53 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 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 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 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 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.

The invention belongs to the technical field of pain assessment, and particularly relates to a multi-modal real-time intelligent pain assessment auxiliary system based on deep learning. A multi-modal data acquisition device and a two-stage consistency reasoning device; wherein the multi-modal data acquisition device is used for synchronously acquiring a face video and a near-field voice under a unified time reference, and segmenting a time axis to form a unified time slice sequence so as to output the unified time slice sequence and an anchor point set which are aligned; and the two-stage consistency reasoning device is used for executing short window scoring on each time slice to obtain a pain score and uncertainty under the constraint of the aligned unified time slice sequence and the anchor point set, completing candidate fragment confirmation on a score timeline and outputting the real-time pain score, the confidence coefficient and the starting and ending time of the confirmed pain fragment. According to the method, the evaluation stability and reliability are improved while real-time response is ensured; and by combining a double-threshold rule and anchor point neighborhood consistency verification, non-pain interference is effectively eliminated, and a pain fragment is accurately confirmed.

The application provides a non-contact pain monitoring mattress and system based on multi-modal physiological signal fusion, the mattress body comprises, from bottom to top, a bottom support layer, a signal shielding layer, a sensing core layer and a breathable skin-friendly layer, the system comprises the non-contact pain monitoring mattress, a low-noise charge amplifier, a fiber demodulator, an edge computing gateway and a cloud analysis center, the data after signal conditioning is transmitted to the edge computing gateway and uploaded to the cloud analysis center, and the cloud analysis center performs quantitative scoring through a tumor-specific pain model.The application has the advantages that no electrode or wearable device needs to be pasted, the skin irritation and psychological burden of traditional monitoring are completely eliminated, the patient compliance is improved, the limitation of subjective evaluation is broken through, an objective pain evaluation method is provided for patients in the late stage / aphasia, multi-modal signal fusion monitoring is used to effectively distinguish pain from ordinary emotional fluctuations, the degree of pain is fed back in real time, the doctor is assisted in dynamically adjusting the dose of analgesic drugs, and the risk of drug overdose and deficiency is reduced.

The present application relates to the technical field of medical image analysis, in particular to a patient post-anesthesia pain grading evaluation method based on artificial intelligence. The method acquires multi-time body posture images in a complete respiratory cycle, constructs a trunk reference system, defines the end of the limb as an analysis point and a preset defense position as a target point; constructs a limb scheduling consumption matrix based on the distance between the analysis point and the target point, calculates the limb wandering confusion degree combined with the element distribution, and fuses to obtain the defense intention focus degree reflecting the order of whole body movement; the consumption matrix is subjected to geometric cost matching to obtain a limb scheduling total consumption index, and the focus degree is combined to generate a defense interference index; finally, the grading evaluation coefficient is determined according to the numerical distribution and stability of the index in the complete respiratory cycle, thereby objectively distinguishing the pain state and the wake-up period agitation state, and significantly improving the automation level and reliability of post-anesthesia pain evaluation.

The invention discloses an electromyographic signal acquisition system and device for standard pose pain assessment and discrimination, and relates to the field of electromyographic acquisition, and the system comprises a pose detection module which is used for discriminating whether the pose of a person to be acquired is a specific standard pose; the electromyographic signal acquisition module is used for selecting a proper acquisition channel to perform signal acquisition according to the pose of the acquired person; according to the electromyographic signal acquisition system and device for standard pose pain assessment and discrimination, the pose of a person to be acquired is detected in real time by using an external camera and a wearable gyroscope sensor array, and is compared with specific standard pose model data in a pose comparison system; whether the posture of the collected person is a specific standard posture or not is judged through a clear and efficient method; the specific standard pose is designed according to a pain principle and a scientific experimental scheme, so that the electromyographic signal corresponding to the specific standard pose of the collected person can be used as an important index for pain assessment and treatment effect judgment.

This invention discloses a method and system for pain threshold determination based on multimodal automatic laser stimulation and animal behavior analysis. The method includes: using an RVC-Pose convolutional neural network model to detect key points on the animal's foot and outputting the foot's spatial coordinates in real time; positioning a laser spot on the foot and outputting continuously adjustable laser stimulation according to a preset gradient; extracting facial key points and micro-expression features using a Light-Face facial recognition algorithm, and / or reconstructing the animal's three-dimensional skeleton using a multi-view geometric reconstruction algorithm and extracting pain-related behavioral features; inputting the multimodal behavioral data into a multi-parameter fusion judgment model to automatically identify pain responses, adaptively adjusting the stimulation intensity under gradient stimulation mode, terminating stimulation and recording the pain threshold when an effective pain response is detected. This invention achieves fully automatic closed-loop control for pain threshold determination, solving the problems of inaccurate positioning, imprecise stimulation, and subjective judgment in existing technologies, significantly improving the objectivity and accuracy of pain assessment.

1. The name of the design product: dial gauge (real-time pain). 2. The use of the design product: for real-time pain assessment, specifically for assessing dial gauge. 3. The design points of the design product: in the combination of shape and pattern. 4. The picture or photo that best indicates the design points: reference diagram in use. 5. Omit the front and back views; the left and right views are symmetrical, omit the left view.

The application discloses a pain assessment model construction method and a non-contact continuous pain assessment method, and belongs to the field of image expression recognition. Through a non-contact mode, multi-modal time sequence data of a patient is collected, and after time-space synchronous processing to obtain a multi-modal frame package sequence, single-modal self-supervision pre-training is performed based on extracted single-modal time sequence data, and then a multi-modal projection layer is connected to obtain a front structure for cross-modal semantic alignment coding, so as to map output features of each mode to a large language model word vector dimension, and then a large language backbone model is connected to obtain an overall structure. The multi-modal time sequence data of the patient is input, fine-tuning training of the large language model is performed by indicating the overall structure to output a pain assessment result, a trained pain assessment model is obtained, and the trained pain assessment model is used for non-contact continuous pain assessment. The pain assessment result includes a pain level, a pain score, an interpretable heat map and the like. The application combines a large language model, so that the trained model can realize pain level division and interpretable continuous output.

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 relates to the technical field of electroencephalogram signal evaluation, in particular to a pain evaluation model, method and system based on electroencephalogram signals and comparative learning. Wherein the model comprises a lightweight channel attention network and a comparative learning interaction layer which are in cascade connection; the lightweight channel attention network serves as a backbone network and is used for receiving the preprocessed standardized electroencephalogram signal fragments and outputting potential representation vectors of electroencephalogram signals; the comparative learning interaction layer is used for pre-training to obtain a pre-training weight, and comprises a hierarchical soft comparison module, a time dynamic comparison module and a depolarization clustering comparison module which are connected to the output end of the lightweight channel attention network in parallel; the lightweight channel attention network adjusts parameters based on a pre-training weight, and outputs a pain assessment result through a classifier. By adopting the evaluation model, the unlabeled data can be effectively utilized, the individual difference can be overcome, and the method has the characteristic of accurately capturing the dynamic change of the pain time sequence.

The Intelligent Pain Management Patch System (IPMPS) is a wearable medical device designed to deliver personalized and continuous pain relief for palliative care patients. The system integrates advanced sensor technology, a smart drug delivery system, and AI-driven pain assessment. The patch continuously monitors patient physiological parameters, assesses pain levels in real-time using AI, and administers precise doses of analgesic medication. The system includes a user interface for healthcare providers and patients, facilitating remote monitoring and adjustments. The IPMPS offers a non-invasive, personalized approach to managing chronic pain, reducing the need for oral medications and minimizing side effects.

Compared with the prior art, the pain assessment reference tool has the advantages that the plate-shaped body is a regular plate body and is convenient to carry, related pain assessment information is recorded on the plate-shaped body according to areas, medical staff can conveniently refer to the plate-shaped body to carry out pain assessment, the identification plate is detachably connected with the plate-shaped body, and therefore the pain assessment reference tool is convenient to carry out. Different pain standard information can be recorded on the identification plate according to departments; comprising a rectangular plate-shaped body, a grade judgment area and an evaluation standard area are sequentially arranged on the first face of the plate-shaped body in the length direction, the grade judgment area is used for setting grade judgment marks, the evaluation standard area comprises at least one identification plate detachably connected with the plate-shaped body, and the identification plate is used for setting evaluation standard information.

The application relates to the technical field of electroencephalogram signal evaluation, in particular to a pain evaluation model, method and system based on electroencephalogram signals and contrast learning; wherein the model comprises a cascade-connected lightweight channel attention network and a contrast learning interaction layer; the lightweight channel attention network serves as a backbone network and is used for receiving a preprocessed standardized electroencephalogram signal segment and outputting a potential representation vector of the electroencephalogram signal; the contrast learning interaction layer is used for obtaining pretraining weights through pretraining and comprises a hierarchical soft contrast module, a time dynamic contrast module and a debiased clustering contrast module which are connected in parallel to an output end of the lightweight channel attention network; the lightweight channel attention network adjusts parameters based on the pretraining weights and outputs a pain evaluation result through a classifier. The evaluation model can effectively utilize unannotated data and overcome individual differences, and has the characteristics of accurately capturing the time sequence dynamic change of pain.

The application relates to the technical field of VR devices, in particular to a VR device for patient pain assessment and a method thereof, which comprises a VR device body, a wearing support arranged on the VR device body, an adjusting shell arranged on the VR device body, an adjusting block slidingly arranged on the adjusting shell and connected with the wearing support and an adjusting component. The VR device has the beneficial effects that the adjusting block is driven to move upwards or downwards by the adjusting component, the wearing support is driven to move upwards or downwards, the distance between the wearing support and the VR device body is adjusted, the head of different patients can be adapted, the patient does not feel uncomfortable when wearing the VR device during the assessment, and the patient is convenient to assess.

1. Name of this design product: Pain Assessment Scale (Naan Bread Pattern). 2. Purpose of this design: To assess the patient's pain level by using the pain rating values ​​on the card. 3. The key design features of this product are the combination of pattern and color. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Other situations requiring explanation: Other explanation: Omitted views: Because this design product is a card-type product, the rear view, left view, right view, top view and bottom view do not have design features, so they are omitted.

The invention discloses a pain assessment and intervention system, which belongs to the technical field of medical detection and comprises a user interaction module, a monitoring feedback module, a privacy security module, a cache management module, an assessment adjustment module, a comprehensive atlas module, a deep assessment module, an intervention suggestion module and a psychological support module. According to the method, more personalized pain assessment can be realized, the pain problem of a patient can be better managed, the workload of a doctor can be relieved, medical resources can be more reasonably distributed, the comprehensiveness and accuracy of pain assessment are improved, a more complete data basis is provided for comprehensive assessment, a deeper diagnosis basis is provided for the doctor, and the patient experience is improved. And the information related to the pain can be more comprehensively mined.

The invention relates to an ultrasonic diagnosis system giving consideration to pain sense monitoring and a construction method, and belongs to the technical field of medical instruments. Comprising an ultrasonic examination module, an electrocardio waveform data acquisition module, a data processing module, a pain sense display module and a pressure display module, the ultrasonic examination module comprises an image acquisition device used for acquiring image data to construct a human body three-dimensional model and determining the position of an ultrasonic probe on a human body, and the ultrasonic probe provided with a pressure sensor; the electrocardio waveform data acquisition module is used for acquiring electrocardio waveform data of a detected person; the data processing module comprises a three-dimensional model construction module, a data comparison and analysis module and a pain assessment module; the ultrasonic examination module and the electrocardio waveform data acquisition module are respectively connected with the data processing module; the pain assessment module is connected with the pain display module; and the data comparison and analysis module is connected with the pressure display module. A doctor can conveniently and reasonably adjust the examination strength, and the psychological burden of a patient is relieved.

1. The name of the design product: pain wisdom turntable. 2. The use of the design product: the design product is used for pain assessment. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: perspective drawing.

The invention discloses a cold-dampness pain assessment method based on multi-modal traditional Chinese medicine sign information. The method comprises the steps that multi-modal sign information of dynamic vision, infrared thermal imaging, bionic touch, acoustics, thermal disturbance, structured inquiry and the like of a patient is synchronously collected; a deep learning model is adopted to carry out feature extraction on the modal information; fusing the multi-modal features by using a symptom decoupling transformer model and introducing learnable query tokens corresponding to pathological factors such as cold and dampness, and decoupling and summarizing independent feature representation of each symptom; and finally outputting a quantitative evaluation vector representing the severity of each symptom. According to the method, interlaced symptoms such as cold and dampness can be effectively separated and independently and quantitatively evaluated, and the objectivity, accuracy and interpretability of diagnosis can be improved.

The utility model discloses a pain assessment tool, and belongs to the field of medical instruments. A pain assessment tool comprises a hand model, five fingers on the hand model are provided with mounting grooves, sounders are mounted in the mounting grooves, a facial makeup display board is arranged on the outer sides of the mounting grooves, a handle is mounted at the root of the hand model, a limiting assembly used for fixing the sounders is arranged in the mounting grooves, and the facial makeup display board comprises a facial makeup containing box connected with the limiting assembly. A facial makeup placing groove for placing facial makeup is formed in the inner side of the facial makeup placing box, and a transparent plate is mounted at the top of the facial makeup placing box; according to the utility model, the handle is arranged at the root part of the hand mold, so that the hand mold is more convenient and labor-saving to hold during evaluation, the evaluation efficiency is effectively improved, and meanwhile, by virtue of the generator, the pain evaluation can be carried out by medical staff through pressing sound production, and the efficiency and the accuracy are improved.

The utility model relates to the technical field of medical indication labels, and discloses a pain scale plate which comprises a base plate, a cover plate, a red plate, a blue plate and a yellow plate, a connecting column is fixedly connected to the upper surface of the base plate, the red plate, the blue plate, the yellow plate and the cover plate are sequentially arranged on the connecting column in a sleeved mode from bottom to top, and the outer diameters of the base plate, the red plate, the blue plate and the yellow plate are sequentially reduced. The outer diameter of the cover plate is the same as that of the chassis; through cooperation of the base plate, the red plate, the blue plate, the yellow plate and the cover plate, indication areas on the base plate, the red plate, the blue plate and the yellow plate can be conveniently exposed through the V-shaped groove in the cover plate, so that the pain level of a patient can be indicated more simply, medical staff can judge the pain level of the patient conveniently, the pain assessment efficiency is improved, and the patient pain assessment accuracy is improved. The system is suitable for popularization and use in common wards, can integrate pain assessment identification, assessment tools and pain scores, is easy to understand, and improves the practicability.

Systems and methods are provided for using machine learning (ML) models to evaluate data generated during MRI procedures, such as magnetic resonance spectroscopy (MRS) data, to evaluate patient disc pain. An ML-based disc assessment system can evaluate MRS spectrum data ML models trained to classify the MRS spectrum based on diagnostic and / or outcome-based ground truth data.

The invention relates to the technical field of medical health monitoring, and discloses a portable wristwatch type pain objective measurement device, which comprises a shell, a watchband, a multi-mode physiological signal sensing module, an edge calculation unit, a storage module, a power management module and a communication module, and is characterized in that the shell is suitable for being worn on the wrist of a user; the watchband is used for fixing the shell on the wrist of a user and is connected with the two ends of the shell; the multi-mode physiological signal sensing module is used for being integrated on the inner side of the shell and making contact with the skin of a user. According to the portable wristwatch type pain objective measurement device, four kinds of physiological signal sensors which are closely related to pain, namely galvanic skin response, heart rate variability, surface myoelectricity and near-infrared blood oxygen, are integrated in a wristwatch structure, so that a multi-mode sensing system oriented to pain assessment is constructed; the defect that an existing wearable device only monitors a single health index and cannot reflect pain specific physiological response is overcome.

This invention discloses a method and system for facial pain expression analysis based on spatiotemporal information. By sampling facial expression videos using a sliding window, several video segments are obtained, improving dataset utilization. Discrete cosine transform is applied to the video frames, followed by sawtooth scanning for enhancement, resulting in detailed feature enhancement maps that enhance detail information. Then, all video frames and their corresponding detailed feature enhancement maps from each sampled video segment are combined and input into a Transformer model. The Transformer model uses a concatenated cross-processing method to extract and fuse temporal and spatial features. Finally, the pain analysis result is obtained based on the pain level corresponding to each sampled video segment. This method effectively extracts feature information from facial expression videos, resulting in high accuracy in pain assessment.