Pain assessment method under local anesthesia and related equipment

Abstract

The invention discloses a pain assessment method under local anesthesia and related equipment. The method comprises the following steps: monitoring grip strength data of an intraoperative patient under local anesthesia to generate a grip strength change curve; monitoring skin conductivity data of an intraoperative patient under local anesthesia to generate a skin conductivity fluctuation curve; and based on the grip strength change curve and the skin conductivity fluctuation curve, evaluating the overall pain experience under local anesthesia through a peak-end law. The problem that an accurate, effective, easy-to-operate and effective solution for knowing the intraoperative pain of the local anesthesia patient does not exist at present can be solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart medical care, and more specifically, the present invention relates to a pain assessment method under local anesthesia and related equipment. Background Art

[0002] Operations or surgical projects performed under local anesthesia include but are not limited to the following: tooth extraction surgery: about 2 million cases are completed each year; skin excision surgery: about 1 million cases are completed each year; fracture reduction surgery: about 500,000 cases are completed each year; arthroscopic surgery: about 300,000 cases are completed each year; hemorrhoidectomy: about 200,000 cases are completed each year; sinusitis surgery: about 100,000 cases are completed each year; ophthalmic surgery: about 50,000 cases are completed each year. How to improve the experience evaluation of patients under local anesthesia is the key to reasonably setting anesthesia rules for different surgical procedures. At present, there is no accurate, effective and easy-to-operate solution to understand the pain of patients under local anesthesia during surgery. Summary of the invention

[0003] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0004] In order to solve the problem that there is currently no accurate, effective, easy-to-operate solution for understanding the pain of patients undergoing local anesthesia during surgery, in a first aspect, the present invention proposes a method for assessing pain under local anesthesia, the method comprising:

[0005] Monitor the grip strength data of patients during surgery under local anesthesia to generate a grip strength change curve;

[0006] Monitor skin conductivity data of patients during surgery under local anesthesia to generate skin conductance fluctuation curves;

[0007] The overall pain experience under local anesthesia was evaluated based on the grip force change curve and the skin conductance fluctuation curve using the peak-end rule.

[0008] Optionally, the evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes:

[0009] The overall pain experience under local anesthesia is evaluated based on the grip force change curve and the skin conductance fluctuation curve combined with the DTW distance measurement algorithm through the peak-end rule.

[0010] Optionally, the overall pain experience under local anesthesia is evaluated by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve in combination with a DTW distance measurement algorithm, including:

[0011] Calculate the DTW distance between the grip force change curve and the skin conductance fluctuation curve to analyze the matching path between the grip force change curve and the skin conductance fluctuation curve;

[0012] From the matching paths calculated by DTW, the local maxima of grip force and skin conductivity were extracted to determine the pain peak and its associated peak time interval;

[0013] From the matching paths calculated by DTW, the end values ​​of the grip force data and the skin conductivity and their associated end time intervals are determined;

[0014] The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

[0015] Optionally, the evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes:

[0016] Divide the surgical procedure into multiple time periods;

[0017] A sliding window is set in each stage to extract statistical features of the grip force change curve and the skin conductance fluctuation curve to form a time series feature matrix, wherein the statistical features are used to indicate the overall fluctuation of the grip force change curve and the skin conductance fluctuation curve in each stage;

[0018] Extracting dynamic features from the time series data in each stage, wherein the dynamic features are used to indicate changes in grip force and skin conductance over time;

[0019] In the data of the high pain category, the peak data of grip force and skin conductance were selected as the pain peak;

[0020] According to the clustering categories, the grip strength and skin conductance data corresponding to the high pain category at the end of the surgery were used as the end value;

[0021] The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

[0022] Optionally, also include:

[0023] The generated grip force change curve and skin conductance fluctuation curve were normalized and the data were smoothed to the same scale.

[0024] Optional options include:

[0025] The isolation forest algorithm or moving average technique was used to eliminate occasional interference data in the grip force data and skin conductivity data.

[0026] Optionally, also include:

[0027] Obtaining historical medical data of the patient;

[0028] Based on the injury and illness history in the historical medical treatment data, extract the types of pain-related injuries and illnesses experienced by the patient that have theoretical pain data;

[0029] Monitoring the patient's choice of the injury type based on pain perception at different times during surgery under local anesthesia;

[0030] Determining the patient's subjective pain experience based on pain perception at different times based on the selection information;

[0031] The overall pain experience under local anesthesia was evaluated by the peak-end rule based on the patient's subjective pain experience based on pain perception at different times.

[0032] In a second aspect, the present invention further provides a pain assessment device under local anesthesia, comprising:

[0033] A first monitoring unit is used to monitor the grip strength data of a patient during surgery under local anesthesia to generate a grip strength change curve;

[0034] A second monitoring unit is used to monitor the skin conductivity data of the patient during surgery under local anesthesia to generate a skin conductance fluctuation curve;

[0035] An evaluation unit is used to evaluate the overall pain experience under local anesthesia based on the grip force change curve and the skin conductance fluctuation curve through the peak-end rule.

[0036] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for assessing pain under local anesthesia as described in any one of the above-mentioned first aspect when executing the computer program stored in the memory.

[0037] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for assessing pain under local anesthesia according to any one of the above items in the first aspect is implemented.

[0038] In summary, the method for assessing pain under local anesthesia proposed in this application generates a grip force change curve by monitoring the grip force data of the patient during surgery under local anesthesia; generates a skin conductance fluctuation curve by monitoring the skin conductivity data of the patient during surgery under local anesthesia; and evaluates the overall pain experience under local anesthesia through the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve. Thus, by monitoring grip force and skin conductance and combining the peak-end rule, the subjective pain experience can be converted into objective quantitative data, reducing the limitations of relying on the patient's subjective feedback. Grip force and skin conductance can be collected in real time, and the patient's pain fluctuation can be captured instantly, so that the anesthesiologist can adjust the analgesic measures in time when necessary and optimize the patient's surgical experience. The generation of the peak-end rule score can provide a basis for optimizing the local anesthesia effect. By summarizing the pain fluctuation patterns of different patients, the anesthesiologist can more reasonably adjust the dosage of local anesthetics or increase auxiliary analgesic measures, thereby improving the local anesthesia effect in subsequent operations. The postoperative pain score helps to review the patient's pain changes during surgery and help doctors provide more accurate suggestions for postoperative analgesia.

[0039] The method for assessing pain under local anesthesia of the present invention, other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1 A schematic flow chart of a method for pain assessment under local anesthesia provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a pain assessment device under local anesthesia provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of an electronic device for pain assessment under local anesthesia provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0045] In order to solve the problem that there is currently no accurate, effective, and easy-to-use solution to effectively understand the pain of patients undergoing local anesthesia, please refer to Figure 1 , is a flow chart of a method for pain assessment under local anesthesia provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0046] S110, monitoring the grip strength data of the patient during surgery under local anesthesia to generate a grip strength change curve.

[0047] S120, monitoring skin conductivity data of a patient undergoing surgery under local anesthesia to generate a skin conductance fluctuation curve.

[0048] S130, evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve.

[0049] It is understandable that this pain assessment method indirectly assesses the patient's pain experience during local anesthesia surgery based on changes in two physiological indicators - grip strength and skin conductance. The core idea is that pain experience will lead to changes in grip strength and skin conductance, and the "peak" and "end value" of these changes can effectively reflect the pain throughout the operation. According to the peak-end rule, we can obtain an overall pain experience score by weighting these key points. According to the peak-end rule in psychology, an individual's overall recollection of an event is mainly affected by the "peak" (highest pain point) and "end value" (pain point at the end of the event) of the event. Therefore, by analyzing the peak and end values ​​of the patient's intraoperative grip strength and skin conductance curves, the overall pain experience can be quantified more concisely and effectively.

[0050] For example, during the operation, the patient is given a grip sensor to monitor the change of grip strength in real time. The grip sensor can be a handle-type device, and the patient only needs to hold it naturally without additional operation. The sensor should have high sensitivity and can accurately record the fluctuation of grip strength. The sampling frequency is recommended to be 100Hz to ensure data continuity. The recorded grip strength data generates a grip strength change curve as the operation progresses. The fluctuation of grip strength can reflect the patient's tension and pain response. For example, when local anesthesia gradually fails, the patient may unconsciously increase the grip strength due to pain; conversely, when sedation is effective, the grip strength will tend to be stable. For example, during a certain operation, the patient's grip strength curve is stable at the beginning, and then multiple peaks appear, which may correspond to the fluctuation of pain. These peak periods can reflect the patient's peak pain, and the change of grip strength at the end of the operation reflects its final pain state. A skin conductance sensor is installed on the patient's palm or finger to monitor the change of skin conductance in real time. Skin conductivity is affected by the sympathetic nerves and is closely related to pain and tension. When the pain increases, the excitability of the sympathetic nerves increases and the skin conductivity increases. It is recommended that the data sampling frequency be kept consistent with the grip strength data to facilitate synchronization of subsequent data. A fluctuation curve is drawn based on the skin conductance data, and the peaks and troughs of the curve represent the changing trend of skin conductance. When patients are stimulated during surgery, skin conductance will fluctuate, and the peak value and frequency of this fluctuation can reveal the frequency and intensity of pain. For example, in a local anesthesia surgery, the skin conductance curve gradually climbs when the local anesthesia wears off, showing that the patient's sympathetic nerve activity is enhanced, which may correspond to the period of increased pain during the operation. Analyzing the time of occurrence of such peaks can help understand the pain fluctuations. Extract the peak and end values ​​from the grip strength curve and the skin conductance curve as the key points of the pain experience. The peak value represents the highest pain during the operation, and the end value represents the pain state at the end of the operation. According to the principle of the peak-end rule, the peak and end values ​​of grip strength and skin conductance are weighted to generate a quantitative pain score. The specific calculation formula is:

[0051] P score =w p Peak+w e ·End

[0052] Among them, Peak is the peak value data in the grip force and skin conductance curves, End is the end value data, and w p and w eis the corresponding weight. Generally speaking, Peak and End will set weights according to the type of surgery and individual differences of patients. By analyzing the final pain score, data support can be provided for the effect of local anesthesia during surgery. The higher the score, the more intense the pain the patient experienced during surgery, and the anesthetic effect may need to be adjusted. For example, suppose a patient has high grip strength and skin conductance peak values, but low end values, indicating that the patient experienced peak pain during surgery, but the final analgesic effect was good. At this time, the peak-end rule pain score may be moderate, which may indicate that anesthesia needs to be strengthened in the middle stage of surgery.

[0053] In summary, the method for assessing pain under local anesthesia provided in the embodiment of the present application monitors the grip strength data of the patient during surgery under local anesthesia to generate a grip strength change curve; monitors the skin conductivity data of the patient during surgery under local anesthesia to generate a skin conductance fluctuation curve; and evaluates the overall pain experience under local anesthesia through the peak-end rule based on the grip strength change curve and the skin conductance fluctuation curve. Thus, by monitoring grip strength and skin conductance and combining the peak-end rule, the subjective pain experience can be converted into objective quantitative data, reducing the limitations of relying on the patient's subjective feedback. Grip strength and skin conductance can be collected in real time, and the patient's pain fluctuation can be captured instantly, so that the anesthesiologist can adjust the analgesic measures in time when necessary and optimize the patient's surgical experience. The generation of the peak-end rule score can provide a basis for optimizing the local anesthesia effect. By summarizing the pain fluctuation rules of different patients, the anesthesiologist can more reasonably adjust the dosage of local anesthetics or increase auxiliary analgesic measures, thereby improving the local anesthesia effect in subsequent operations. The postoperative pain score helps to review the patient's pain changes during surgery and help doctors provide more accurate suggestions for postoperative analgesia.

[0054] According to some embodiments, the evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes:

[0055] The overall pain experience under local anesthesia is evaluated based on the grip force change curve and the skin conductance fluctuation curve combined with the DTW distance measurement algorithm through the peak-end rule.

[0056] It is understandable that the DTW (Dynamic Time Warping) distance measurement algorithm is introduced to align the grip force change curve and the skin conductance fluctuation curve to more accurately extract the "peak value" and "end value" required by the peak-end rule. The introduction of DTW helps solve the problem of asynchrony between the two curves in the time dimension, thereby being able to more accurately measure the pain experience.

[0057] Exemplarily, during local anesthesia surgery, grip force and skin conductance data are collected in real time to form a grip force change curve and a skin conductance fluctuation curve. The collected data is standardized to eliminate dimensional differences and smooth the data to a uniform scale to ensure that there is no deviation due to different numerical ranges when the DTW algorithm is processed. The grip force change curve and the skin conductance fluctuation curve are time-aligned by the DTW algorithm, and the shortest matching path of the two curves at different time points is calculated. Specifically, DTW obtains the alignment path by minimizing the following distance:

[0058]

[0059] Among them, G(i) and S(j) represent the values ​​of the grip force curve and the skin conductance curve at the i-th and j-th time points, respectively, and path is the path with the shortest distance among all possible paths. Each point on the DTW path represents the matching point of grip force and skin conductance, which reflects the dynamic fluctuations of the patient's pain. DTW allows synchronous alignment data to be obtained even if the two curves are misaligned in time, laying the foundation for the subsequent peak and end value extraction. In the grip force and skin conductance data after DTW alignment, the peak on the matching path is selected, that is, the combination of the highest points in grip force and skin conductance. This combination point can more realistically reflect the patient's highest pain during the operation. Similarly, in the aligned data, the combination of grip force and skin conductance values ​​at the end of the operation is selected as the end value. This value reflects the patient's pain state when the operation is about to end, and is an important parameter for calculating the overall pain experience. Combined with the peak and end values ​​after DTW alignment, the weighted score is calculated according to the peak-end rule to obtain the overall pain experience score:

[0060] P score =w p Peak DTW +w e ·End DTW

[0061] Among them, Peak DTW The peak values ​​of grip force and skin conductance after DTW alignment. DTW is the end value after alignment. p and w e is the corresponding weight, which is used to adjust the impact of the peak and end values ​​on the overall score. Through the DTW alignment analysis, we can more accurately locate the peak and end values ​​in the grip force and skin conductance fluctuations, reducing the error caused by time misalignment. This helps to accurately capture the patient's pain fluctuations.

[0062] It is understandable that DTW eliminates the misalignment of the grip force and skin conductance curves in the time dimension, making the extraction of peak and end values ​​more accurate. The peak-end rule score can help doctors understand the patient's overall pain experience, which is helpful for the adjustment of personalized anesthesia plans and postoperative pain management. Suppose a patient's grip force curve has two significant peaks during surgery, and the skin conductance also peaks in the late intraoperative period. If DTW is not applied, the peaks of grip force and skin conductance may not correspond due to time misalignment. But through DTW, we can synchronize these data to obtain more representative peak and end values, so as to more accurately calculate the pain score and provide more reliable data support for clinical practice.

[0063] In some examples, the property information includes shape and texture, and the overall pain experience under local anesthesia is evaluated by the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve in combination with the DTW distance measurement algorithm, including:

[0064] Calculate the DTW distance between the grip force change curve and the skin conductance fluctuation curve to analyze the matching path between the grip force change curve and the skin conductance fluctuation curve;

[0065] From the matching paths calculated by DTW, the local maxima of grip force and skin conductivity were extracted to determine the pain peak and its associated peak time interval;

[0066] From the matching paths calculated by DTW, the end values ​​of the grip force data and the skin conductivity and their associated end time intervals are determined;

[0067] The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

[0068] It is understandable that when patients experience pain, both grip strength and skin conductivity will change, and the shape, fluctuation intensity and peak value of these changes can be used as effective information for evaluating the patient's pain experience. The DTW algorithm dynamically aligns the two curves so that the peak and end values ​​of grip strength and skin conductance data can be aligned and matched in time, thereby optimizing the overall evaluation.

[0069] Exemplarily, grip force and skin conductance data are collected in real time during surgery, and their changes over time are recorded to form a grip force change curve and a skin conductance fluctuation curve. The DTW algorithm is used to calculate the distance between the grip force curve and the skin conductance curve, and a matching path is generated (the points on the path represent the best match of the two curves in time). The curve shape and feature information analyzed by DTW enable us to understand how grip force and skin conductance in different time periods affect each other, reflecting the patient's response to pain stimulation. DTW aligns the smooth and fluctuating parts of the curve, which can be understood as the change in the texture of the curve corresponding to different intensities of pain experience. From the DTW matching path, the local maximum points of the grip force curve and the skin conductance curve are identified. These local maxima are the "pain peaks", representing the highest pain experienced by the patient during surgery. Through the time information of the DTW matching path, the time interval where the local maximum of grip force and skin conductance is located is found. The determination of this interval can reflect the duration of the patient's high pain and is an important reference for subsequent pain experience analysis. Assuming that during the operation, the grip force curve has a peak at 5 minutes and the skin conductance reaches a peak at 5 minutes and 30 seconds. DTW can align these two peaks and identify them as the "pain peak" in a time interval, thereby more accurately reflecting the patient's pain state at this time. The grip force and skin conductance data of the last stage of the operation are found through the DTW matching path, and the data points of this stage are used as the "end value" to represent the patient's pain state at the end of the operation. From the DTW matching path, find the time interval corresponding to the end value to ensure the data synchronization and representativeness of the end state. For example, within 3 minutes before the end of the operation, the fluctuations of the grip force and skin conductance curves tend to be stable. Through DTW alignment analysis, we can determine the end value in this period to reflect the patient's pain relief state at the end of the operation. Weighted calculation based on peak and end values: According to the peak-end rule, the pain peak and end value of grip force and skin conductance are weighted to obtain the overall pain score. The calculation formula is:

[0070] P score =w p Peak DTW +w e ·End DTW

[0071] Among them, Peak DTW The pain peak in the DTW matching path. DTW The end value in the matching path. p and w e is the weight used to balance the impact of peak and end values ​​in the score. scoreIt represents the overall pain experience during the operation, and is used to reflect the patient's highest pain during the operation and the pain at the end, helping to evaluate the effectiveness of local anesthesia. Assuming that a patient's grip strength and skin conductance peak values ​​are both high, reflecting significant pain in the middle of the operation, while the end value is low, indicating that the pain was relieved at the end of the operation. A moderate score is calculated based on the peak-end rule weighted score, providing data support for postoperative pain management. Combined with the DTW distance measurement algorithm, the pain peak and end value of the grip strength and skin conductance curves can be accurately extracted.

[0072] It is understandable that DTW solves the time asynchrony problem of grip force and skin conductance curves, making the extraction of peak and end values ​​more accurate and reducing the error caused by time offset. The peak-end rule scoring method combines the peak and end values ​​of grip force and skin conductance, and makes the scoring more representative through the alignment of DTW. This scoring can provide reliable data support for the improvement of anesthesia schemes. Through more accurate pain assessment, doctors can better understand the patient's response to local anesthesia, and then formulate pain management plans based on individual differences after surgery to improve the patient's postoperative comfort.

[0073] In some examples, the evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes:

[0074] Divide the surgical procedure into multiple time periods;

[0075] A sliding window is set in each stage to extract statistical features of the grip force change curve and the skin conductance fluctuation curve to form a time series feature matrix, wherein the statistical features are used to indicate the overall fluctuation of the grip force change curve and the skin conductance fluctuation curve in each stage;

[0076] Extracting dynamic features from the time series data in each stage, wherein the dynamic features are used to indicate changes in grip force and skin conductance over time;

[0077] In the data of the high pain category, the peak data of grip force and skin conductance were selected as the pain peak;

[0078] According to the clustering categories, the grip strength and skin conductance data corresponding to the high pain category at the end of the surgery were used as the end value;

[0079] The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

[0080] For example, the clustering results reflect individualized pain responses, not just limited to a certain period of surgery. With the support of dynamic features and clustering results, the peak-to-end score can more accurately reflect the peak and end values ​​of pain, optimizing the overall evaluation effect.

[0081] In some examples, this also includes:

[0082] The generated grip force change curve and skin conductance fluctuation curve were normalized and the data were smoothed to the same scale.

[0083] In some examples, this also includes:

[0084] The isolation forest algorithm or moving average technique was used to eliminate occasional interference data in the grip force data and skin conductivity data.

[0085] It is understandable that during real-time monitoring, grip strength and skin conductance data may be disturbed by environmental factors, sensor noise or accidental movements of the patient, resulting in abnormal fluctuations in the data. To ensure the stability and accuracy of the data. Among them, the isolation forest algorithm is an unsupervised anomaly detection method based on a tree structure. It filters out abnormal data by identifying outliers and is suitable for use in scenarios with irregular fluctuations. The moving average technology is a smoothing method that eliminates random noise by taking the average value of data within a certain window, which is suitable for smoothing short-term fluctuations in data.

[0086] Exemplarily, during a local anesthesia surgery, the patient's grip strength and skin conductance data are collected in real time to form a continuous time series. The isolation forest algorithm generates multiple random binary trees and maps the data points to these trees. Abnormal points are usually separated at the higher levels of the tree (i.e., fewer partitioning steps), making them easier to identify. The grip strength and skin conductance data are input into the isolation forest algorithm to calculate the abnormal score of each data point. According to the preset abnormal threshold, high-scoring abnormal points (i.e., outliers) are filtered out. For example, at a certain moment during the operation, the patient's grip strength suddenly fluctuates due to external noise stimulation, but this fluctuation does not represent a change in pain. The isolation forest algorithm identifies the fluctuation as abnormal data and removes it from the data to ensure the accuracy of pain assessment.

[0087] Exemplarily, the moving average method smoothes the data by calculating the average value of the data within a sliding window, thereby eliminating short-term fluctuations. Appropriate sliding windows (such as 3 seconds or 5 seconds) are set for the grip force and skin conductance curves, respectively, and the average value within the sliding window is calculated to smooth the data into a more continuous curve. For example, during surgery, random noise may occasionally appear in the skin conductance signal, such as the influence of sweat. Through moving average processing, these fluctuations can be smoothed, making the curve more stable and reflecting the true pain trend.

[0088] For example, after the isolation forest algorithm and moving average processing, the data fluctuations of grip force and skin conductance will be more stable, reducing the impact of occasional abnormalities. The generated grip force change curve and skin conductance fluctuation curve can more accurately reflect the patient's real pain fluctuations. After removing outliers and smoothing, continue with the steps of segmentation, peak and end value extraction, and peak-end rule scoring to ensure that the data at each stage is more representative.

[0089] It is understandable that by introducing the isolation forest algorithm and moving average technology, the accuracy and reliability of the data can be effectively improved, and the interference of abnormal data on the score can be reduced: the isolation forest algorithm can automatically detect and eliminate outlier data, making the curves of grip strength and skin conductance smoother, and reducing the interference of occasional noise on the overall score. Moving average technology can effectively remove random fluctuations in the short term, so that the fluctuations of the curve can more clearly reflect the pain trend, thereby improving the interpretability of the data. The cleaned data curve can be more accurately used for segmented analysis, peak and end value extraction, ensuring that the final peak-end rule score is more in line with the actual pain experience. In the scenario of real-time data collection, anomaly detection and smoothing can help the system automatically correct irregular data, ensure the stability of the evaluation results, and provide a more reliable reference for anesthesia adjustments during surgery.

[0090] In some examples, this also includes:

[0091] Obtaining historical medical data of the patient;

[0092] Based on the injury and illness history in the historical medical treatment data, extract the types of pain-related injuries and illnesses experienced by the patient that have theoretical pain data;

[0093] Monitoring the patient's choice of the injury type based on pain perception at different times during surgery under local anesthesia;

[0094] Determining the patient's subjective pain experience based on pain perception at different times based on the selection information;

[0095] The overall pain experience under local anesthesia was evaluated by the peak-end rule based on the patient's subjective pain experience based on pain perception at different times.

[0096] It is understandable that, combined with the patient's historical injury data, the types of injuries with known pain intensity that the patient has experienced can be identified as a reference for subjective pain experience. During surgery, patients can describe their pain feelings by selecting these types of injuries, thereby forming a subjective experience time series based on pain recall. Combined with the peak-end rule, the patient's overall pain experience can be quantified more comprehensively and individually.

[0097] Exemplarily, the patient's past injury history, including painful diseases or injuries, is obtained from the patient's electronic health record (EHR) or other medical records. Those injury types that are directly related to pain are screened out, especially those injury types (such as fractures, burns, etc.) that have strong subjective pain and theoretical pain data. For example, a patient's historical records show that he has experienced injuries such as fractures and acute postoperative inflammation. These records will serve as a reference for his subjective pain recall. The extracted injury types are analyzed to obtain the average pain data (such as pain level, 1-10 pain scale) of these injury types. Different injury types are classified according to pain levels to provide a reference for the patient's pain selection during surgery. For example, fractures may be classified as "high pain", while inflammation may be classified as "moderate pain". This classification helps patients choose to describe their pain experience during surgery. During surgery, through interactive devices (such as tablets or key devices), patients are allowed to choose the historical injury type that best matches their current pain at different time points based on their pain feelings. Patients can periodically select the type of injury during the operation, such as selecting "fracture" to indicate that the current pain is similar to the pain they felt when they had a fracture, thereby quantifying the patient's subjective pain experience. For example, when the effect of anesthesia during the operation is weakened, the patient may select "inflammation" as a description of pain, and when the pain intensifies, select "fracture". These choices record the changes in the patient's subjective pain. Time series generation of subjective pain experience: The patient's selection records at different time points during the operation are converted into a time-based subjective pain experience sequence. Based on the known pain intensity of different injury types, the injury type selected by the patient is converted into a numerical pain intensity to form a subjective pain score sequence. For example, at different time points during the operation, when the patient selects "inflammation" or "fracture", the corresponding pain intensity is 5 and 8 respectively, thus generating a pain intensity curve that changes over time. From the subjective pain experience sequence, the patient's highest pain during the operation (peak value) and the pain intensity at the end of the operation (end value) are extracted. Based on the peak-end rule, the peak value and the end value are weighted to calculate the overall subjective pain experience score.

[0098] It is understandable that based on the patient's historical pain experience, combined with his or her subjective description of pain during surgery, the overall pain assessment is more in line with individual differences, avoiding errors that may occur by relying solely on physiological data. Patients make choices based on their actual pain perception during surgery, forming a dynamic subjective pain experience curve, which provides a reference for real-time pain management. The peak-end rule scoring can accurately reflect the patient's subjective pain fluctuations during surgery, supporting personalized anesthesia adjustments and postoperative pain management. Traditional pain assessment usually relies on physiological data and may ignore individual differences in patients. After introducing subjective experience information, the evaluation results are more comprehensive and reliable, which helps to accurately predict patients' postoperative pain.

[0099] See also Figure 2 , an embodiment of the pain assessment device under local anesthesia in the embodiment of the present application may include:

[0100] A first monitoring unit 21 is used to monitor the grip strength data of a patient during surgery under local anesthesia to generate a grip strength change curve;

[0101] A second monitoring unit 22, used to monitor the skin conductivity data of the patient during surgery under local anesthesia to generate a skin conductance fluctuation curve;

[0102] The evaluation unit 23 is used to evaluate the overall pain experience under local anesthesia based on the grip force change curve and the skin conductance fluctuation curve through the peak-end rule.

[0103] In summary, the pain assessment device under local anesthesia provided by the embodiment of the present application generates a grip force change curve by monitoring the grip force data of the patient during surgery under local anesthesia; generates a skin conductance fluctuation curve by monitoring the skin conductivity data of the patient during surgery under local anesthesia; and evaluates the overall pain experience under local anesthesia through the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve. Thus, by monitoring grip force and skin conductance and combining the peak-end rule, the subjective pain experience can be converted into objective quantitative data, reducing the limitations of relying on the patient's subjective feedback. Grip force and skin conductance can be collected in real time, and the patient's pain fluctuation can be captured instantly, so that the anesthesiologist can adjust the analgesic measures in time when necessary and optimize the patient's surgical experience. The generation of the peak-end rule score can provide a basis for optimizing the local anesthesia effect. By summarizing the pain fluctuation rules of different patients, the anesthesiologist can more reasonably adjust the dosage of local anesthetics or increase auxiliary analgesic measures, thereby improving the local anesthesia effect in subsequent operations. The postoperative pain score helps to review the patient's pain changes during surgery and help doctors provide more accurate suggestions for postoperative analgesia.

[0104] like Figure 3 As shown, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any one of the above-mentioned methods for pain assessment under local anesthesia are implemented:

[0105] Monitor the grip strength data of patients during surgery under local anesthesia to generate a grip strength change curve;

[0106] Monitor skin conductivity data of patients during surgery under local anesthesia to generate skin conductance fluctuation curves;

[0107] The overall pain experience under local anesthesia was evaluated based on the grip force change curve and the skin conductance fluctuation curve using the peak-end rule.

[0108] Since the electronic device introduced in this embodiment is a device used to implement a pain assessment device under local anesthesia in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by the technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0109] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation method in the corresponding embodiment:

[0110] Monitor the grip strength data of patients during surgery under local anesthesia to generate a grip strength change curve;

[0111] Monitor skin conductivity data of patients during surgery under local anesthesia to generate skin conductance fluctuation curves;

[0112] The overall pain experience under local anesthesia was evaluated based on the grip force change curve and the skin conductance fluctuation curve using the peak-end rule.

[0113] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the process of pain assessment under local anesthesia in the embodiment.

[0119] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0120] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0125] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for pain assessment under local anesthesia, characterized in that: include: Monitor the grip strength data of patients during surgery under local anesthesia to generate a grip strength change curve; Monitor skin conductivity data of patients during surgery under local anesthesia to generate skin conductance fluctuation curves; The overall pain experience under local anesthesia was evaluated based on the grip force change curve and the skin conductance fluctuation curve using the peak-end rule.

2. The method according to claim 1, characterized in that The method of evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes: The overall pain experience under local anesthesia is evaluated based on the grip force change curve and the skin conductance fluctuation curve combined with the DTW distance measurement algorithm through the peak-end rule.

3. The method according to claim 2, characterized in that The method of evaluating the overall pain experience under local anesthesia based on the grip force change curve and the skin conductance fluctuation curve in combination with the DTW distance measurement algorithm through the peak-end rule includes: Calculate the DTW distance between the grip force change curve and the skin conductance fluctuation curve to analyze the matching path between the grip force change curve and the skin conductance fluctuation curve; From the matching paths calculated by DTW, the local maxima of grip force and skin conductivity were extracted to determine the pain peak and its associated peak time interval; From the matching paths calculated by DTW, the end values ​​of the grip force data and the skin conductivity and their associated end time intervals are determined; The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

4. The method according to claim 1, characterized in that The method of evaluating the overall pain experience under local anesthesia by using the peak-end rule based on the grip force change curve and the skin conductance fluctuation curve includes: Divide the surgical procedure into multiple time periods; A sliding window is set in each stage to extract statistical features of the grip force change curve and the skin conductance fluctuation curve to form a time series feature matrix, wherein the statistical features are used to indicate the overall fluctuation of the grip force change curve and the skin conductance fluctuation curve in each stage; Extracting dynamic features from the time series data in each stage, wherein the dynamic features are used to indicate changes in grip force and skin conductance over time; In the data of the high pain category, the peak data of grip force and skin conductance were selected as the pain peak; According to the clustering categories, the grip strength and skin conductance data corresponding to the high pain category at the end of the surgery were used as the end value; The peak and end pain values ​​were used in a peak-end rule weighted score calculation to determine the overall pain experience.

5. The method according to claim 1, characterized in that Also includes: The generated grip force change curve and skin conductance fluctuation curve were normalized and the data were smoothed to the same scale.

6. The method according to claim 5, characterized in that Also includes: The isolation forest algorithm or moving average technique was used to eliminate occasional interference data in the grip force data and skin conductivity data.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: Obtaining historical medical data of the patient; Based on the injury and illness history in the historical medical treatment data, extract the types of pain-related injuries and illnesses experienced by the patient that have theoretical pain data; Monitoring the patient's choice of the injury type based on pain perception at different times during surgery under local anesthesia; Determining the patient's subjective pain experience based on pain perception at different times based on the selection information; The overall pain experience under local anesthesia was evaluated by the peak-end rule based on the patient's subjective pain experience based on pain perception at different times.

8. A pain assessment device under local anesthesia, characterized in that: include: A first monitoring unit is used to monitor the grip strength data of a patient during surgery under local anesthesia to generate a grip strength change curve; A second monitoring unit is used to monitor the skin conductivity data of the patient during surgery under local anesthesia to generate a skin conductance fluctuation curve; An evaluation unit is used to evaluate the overall pain experience under local anesthesia based on the grip force change curve and the skin conductance fluctuation curve through the peak-end rule.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for assessing pain under local anesthesia as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assessing pain under local anesthesia according to any one of claims 1 to 7 is implemented.

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