pain attack and a treatment and intervention scheme; establishing a pain multi-modal evaluation model according to the physiological data and the pain evaluation report; wherein the pain multi-modal evaluation model comprises a learning identification module, a 1-all strategy module, a time sequence prediction module and a multi-modal fusion evaluation module; inputting the current physiological data into the pain multi-mode evaluation model, and predicting the pain intensity and the pain mode of the patient. Through the model comprising rule learning, the 1-all strategy, time sequence prediction and multi-mode fusion evaluation, the prediction precision of the pain intensity and the pain mode of the patient is improved, and the diagnosis and treatment effect of the patient, especially the chronic pain patient, can be remarkably improved.

The invention relates to the technical field of pain assessment, in particular to a paediatric pain assessment system which comprises a muscle group rhythm recognition module, a behavior linkage screening module, a periodic fluctuation extraction module, a behavior physiological difference module and a pain grading judgment module. According to the method, a linkage offset identification path is constructed through an angle relation between facial muscle groups and a starting time sequence, multi-muscle cooperative imbalance performance is identified, a region-level dynamic behavior judgment basis is formed, and a behavior cooperative judgment mechanism is established by utilizing response overlapping conditions of multiple behavior characteristics in the same time period; path signals which do not form effective cooperation are eliminated, the effectiveness of behavior characteristics is enhanced, a continuous trend structure of peak time in rhythm change is introduced, a periodic response sudden change monitoring channel is constructed, the recognition sensitivity of a short-time fluctuation concentration section is improved, and a response mismatch section is extracted based on a trigger time dislocation state between behavior and physiological response, so that the recognition sensitivity of the short-time fluctuation concentration section is improved. And a pain judgment key point is positioned in the time sequence overlapping region.

The invention discloses a whole-process patient self-control analgesia management platform and method based on artificial intelligence, and relates to the related field of analgesia management, and the platform comprises a multi-source data interaction module which is used for building a time sequence standard data set; the information configuration module is used for starting the self-control analgesia pump; the AI pain evaluation module is used for generating a pain scoring result; and the identification early warning module is used for generating graded early warning. The technical problems of insufficient safety and unstable effect in the analgesia management process due to dependence on a fixed dose threshold and lagged artificial pain assessment in traditional patient self-control analgesia management are solved, the whole-process pain management platform based on artificial intelligence is constructed, multi-source data can be analyzed in real time, and the pain management efficiency is improved. And the pain state of the patient is dynamically evaluated, and the parameters of the analgesia pump are automatically adjusted, so that the technical effects of personalized analgesia management and safety and effectiveness in the analgesia process are realized.

The invention relates to a system for realizing pain scoring of an ICU (intensive care unit) patient. The system comprises a data acquisition module for acquiring a facial micro-expression video of the ICU patient in real time by using a high-definition camera; the micro-expression recognition module analyzes the collected micro-expression video image; the vital sign analysis module analyzes the collected vital sign data in real time; the pain scoring model is obtained by training through a machine learning algorithm, and the pain degree of the patient is quantitatively scored; and the result output module outputs the pain scoring result to the medical staff in real time. According to the system and method for achieving ICU patient pain scoring, the processor and the computer readable storage medium of the system and method, the objectivity and accuracy of pain scoring are improved through the artificial intelligence technology. By analyzing the combination of micro-expressions and vital signs, the sensitivity and specificity of pain assessment are improved. Real-time monitoring and continuous evaluation of pain scores are realized, and timely adjustment of a treatment scheme is facilitated.

The invention discloses an interpretable multi-modal information system for postoperative chronic pain assessment, which is characterized by comprising a facial expression algorithm framework for acquiring dynamic change of a facial expression facial action unit (AU) and a physiological signal algorithm module for receiving physiological signals, and the multi-modal fusion framework is used for fusing the facial expression algorithm framework and the physiological signal algorithm module. The method has the effects of accuracy, individuation and interpretability.

The invention provides a pain assessment system and method based on multi-modal physiological signals. The system comprises a multi-modal signal acquisition module, a signal preprocessing module, a multi-modal feature extraction module, a deep fusion analysis module, an individualized calibration module and a result output and early warning module. By synchronously collecting and analyzing multi-dimensional data such as facial expressions, sound features, physiological signs and behavior responses and combining deep learning and multi-modal information fusion technologies, objective quantitative evaluation and real-time monitoring of the pain degree are achieved, and accurate decision support is provided for clinical pain management.

The invention discloses a personalized child postoperative pain management method and system, and relates to the technical field of pain management. A personalized child postoperative pain management system comprises a data acquisition module, a development evaluation module, a sample management module, a pain evaluation module, a scheme matching module and a feedback adjustment module. According to the invention, by combining objective assessment and subjective assessment, comprehensive acquisition and integration of multi-dimensional data are realized, and the accuracy of pain assessment is greatly improved; through psychological development and physiological development stage evaluation, weighted analysis is carried out on patients, it is ensured that individual characteristics of children are fully considered in a pain evaluation result and a management scheme, and the pertinence and scientificity of pain management are remarkably improved.

The invention relates to a pain assessment method and system based on a visual large language model. The method comprises the following steps: S100, synchronously collecting video data of the face and limbs of a target object; s200, performing space-time sampling processing on the video data to obtain image data; s300, generating a multi-step guide instruction sequence based on a preset pain assessment dimension; s400, inputting the image data and the instruction sequence into a visual big language model, and gradually reasoning the visual big language model according to a preset pain assessment dimension; and S500, the visual big language model sequentially generates a scoring basis, a scoring result and a comprehensive pain grade of each dimension according to the reasoning result of each dimension. Through the arrangement, the technical problem of objective assessment of the pain degree in the medical operation process is solved.

The invention relates to the technical field of intelligent medical treatment, in particular to an abdominal postoperative pain intelligent medical management system based on a JBI mode. The system comprises a platform application layer which is used for providing a user interface and an interaction mode, and medical staff and patients can access and use various services provided by a platform service layer; the platform service layer is used for monitoring and analyzing the pain level of a patient in real time, giving an alarm when the pain level is abnormal, and formulating and adjusting an analgesia strategy according to the pain level; and the evaluation feedback unit is used for formulating an evaluation index for the result of the platform service layer and optimizing the service according to the result of the index. The system not only records the pain score and accompanying symptoms of the patient, but also performs customized pain assessment according to different types of patients, so that the working efficiency of medical personnel is improved, the treatment compliance and satisfaction of the patient are enhanced, and finally the overall pain management level and the life quality of the patient are improved.

The invention discloses a health monitoring and pain assessment system based on multi-modal data fusion, and relates to the technical field of health assessment. The system comprises data acquisition, preprocessing, feature fusion and other modules. Multi-modal data are collected and frequency is adaptively adjusted, various data are processed by using a specific technology during preprocessing, a feature fusion module innovates a fusion mechanism, and an evaluation module uses a reinforcement learning model and comprises a health monitoring module, a user interaction module and a data storage module, so that comprehensive functions are guaranteed. According to the method, the evaluation accuracy is improved through multi-modal collection, data association is uniquely fused and mined, personalized services are realized through self-adaptive adjustment, data security is guaranteed through the block chain technology, and medical health development is powerfully promoted.

The invention discloses a cancer pain treatment pain assessment method and system based on intelligent data processing, and relates to the field of treatment assessment, and the method comprises the steps: 1, obtaining basic pathological data, medication records and real-time vital signs of a patient in real time through a hospital information system, extracting symptom features, and carrying out the calculation of the symptom features; identifying the self-contained pain text of the patient, extracting the pain event characteristics, and matching the symptom with the pain event characteristics to form a pain correlation factor matrix; 2, constructing a pain prediction model based on a time sequence convolutional network, and generating a multi-dimensional data acquisition trigger instruction; a mode of combining a time sequence convolutional network and a multi-head self-attention mechanism is adopted, a high-sensitivity acquisition window of a pain event is automatically identified, discontinuous event interference is inhibited through a time decay factor, and a high-weight time period is reserved by utilizing sparsification, so that a data acquisition time period with a high contribution value is automatically obtained; and the recognition precision of the key pain time period is remarkably improved.

The invention relates to the technical field of medical monitoring, and particularly discloses a pain degree assessment method and system based on multi-modal sensing and wearable equipment. The method comprises the following steps: acquiring physiological signals and limb movement data of a patient to form a multi-dimensional pain feature vector; a dynamic weight distribution algorithm is adopted, and physiological and limb action modal weight coefficients are adjusted in real time based on each modal data prediction confidence coefficient; a multi-modal classifier is utilized, features are fused through a cross-modal attention mechanism, and 0-10 levels of pain quantized values are output; and intelligent regulation and control of analgesia parameters are realized. The system comprises a multi-modal sensing module, a feature extraction module, a dynamic weight distribution module, a multi-modal classification module and an analgesia equipment control module. The wearable device integrates a sensor and a processing unit to realize data acquisition and analysis. According to the invention, the dynamic, precise and intelligent pain assessment is realized, the technical problems of strong subjectivity and poor real-time performance of the traditional assessment method are solved, and reliable technical support is provided for clinical analgesia management.

The invention discloses a self-adaptive control method of an analgesia infusion system based on multi-modal feedback, and relates to the field of analgesia infusion automation control, and the method comprises the steps: inputting a feature vector into a multi-modal fusion decision-making layer, and calculating a comprehensive pain index PI by adopting a confidence-weighted self-adaptive fusion algorithm; taking the comprehensive pain index PI, the drug cumulant and the physiological stability index as state input, and inputting the state input into a self-adaptive controller based on reinforcement learning; the adaptive controller outputs a basic infusion rate adjustment amount, a pulse dose adjustment amount, and a locking time adjustment amount. According to the invention, through multi-source signal acquisition and confidence coefficient weighted adaptive fusion, the accuracy and robustness of pain assessment are improved; the hierarchical reinforcement learning controller realizes personalized dose optimization on the premise of safety priority, and gives consideration to both analgesic effect and physiological stability; and the safety monitoring module is combined with multi-modal cross validation and pharmacokinetic prediction, so that the safety risk is effectively reduced.

The invention relates to the technical field of facial dynamic video image pain assessment, in particular to a facial dynamic video image pain assessment method based on a dynamic fusion module, and the method comprises the steps: collecting data through a multi-modal sensor, and generating a three-dimensional feature mapping map; activating an adaptive weight adjustment module to generate a configuration parameter set; executing micro-expression feature extraction and dynamic fusion operation to output a high-precision feature vector; inputting a grading evaluation model to complete pain degree quantitative grading. According to the method, facial micro-expression changes can be accurately captured, feature distortion can be dynamically recovered, nonlinear feature components can be mined, feature extraction integrity and evaluation accuracy can be improved, meanwhile, the feature capture capability in a complex environment can be enhanced through a controlled gradient optimization dynamic fusion technology, and the calculation efficiency can be optimized.

The invention provides a pain assessment method, and the method comprises the steps: obtaining target myoelectricity data and target scale assessment data of a target object, the target myoelectricity data being myoelectricity data obtained by carrying out the myoelectricity signal sampling of a specified sampling part under a specified action, the target scale evaluation data is pain evaluation data obtained by performing pain grade evaluation on the target object according to a preset pain scale; and based on the target myoelectricity data, correcting the target scale evaluation data to obtain a final pain evaluation result. According to the method, the myoelectricity signal sampling is performed on the specified sampling part under the specified action, the scale evaluation data obtained by adopting the pain scale are corrected by utilizing the myoelectricity data obtained by sampling, and the myoelectricity data reflect the real pain response of the target object, so that the corrected pain evaluation result is more accurate, and the accuracy of the pain evaluation result is improved. And the accuracy of pain assessment is improved.

The invention is suitable for the field of pain assessment, and particularly provides a pain threshold estimation method and system based on multi-modal data fusion, and the method comprises the steps: constructing a cross-modal weak feature enhancement fusion network, carrying out the processing of collected multi-modal data, and extracting physiological weak features and behavior weak features; introducing a time-varying attention Transform model, dynamically allocating weights for the processed physiological weak features, behavior weak features and environment auxiliary data by using a pain dynamic tag, and outputting cross-modal fusion features; constructing a double-branch pain threshold estimation model, wherein the model comprises a supervised estimation branch and an unsupervised estimation branch; and introducing a pain feature element library, carrying out online calibration on parameters of the double-branch model in combination with a meta learning algorithm, and outputting a real-time pain threshold value and a short-term pain threshold value change trend of the target object. According to the embodiment of the invention, the clinical operability of pain assessment is remarkably improved, and ineffective intervention caused by inaccurate assessment is reduced.

The invention discloses a pain grade evaluation method and an evaluation system thereof. According to the method and the system, the pain condition of the patient can be comprehensively and accurately evaluated by integrating various evaluation tools. The combined use of e-VAS, EIAS, AIS and PMS not only considers the intensity and physiological influence of pain, but also covers the influence of psychological state and life quality, thereby providing a multi-dimensional pain assessment perspective for medical staff. And meanwhile, by introducing digital and remote monitoring technologies, the convenience and efficiency of pain management are greatly improved. The digital operation of the e-VAS is simple and visual, the patient can easily complete self-evaluation, and the remote monitoring system allows the patient to continue to participate in pain management after being discharged from the hospital and keep continuous communication with a medical team. The continuity and convenience not only improve the treatment compliance of the patient, but also enable the medical staff to adjust the treatment scheme in time so as to cope with the dynamic change of the pain, thereby achieving the effects of optimizing the patient experience and enhancing the treatment effect.

State of art techniques hardly address real-time detection of pain with sensor fusion approaches. A method and system herein provides intelligent sensor selection and fusion mechanism to detect the presence of pain and quantify pain level on edge devices in an effective and efficient manner, guided by information on pain origin, pathway and type extracted from input signals, without the requirement of any manual intervention or interference. The system provides an automation framework to accelerate the deployment of customized tiny models for wearable and edge devices, based on sensor availability and device compute capacity to create suitable model for the target device and task. The system takes accuracy, model size and latency as objectives, and sensor availability and computation resource constraints as targets for the automation framework enabling rapid generation and deployment of models on multiple edge devices (wearable devices), independent of the availability of a specific sensor.

The invention relates to the field of medical auxiliary diagnosis, in particular to an intelligent child pain assessment system based on AI facial expression analysis, a facial detection module obtains a child facial image, and performs three-stage cascade detection by using a multi-task cascade convolutional neural network model to obtain facial key point pixel coordinate information; the key point positioning module carries out noise filtering processing on the coordinate information to obtain more accurate face key point coordinate information, the information is transmitted to the key point thermodynamic diagram representation module, and the key point thermodynamic diagram representation module generates a face key point thermodynamic diagram representing the degree of pains of children in combination with the face key point coordinate information and a thermodynamic diagram mechanism. The child pain intelligent evaluation module evaluates the degree of pain of the child based on the thermodynamic diagrams and obtains an evaluation result, and finally, the pain evaluation result visualization module visualizes the evaluation result, and the accuracy of pain characterization is improved through high-precision face detection and key point thermodynamic diagram technology, so that the accuracy of pain characterization is improved. And an effective method is provided for evaluating the pains of children.

The invention relates to the technical field of medical technology, in particular to a method for evaluating and adjusting pain in maternal and child anesthesia, which comprises the following steps of: constructing a physiological signal acquisition subsystem, a pretreatment subsystem, a self-adaptive evaluation subsystem and a closed-loop analgesia control subsystem to realize continuous, objective and specific evaluation of pain states of maternal and child patients under anesthesia; and the analgesic medicine is accurately and automatically adjusted. The continuous, objective and specific pain assessment can be realized, the analgesic medicine can be accurately and automatically adjusted, and the safety and comfort of maternal and child anesthesia are improved.

The invention relates to the technical field of medical auxiliary evaluation, in particular to a perioperative pain evaluation system for a brain tumor surgery patient, which comprises a surgery pain data acquisition module for acquiring an HRV signal, an intracranial pressure waveform, a subjective VAS score and pain part data of the patient; the feature extraction module processes the data through abnormal value detection and sliding window filtering, extracts HRV and ICP features in combination with time domain analysis, and outputs standardized data after integrating the HRV and ICP features with subjective feedback features; the AI pain quantification module is used for generating a pain risk probability value based on an AI model constructed by an XGBoost algorithm, triggering three-level early warning and associating pain properties and causes of a patient; the intervention pushing module triggers a multi-terminal prompt and pushes an intervention scheme, and a patient performs graphic interaction and feedback through a mobile phone terminal; and the management and tracing module is used for associating single-time whole-process data, recording an intervention effect and automatically upgrading an unexpected scheme. Therefore, the problems that in the prior art, intracranial pressure and pain coupling is not quantified, intervention lags and the like are solved.

The invention discloses a system for treating chronic lumbago through the synergistic effect of massage and whole body vibration training. The system comprises a patient information registration and management module, a chronic pain evaluation module, a treatment effect evaluation module, a data analysis and feedback module, a vibration training control terminal and a massage treatment and guidance module. A patient is guided to complete standardized waist actions, pain scores under various postures are recorded, a main pain area is determined in combination with a Pareto analysis method, an average value and standard deviation model is constructed by using score data in multiple treatments, and normal distribution fitting and curative effect judgment of pain changes are further achieved. Massage and vibration training are linked through the system, data intercommunication is achieved, standardized, intelligent and personalized intervention in the treatment process is achieved, and the rehabilitation efficiency of the chronic lumbago patient is effectively improved. The system has the advantages of complete structure, scientific evaluation, accurate intervention and the like, and is suitable for popularization and application in the fields of rehabilitation medicine and clinical physical treatment.

Technology for cooperative learning pain assessment may include a sensed pain classifier to determine a sensed context and a pain assessment classifier to determine an inferred pain score. Disclosed technology may determine an uncertainty score based on the sensed context and the inferred pain score. Responsive to the uncertainty score meeting a first threshold condition, a manual label for the subject data may be used to update the sensed pain classifier or the pain assessment classifier. Responsive to the uncertainty score meeting a second threshold condition, a generated label for the subject data may be used to update the sensed pain classifier or the pain assessment classifier. The inferred pain score may be provided as an assessed pain result for the subject.

The invention relates to the technical field of medical data management, and particularly discloses an acute postoperative pain assessment and management decision-making system based on a patient report result, which is used for solving multiple challenges facing postoperative acute pain management, including subjectivity of a traditional assessment method, lack of personalized treatment, insufficient intervention coordination and lack of real-time monitoring ability. The problems that an existing decision support system is insufficient in intelligence and complex in user interaction experience, so that the efficiency of medical staff is low, and finally rehabilitation and life quality of a patient are affected are solved, and the system comprises a patient pain data collection module, a data analysis and processing module, a decision support module and a user interface. The data analysis processing module analyzes the collected data by adopting a machine learning algorithm so as to evaluate the severity of the pain; according to the invention, individualized pain assessment is realized through a machine learning algorithm, patient pain data is analyzed in real time, a management scheme is rapidly adjusted, patient satisfaction is improved, a pain management scheme is optimized, and manual intervention is reduced.

The invention belongs to the technical field of neurobiology and pain mechanism research, and discloses a full-automatic pain sense measuring system based on artificial intelligence integrated vital sign monitoring, which is integrated with multi-mode biological signal acquisition equipment, can synchronously record pain sense behaviors and physiological data (such as heart rate, respiratory rate, body temperature, excretion and the like) of experimental animals, and can be used for measuring the pain sense of the experimental animals. And the acquired data is analyzed and processed in real time by using an artificial intelligence algorithm to generate quantitative pain assessment indexes. Compared with a traditional pain assessment method depending on subjective observation, the system has the advantages that the efficiency and accuracy of data acquisition and analysis are remarkably improved, the human deviation can be reduced, and the assessment error rate is reduced to 5% or below. The method is particularly suitable for research of complex pain models and high-throughput drug screening, the preclinical research and development period can be remarkably shortened by 20%-30%, and the research and development cost is reduced. The invention has obvious market demand and economic benefit, and provides important technical support for the field of pain research.

The invention discloses a patient pain assessment method and system, and relates to the technical field of pain assessment. A patient pain assessment system comprises a data acquisition module, a self-assessment correction module, a physiological assessment correction module, an expression assessment correction module and a pain assessment module. According to the method, the personalized self-evaluation deviation P of the patient is calculated, and the score V of the visual simulation scale VAS filled by the patient is subjected to personalized correction, so that the subjective difference between individual patients can be effectively reduced, the final VAS score is closer to the actual pain level, the real pain state of the patient can be reflected more accurately, and the patient experience is improved. A doctor can make more accurate diagnosis, insufficient treatment or excessive treatment caused by misjudgment is avoided, and the assessment accuracy of the patient pain assessment method and system is improved.

The invention relates to the field of pain management and rehabilitation treatment, in particular to a painless ward construction method and system based on big data, and aims to improve pain management efficiency and fracture patient rehabilitation quality through big data and artificial intelligence means. By integrating various information technologies, an artificial intelligence algorithm and a big data analysis technology, the functions of encrypted storage of fracture patient information, multi-dimensional pain assessment, personalized analgesia scheme formulation, real-time monitoring and early warning, comprehensive assessment of recovery conditions and the like are realized. Meanwhile, the method also considers the prediction of the pain trend and the intelligent generation of the training plan, solves the problems that the making of most rehabilitation training plans at present still depends on the experience and judgment of professionals and is lack of scientificity and systematicness, and provides a brand new solution for pain management and rehabilitation treatment based on big data.

The invention discloses a visual pain assessment report system and method, and relates to the technical field of medical treatment, three groups of algorithm units are matched with one another to form a core architecture of the visual pain assessment report system, a basic pain value Pd is calculated by considering pain intensity Pi, pain time Pct and a sensitivity coefficient Sp, and the basic pain value Pd is calculated by taking the pain intensity Pi, the pain time Pct and the sensitivity coefficient Sp into consideration. The comprehensive pain value Ps is calculated by combining the real-time physiological data (such as heart rate and blood pressure) of the patient, more comprehensive and more detailed visual pain assessment is provided, and compared with subjective description singly depending on the patient, the pain assessment mode can reflect the actual pain condition of the patient more accurately, and the pain assessment accuracy is improved. The planned drug application amount Dp is calculated by comprehensively considering the basic drug application amount Db, the body weight We of the patient and the comprehensive pain value Ps through the planned drug application amount algorithm unit, it can be ensured that the patient receives proper drug treatment, the situation that the drug amount is insufficient or excessive is avoided, and therefore the treatment effect on the patient can be improved, and adverse reactions are reduced.

The present application relates to the technical field of medical image processing, in particular to a pain assessment system for neonatal facial expression image analysis, comprising a data acquisition module, a facial expression manifold construction module, a curvature field analysis module, a spatiotemporal feature hierarchical analysis module and a pain assessment and alarm module, the facial expression manifold construction module maps discrete facial feature points to a Riemann manifold space to construct an individualized expression manifold model; the curvature field analysis module calculates a facial expression dynamic curvature field and extracts micro-motion curvature features; the spatiotemporal feature hierarchical analysis module performs multi-scale decomposition on the curvature features to identify pain-related micro-motion units; the pain assessment and alarm module calculates a pain score and performs early warning, the present application significantly improves micro-expression capture sensitivity, is robust to changes in shooting conditions, improves the ability to distinguish different types of pain, realizes objective quantitative assessment and multi-level early warning of the pain state of neonates, and provides reliable technical support for clinical pain management.