A method and device for detecting lower extremity nerves in total hip arthroplasty

Abstract

The application relates to the technical field of clinical medicine, and particularly discloses a lower limb nerve detection method and device in artificial total hip arthroplasty. The method comprises the following steps: acquiring performance state data of a lower limb nerve detection device, monitoring intraoperative environment data of artificial total hip arthroplasty, comprehensively analyzing to obtain a nerve detection accuracy evaluation value; recording electrode pair waveform data, comprehensively analyzing to obtain an electrode pair waveform abnormality evaluation value; correcting a parameter according to the waveform abnormality evaluation to obtain a waveform abnormality evaluation threshold value, and performing abnormality feedback. The application provides a lower limb nerve detection method and device in artificial total hip arthroplasty, which can help doctors to identify the existence and degree of nerve injury, help to distinguish different types of nerve injury, and thus formulate a targeted treatment scheme, can accurately locate the nerve injury, and can also monitor the recovery progress of the nerve function, help to timely adjust the treatment scheme, and improve the treatment effect.

Description

Technical Field

[0001] This invention relates to the field of clinical medical technology, specifically to a method and device for detecting lower limb nerves during total hip arthroplasty. Background Technology

[0002] Currently, lower limb nerve detection during total hip arthroplasty is a crucial aspect of clinical medicine and bioinformatics. With technological advancements and increasing demand, providing more refined methods for lower limb nerve detection during total hip arthroplasty has become the norm. Efficient and accurate lower limb nerve detection devices during total hip arthroplasty are of great significance for improving surgical safety and user experience.

[0003] For example, the invention patent with announcement number CN111714339B is a brain-myoelectric small-world neural network prediction method for human lower limb movement. It detects surface electromyography (EMG) signals and EEG signals in real time, extracts EEG signal feature vectors and surface EMG signal feature vectors, and fuses them to obtain a new brain-myoelectric feature vector O. It simultaneously records the three-dimensional coordinates of human lower limb walking movement, calculates the movement angle of the lower limb joints through human lower limb kinematic modeling methods, and inputs the fused brain-myoelectric feature vector and lower limb joint movement angles into a small-world neural network. The small-world neural network is used to accurately decode the movement and predict the corresponding lower limb joint movement angles.

[0004] For example, the invention patent with announcement number CN112754468B is a method for detecting and recognizing human lower limb movement based on multi-source signals, including the following steps: acquiring human lower limb movement information through multi-source signal sensing technology, and obtaining the human lower limb movement state through the human lower limb movement information; collecting and preprocessing multi-source signals to obtain input signals for recognition; dividing the input signals into training data and test data, and training and detecting classification and recognition using SVM classifier and SVM-based BP neural network methods respectively, to obtain recognition results; optimizing the accuracy output of the recognition results to obtain the final recognition result.

[0005] However, in the process of implementing the technical solutions of the present invention, it was found that the above-mentioned technology has at least the following technical problems: At present, there is a lack of methods for monitoring sciatic nerve injury during total hip arthroplasty, and it is difficult to ensure that the patient's pain is minimized. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and device for detecting lower limb nerves during total hip arthroplasty, which can effectively solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The first aspect of the present invention provides a method for lower limb nerve detection during total hip arthroplasty, comprising: acquiring performance status data of a lower limb nerve detection device, monitoring intraoperative environmental data of total hip arthroplasty, comprehensively analyzing to obtain a nerve detection accuracy assessment value, and matching waveform abnormality assessment correction parameters according to the nerve detection accuracy assessment value.

[0009] The neural electrodes are deployed to provide periodic weak current stimulation, and the waveform data of the electrode pairs are recorded. The comprehensive analysis yields the evaluation value of the abnormal waveform of the electrode pairs.

[0010] The waveform anomaly assessment threshold is obtained by processing the waveform anomaly assessment correction parameters, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. Anomaly feedback is then given based on the comparison result.

[0011] Optionally, the comprehensive analysis yields an accuracy assessment value for nerve detection. The specific analysis process involves processing the performance status data of the lower limb nerve detection device to obtain the device accuracy assessment index.

[0012] Based on the equipment accuracy assessment index and intraoperative environmental data, a comprehensive analysis was conducted to obtain the accuracy assessment value for nerve detection.

[0013] Optionally, the matching process to obtain waveform abnormality assessment correction parameters involves inputting the nerve detection accuracy assessment values ​​into the lower limb nerve database to match and obtain waveform abnormality assessment correction parameters corresponding to each nerve detection accuracy assessment value range.

[0014] Optionally, the performance status data of the lower limb nerve detection device is processed to obtain the device accuracy evaluation index. The specific processing procedure is as follows: the performance status data of the lower limb nerve detection device includes the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval.

[0015] The critical amplifier channel number, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current, and critical adjacent maintenance interval duration are extracted from the lower limb nerve database, and a comprehensive analysis is performed to obtain the equipment accuracy evaluation index.

[0016] Optionally, the comprehensive analysis yields an abnormality assessment value for the electrode pair waveform. The specific analysis process is as follows: the electrode pair waveform data includes the waveform frequency, peak value, and trough value of each electrode pair waveform within the monitoring period.

[0017] The standard electrode pair waveform data is obtained by processing the electrode pair waveform data.

[0018] The permissible deviation waveform frequency, permissible deviation peak value, and permissible deviation trough value are extracted from the lower limb nerve database, and the abnormality assessment value of the electrode pair waveform is obtained by comprehensive analysis.

[0019] Optionally, the electrode pair waveform data is processed to obtain standard electrode pair waveform data. The specific processing procedure is as follows: the average value of the waveform frequency, peak value and trough value of each electrode pair waveform is obtained and marked as the reference standard waveform frequency, reference standard peak value and reference standard trough value, respectively.

[0020] Optionally, the waveform anomaly assessment threshold is obtained by processing the waveform anomaly assessment correction parameters, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. Anomaly feedback is given based on the comparison result. The specific analysis process is as follows: a preset initial waveform anomaly assessment threshold is extracted from the lower limb nerve database, the initial waveform anomaly assessment threshold is added to the waveform anomaly assessment correction parameters to obtain the waveform anomaly assessment threshold, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. If the waveform anomaly assessment value is greater than or equal to the waveform anomaly assessment threshold, an anomaly warning is immediately issued. If the waveform anomaly assessment value is less than the waveform anomaly assessment threshold, no additional operation is performed.

[0021] Optionally, the abnormal feedback also includes: deploying waveform acquisition points within the monitoring period, acquiring and comparing the spatial positions of the waveforms at each acquisition point, statistically analyzing the most frequently occurring spatial positions of the waveforms and marking them as high-frequency points, outputting a standard waveform based on the waveform frequency, peak value, trough value, and high-frequency point, and issuing a prompt sound to remind the operator of the waveform abnormality if the difference in any one of the waveform frequency, peak value, trough value, or high-frequency point exceeds 50%.

[0022] Optionally, the specific numerical expression for the neural detection accuracy assessment value is as follows:

[0023]

[0024] Wherein, Ta represents the neural detection accuracy assessment value, Ma represents the equipment accuracy assessment index, e represents the natural constant, S represents the light intensity, S0 represents the reference standard light intensity, ΔS represents the allowable deviation light intensity, z represents the noise intensity, z0 represents the critical noise intensity, ρ1 represents the neural detection accuracy assessment influence factor corresponding to the set equipment accuracy assessment index, ρ2 represents the neural detection accuracy assessment influence factor corresponding to the set light intensity, and ρ3 represents the neural detection accuracy assessment influence factor corresponding to the set noise intensity.

[0025] A second aspect of the present invention provides a lower limb nerve detection device during total hip arthroplasty, comprising: an electrode cap for controlling the electrode and preventing bacterial infection.

[0026] Electrical limit switches are used to restrict joint movement to protect nerves from damage, allowing electrodes to be applied to the skin in a flat shape.

[0027] Electrode wires are used to transmit the neural signals captured by the electrodes to the monitoring instrument for analysis and display.

[0028] Electrode needles are inserted into specific nerves or muscle tissue. By stimulating the electrode needles and observing the patient's response, the location of the nerve can be determined, and the functional status of the nerve can be monitored in real time.

[0029] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0030] The above approach can help doctors identify the presence and extent of nerve damage, help distinguish different types of nerve damage, and thus develop targeted treatment plans. It can more accurately locate nerve damage, monitor the progress of nerve function recovery, and help adjust the treatment plan in a timely manner to improve treatment effectiveness.

[0031] By evaluating the device's accuracy assessment index, we can ensure that the device can accurately detect and analyze lower limb nerve signals in practical applications. This helps doctors to more accurately judge the patient's neurological condition, develop more effective treatment plans, and also identify potential defects or deficiencies in the device's performance, thereby taking targeted optimization measures to provide patients with higher quality medical services.

[0032] By evaluating the accuracy of neurological tests, the patient's neurological function can be more accurately reflected, thereby reducing inappropriate or delayed treatment due to misdiagnosis or missed diagnosis. This helps doctors develop more precise treatment plans for patients, improve treatment outcomes, reduce treatment risks, and further enhance patients' trust and satisfaction with medical services. Attached Figure Description

[0033] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0035] Figure 2 The device diagram of the present invention includes an electrode cap 10, an electrical limit switch 11, an electrode wire 12, and an electrode needle 13.

[0036] Figure 3 This is a schematic diagram illustrating the functional relationship between the device accuracy evaluation index and the number of amplifier channels in this invention. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Reference Figure 1 As shown, the first aspect of the present invention provides a method for lower limb nerve detection during total hip arthroplasty, comprising: acquiring performance status data of a lower limb nerve detection device, monitoring intraoperative environmental data of total hip arthroplasty, comprehensively analyzing to obtain a nerve detection accuracy assessment value, and matching waveform abnormality assessment correction parameters according to the nerve detection accuracy assessment value.

[0039] The neural electrodes are deployed to provide periodic weak current stimulation, and the waveform data of the electrode pairs are recorded. The comprehensive analysis yields the evaluation value of the abnormal waveform of the electrode pairs.

[0040] The waveform anomaly assessment threshold is obtained by processing the waveform anomaly assessment correction parameters, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. Anomaly feedback is then given based on the comparison result.

[0041] Specifically, a comprehensive analysis yields an accuracy assessment value for nerve detection. The specific analysis process involves processing the performance status data of the lower limb nerve detection equipment to obtain the equipment accuracy assessment index.

[0042] Based on the equipment accuracy assessment index and intraoperative environmental data, a comprehensive analysis was conducted to obtain the accuracy assessment value for nerve detection.

[0043] The performance status data of the lower limb nerve detection device is processed to obtain the device accuracy evaluation index. The specific processing procedure is as follows: the performance status data of the lower limb nerve detection device includes the number of amplifier channels, operating voltage, operating current and adjacent maintenance interval.

[0044] The critical amplifier channel number, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current, and critical adjacent maintenance interval duration are extracted from the lower limb nerve database, and a comprehensive analysis is performed to obtain the equipment accuracy evaluation index.

[0045] In a specific embodiment, the number of amplifier channels typically refers to the number of signal channels that the device can process simultaneously. Multi-channel devices can process multiple signals simultaneously, thereby improving detection efficiency. For complex lower limb nerve detection tasks, multi-channel devices can capture more comprehensive data and improve diagnostic accuracy. This information can be found in the device manual. The operating voltage is the power supply voltage required for the device to operate normally, and the operating current is the current consumed by the device during operation. Appropriate operating voltage and current can ensure stable operation of the device, reduce signal distortion and noise interference, thereby improving detection accuracy. The operating voltage and operating current can be obtained through electrical testing instruments. The adjacent maintenance interval refers to the interval between the device's adjacent maintenance time point and the current time point. Maintenance can promptly detect and handle potential problems of the device, avoid failures, and thus improve the stability and reliability of the device. This information can be found in the device maintenance log.

[0046] Specifically, the equipment accuracy assessment index has the following numerical expression:

[0047]

[0048] Where Ma represents the equipment accuracy assessment index, B represents the number of amplifier channels, B0 represents the critical number of amplifier channels, U represents the operating voltage, U0 represents the reference standard operating voltage, ΔU represents the allowable deviation operating voltage, I represents the operating current, I0 represents the reference standard operating current, ΔI represents the allowable deviation operating current, e represents the natural constant, t represents the adjacent maintenance interval duration, t0 represents the critical adjacent maintenance interval duration, ω1 represents the equipment accuracy assessment influence factor corresponding to the set number of amplifier channels, ω2 represents the equipment accuracy assessment influence factor corresponding to the set operating voltage, ω3 represents the equipment accuracy assessment influence factor corresponding to the set operating current, and ω4 represents the equipment accuracy assessment influence factor corresponding to the set adjacent maintenance interval duration.

[0049] This embodiment's algorithm combines the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval duration to comprehensively analyze and obtain a device accuracy evaluation index. Increasing the number of channels increases the device's power consumption, thus requiring a higher operating voltage to provide sufficient power. At the same time, with the increase in the number of channels, the device requires more operating current to support the simultaneous operation of multiple channels. Under certain resistance conditions, an increase in operating voltage will lead to an increase in operating current. Furthermore, if the device operates under high voltage and high current conditions for a long time, it may cause problems such as circuit aging and component damage, thereby shortening the maintenance interval duration. Comprehensive analysis can yield a more comprehensive device accuracy evaluation index.

[0050] like Figure 3As shown, in a specific embodiment, B0 = 16, U = 90V, U0 = 100V, ΔU = 10V, I = 105mA, I0 = 120mA, ΔI = 15mA, t0 = 30 days, ω1 = ω2 = 0.3, ω3 = ω4 = 0.4. When t = 6, the functional relationship between the equipment accuracy evaluation index and the number of amplifier channels is shown as curve a; when t = 9, the functional relationship between the equipment accuracy evaluation index and the number of amplifier channels is shown as curve b; when t = 15, the functional relationship between the equipment accuracy evaluation index and the number of amplifier channels is shown as curve c.

[0051] It should be explained that this embodiment considers four key factors: the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval. Reasonable settings for operating voltage and current can avoid problems such as overheating and overload, extending the equipment's lifespan. Stable voltage and current supply ensures normal operation under various environments. A reasonable maintenance plan can extend the equipment's lifespan and reduce the frequency of replacement. Multi-channel equipment can provide more comprehensive test data, helping doctors make more accurate diagnoses. Stable equipment reduces uncertainty and errors during the testing process, increasing patient trust in the results. Reasonable maintenance intervals and convenient maintenance procedures reduce patient waiting time, improve testing efficiency, and thus enhance overall patient satisfaction. Standardizing the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval ensures they are compared on the same order of magnitude, improving the fairness and comparability of the evaluation. Furthermore, the settings of B0, U0, I0, and t0 help avoid equipment overload, ensuring the accuracy and stability of signal processing, protecting the equipment from damage caused by voltage fluctuations and current overload, ensuring timely maintenance and upkeep, and preventing safety issues caused by equipment aging or damage. By weighting the effects of the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval, the relative importance of these factors in the evaluation index is reflected. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the larger the number of amplifier channels, or the smaller the operating voltage deviation, or the smaller the operating current deviation, or the shorter the adjacent maintenance interval, the higher the equipment accuracy evaluation index. Evaluating the equipment accuracy evaluation index ensures that the equipment can accurately detect and analyze lower limb nerve signals in practical applications. This helps doctors more accurately assess the patient's neurological condition, develop more effective treatment plans, and identify potential defects or deficiencies in equipment performance. Targeted optimization measures can then be taken to further ensure that the equipment remains in optimal condition, providing patients with higher quality medical services, enhancing patient confidence, and improving treatment outcomes and satisfaction.

[0052] In a specific embodiment, the values ​​of the equipment accuracy assessment influence factors corresponding to the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval are between 0 and 1, representing the numerical values ​​of the degree of influence of the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval on the equipment accuracy assessment index. Each equipment accuracy assessment influence factor can be obtained from the lower limb nerve database. By adjusting the values ​​of the influence factors, the degree of influence of different factors on the final equipment accuracy assessment index can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval form a mapping set with the weight factors corresponding to the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval preset in the lower limb nerve database. The real-time number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval are substituted into the mapping set to obtain the weight factors corresponding to the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval. The mapping relationship can be one-to-one or many-to-one.

[0053] In one specific embodiment, the device accuracy evaluation index is an important indicator for measuring the performance of the device. High-precision devices can more accurately reflect the patient's lower limb nerve function status and provide doctors with reliable diagnostic basis. Light intensity and noise intensity refer to the light intensity and noise level of the environment in which the lower limb nerve detection device is located. Appropriate light intensity can ensure the brightness and clarity of the detection environment, which helps doctors to more accurately observe and analyze the patient's lower limb nerve function status. A low noise intensity environment can reduce the interference of external factors on the detection results and improve the accuracy and reliability of the detection. Light intensity can be measured by a portable illuminance meter, and noise intensity can be measured by a sound level meter.

[0054] Specifically, the numerical expression for the accuracy assessment value of neural testing is as follows:

[0055]

[0056] Wherein, Ta represents the neural detection accuracy assessment value, Ma represents the equipment accuracy assessment index, e represents the natural constant, S represents the light intensity, S0 represents the reference standard light intensity, ΔS represents the allowable deviation light intensity, z represents the noise intensity, z0 represents the critical noise intensity, ρ1 represents the neural detection accuracy assessment influence factor corresponding to the set equipment accuracy assessment index, ρ2 represents the neural detection accuracy assessment influence factor corresponding to the set light intensity, and ρ3 represents the neural detection accuracy assessment influence factor corresponding to the set noise intensity.

[0057] This embodiment's algorithm combines the device accuracy assessment index, light intensity, and noise intensity to comprehensively analyze and obtain a neural network detection accuracy assessment value. Light intensity has a certain impact on the detection accuracy of the device. If the light is too strong or too weak, it may cause deviations in the signals received by the device's sensors, thereby affecting the device accuracy assessment index. At the same time, excessive noise intensity will interfere with the signals received by the device's sensors, leading to deviations in the detection results, and thus affecting the device accuracy assessment index. Comprehensive analysis can yield a more comprehensive, accurate, and in-depth neural network detection accuracy assessment value.

[0058] Table 1. Examples of data for the accuracy assessment of neurological testing.

[0059]

[0060]

[0061] As shown in Table 1, the accuracy assessment value of neural detection is jointly determined by the device accuracy assessment index, light intensity, and noise intensity. In a specific embodiment, the reference standard light intensity is 100 lx, the allowable deviation light intensity is 10 lx, and the critical noise intensity is 38 dB. The set device accuracy assessment index corresponds to a neural detection accuracy assessment influence factor of 0.4, the set light intensity corresponds to a neural detection accuracy assessment influence factor of 0.3, and the set noise intensity corresponds to a neural detection accuracy assessment influence factor of 0.3. This formula considers three key factors: device accuracy assessment index, light intensity, and noise intensity. It can reduce misdiagnosis caused by device errors, improve the accuracy and reliability of diagnosis, and help improve the hospital's medical level and competitiveness. At the same time, appropriate light intensity can help the device capture neural signals more clearly, improve the signal recognition rate, and thus enhance the accuracy of detection. In a low-noise environment, the device can receive clearer neural signals, improve signal quality, and thus enhance the accuracy of detection. By standardizing the equipment accuracy assessment index, light intensity, and noise intensity, comparisons are ensured to be made on the same order of magnitude, improving the fairness and comparability of the assessment. Simultaneously, the setting of S0 ensures that the ambient light is neither too strong nor too weak, thus avoiding interference from light on the detection results. The setting of z0 limits the interference of environmental noise on the detection equipment, reducing misdiagnosis or missed diagnosis caused by noise. Weighting the impact of the equipment accuracy assessment index, light intensity, and noise intensity reflects their relative importance in the assessment index. The weights of different factors can be adjusted according to different needs, making the model highly adaptable. It is evident that the larger the equipment accuracy assessment index, or the smaller the light intensity deviation, or the smaller the noise intensity deviation, the higher the accuracy assessment value of the neurological detection. Evaluating the accuracy assessment value of neurological detection can more accurately reflect the patient's neurological function status, thereby reducing inappropriate or delayed treatment due to misdiagnosis or missed diagnosis. This helps doctors develop more precise treatment plans for patients, and accurate neurological detection can reduce the likelihood of patients undergoing unnecessary examinations.

[0062] In a specific embodiment, the values ​​of the neural detection accuracy assessment influencing factors corresponding to the device accuracy assessment index, light intensity, and noise intensity range from 0 to 1, representing the degree of influence of the device accuracy assessment index, light intensity, and noise intensity on the neural detection accuracy assessment value. Each neural detection accuracy assessment influencing factor can be obtained from the lower limb nerve database. By adjusting the values ​​of the influencing factors, the degree of influence of different factors on the final neural detection accuracy assessment value can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, the device accuracy assessment index, light intensity, and noise intensity form a mapping set with the pre-set weighting factors corresponding to the device accuracy assessment index, light intensity, and noise intensity in the lower limb nerve database. The real-time device accuracy assessment index, light intensity, and noise intensity are substituted into the mapping set to obtain the weighting factors corresponding to the device accuracy assessment index, light intensity, and noise intensity. The mapping relationship can be one-to-one or many-to-one.

[0063] Among them, the waveform abnormality assessment correction parameters are obtained through matching. The specific matching process is as follows: the accuracy assessment value of nerve detection is input into the lower limb nerve database to match the waveform abnormality assessment correction parameters corresponding to each nerve detection accuracy assessment value range.

[0064] Specifically, the comprehensive analysis yields the abnormal waveform assessment value of the electrode pair. The specific analysis process is as follows: the waveform data of the electrode pair includes the waveform frequency, peak value, and trough value of each electrode pair waveform within the monitoring period.

[0065] The standard electrode pair waveform data is obtained by processing the electrode pair waveform data.

[0066] The waveform frequency, peak value, and trough value of each electrode pair are summed and averaged to obtain the reference standard waveform frequency, reference standard peak value, and reference standard trough value.

[0067] The permissible deviation waveform frequency, permissible deviation peak value, and permissible deviation trough value are extracted from the lower limb nerve database, and the abnormality assessment value of the electrode pair waveform is obtained by comprehensive analysis.

[0068] In a specific embodiment, waveform frequency can reflect the conduction velocity of nerve impulses and the excitability of neurons. By measuring waveform frequency, the integrity of nerve fibers and whether nerve impulse conduction is normal can be determined. The peak value represents the intensity or amplitude of the nerve signal and is an important indicator for assessing neuronal excitability and nerve fiber integrity. Changes in the peak value can intuitively reflect changes in the intensity of the nerve signal. The trough value reflects the degree of attenuation or inhibition of the nerve signal during conduction. By measuring the trough value, the degree of nerve fiber damage and interfering factors in the nerve impulse conduction process can be understood. This value can be obtained through electrodes and neurophysiological instruments.

[0069] It should be explained that there are six pairs of electrodes in this embodiment, labeled as: A1, A2, B1, B2, C1, C2, D1, D2, E1, E2, F1, and F2. The specific locations are as follows: Electrodes A1 and A2 are femoral nerve stimulation electrodes; electrode A1 is inserted 5cm lateral to the midpoint of the inguinal ligament, and electrode A2 is inserted 5cm medial to the midpoint of the inguinal ligament; B1 and B2 are sciatic nerve stimulation electrodes; B1 is inserted at the outer 1 / 5 of the line connecting the posterior superior iliac spine and the ischial tuberosity, and B2 is inserted at the inner 1 / 5 of the line connecting the posterior superior iliac spine and the ischial tuberosity; C1 and C2 are rectus femoris muscle electrodes; C1 is inserted 10cm directly above the patella, and C2 is inserted 20cm directly above the patella; D1 and D2... For the vastus lateralis muscle electrode, insert D1 10cm lateral to the midline 20cm directly above the patella, and insert D2 10cm medial to the midline 20cm directly above the patella; for the gastrocnemius muscle electrode, insert E1 3cm to the left of the midline 8cm directly below the popliteal fossa, and insert E2 3cm to the right of the midline 8cm directly below the popliteal fossa; for the tibialis anterior muscle electrode, insert F1 1.5cm lateral to the anterior tibial border 10cm directly below the patella, and insert F2 1.5cm lateral to the anterior tibial border 20cm directly below the patella. Pinch the electrode cap and insert the electrode into the thickest part of the four muscles: rectus femoris, gastrocnemius, tibialis anterior, and flexor pollicis. Make sure the electrode limit device is in close contact with the skin. Then, hold the electrode limit device and bend the electrode cap to the side. The connection between the electrode cap and the electrode limit device will break. Then, discard the electrode cap. The electrode will then be in a flat position on the skin. At this point, you can cover the electrode with a commercially available surgical film to avoid interference with surgical sterilization caused by the presence of the electrode.

[0070] The specific numerical expression for the electrode pair waveform anomaly assessment value is as follows:

[0071]

[0072] Where Wd represents the waveform anomaly assessment value of the electrode pair, e represents the natural constant, and f i The waveform frequency of the i-th electrode pair is represented by f0, the reference standard waveform frequency is represented by Δf, and the allowable deviation waveform frequency is represented by T. i The peak value of the i-th electrode pair is represented by T0, the peak value of the reference standard is represented by ΔT, and the peak value of the allowable deviation is represented by D. iThe trough value represents the value of the i-th electrode pair, D0 represents the reference standard trough value, ΔD represents the allowable deviation trough value, β1 represents the electrode pair waveform anomaly assessment influence factor corresponding to the set waveform frequency, β2 represents the electrode pair waveform anomaly assessment influence factor corresponding to the set peak value, β3 represents the electrode pair waveform anomaly assessment influence factor corresponding to the set trough and peak values, i represents the electrode pair number, i = 1, 2, 3, ..., m, m represents the total number of electrode pairs, m = 5, namely electrode pairs A1A2, B1B2, C1C2, D1D2, E1E2 and F1F2.

[0073] This embodiment's algorithm combines the waveform frequency, peak value, and trough value of each electrode pair to comprehensively analyze and obtain an abnormal assessment value for the electrode pair waveform. Under normal circumstances, an increase in waveform frequency is usually accompanied by an increase in peak value, because as the conduction speed of nerve impulses increases, the intensity of nerve signals also increases accordingly. However, in some pathological states, an increase in waveform frequency is not accompanied by an increase in peak value, and may even show a decrease in peak value. This is due to damage to nerve fibers or a decrease in neuronal excitability. There is usually no direct linear relationship between waveform frequency and trough value. However, in some cases, such as when nerve fibers are damaged or nerve impulse conduction is blocked, a decrease in waveform frequency is accompanied by an increase in trough value. This reflects an increase in the degree of attenuation or inhibition of nerve signals during conduction. Comprehensive analysis can obtain a more comprehensive, accurate, and in-depth assessment value for abnormal electrode pair waveforms.

[0074] It should be explained that this embodiment considers three key factors: the waveform frequency, peak value, and trough value of each electrode pair. This allows for the timely detection of abnormal changes in neurological function, aiding in early disease diagnosis and significantly contributing to patient treatment and rehabilitation. Early intervention can prevent disease progression and provide doctors with a scientific basis for developing personalized treatment plans. By standardizing the waveform frequency, peak value, and trough value of each electrode pair, comparisons are ensured to be made on the same order of magnitude, improving the fairness and comparability of the assessment. Furthermore, the settings of f0, T0, and D0 can be used to assess whether the actual detected waveform frequency, peak value, and trough value are within the normal range. Weighting the influence of each electrode pair on the waveform frequency, peak value, and trough value reflects their relative importance in the assessment index. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the smaller the deviation in waveform frequency, peak value, or trough value, the greater the abnormal assessment value of the electrode pair on the waveform. By evaluating abnormal waveform values ​​using electrodes, doctors can identify the presence and extent of nerve damage. For example, abnormal waveform frequency may indicate a slowdown in nerve conduction velocity, while abnormal peak or trough values ​​may reflect a weakening or abnormal enhancement of nerve signals. This helps to differentiate between different types of nerve damage, such as complete and incomplete damage, thereby enabling the development of targeted treatment plans. It can also more accurately locate nerve damage and monitor the progress of nerve function recovery, helping to adjust treatment plans in a timely manner to improve treatment outcomes.

[0075] In a specific embodiment, the values ​​of the electrode pair waveform abnormality assessment influence factors corresponding to waveform frequency, peak value, and trough value range from 0 to 1, representing the degree of influence of waveform frequency, peak value, and trough value on the waveform abnormality assessment value of the electrode pair. Each electrode pair waveform abnormality assessment influence factor can be obtained from the lower limb nerve database. By adjusting the values ​​of the influence factors, the degree of influence of different factors on the final waveform abnormality assessment value of the electrode pair can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, waveform frequency, peak value, and trough value form a mapping set with the weight factors corresponding to waveform frequency, peak value, and trough value preset in the lower limb nerve database. The real-time waveform frequency, peak value, and trough value are substituted into the mapping set to obtain the weight factors corresponding to waveform frequency, peak value, and trough value. The mapping relationship can be one-to-one or many-to-one.

[0076] Specifically, the waveform abnormality assessment threshold is obtained by processing the waveform abnormality assessment correction parameters, and the waveform abnormality assessment value is compared with the waveform abnormality assessment threshold. The specific analysis process is as follows: the preset initial waveform abnormality assessment threshold is extracted from the lower limb nerve database, the initial waveform abnormality assessment threshold is added to the waveform abnormality assessment correction parameters to obtain the waveform abnormality assessment threshold, and the waveform abnormality assessment value is compared with the waveform abnormality assessment threshold. If the waveform abnormality assessment value is greater than or equal to the waveform abnormality assessment threshold, an abnormality warning is immediately issued. If the waveform abnormality assessment value is less than the waveform abnormality assessment threshold, no additional operation is performed.

[0077] It should be explained that the abnormal feedback based on the comparison results in this embodiment also includes: deploying waveform acquisition points within the monitoring cycle, acquiring and comparing the spatial positions of waveforms at 1 / 5, 2 / 5, 3 / 5, 4 / 5, and 5 / 5 of each cycle, counting the spatial positions where the waveforms most frequently appear and marking them as high-frequency points, and outputting a standard waveform based on the waveform frequency, peak value, trough value, and high-frequency point. If the generated waveform differs by more than 50% in any of the waveform frequency, peak value, trough value, or high-frequency point, a prompt sound is emitted to remind the operator of the waveform abnormality.

[0078] Reference Figure 2 As shown, the second aspect of the present invention provides a lower limb nerve detection device during total hip arthroplasty, comprising: an electrode cap 10 for controlling the electrode and preventing bacterial infection.

[0079] The electrical limit switch 11 is used to restrict joint movement to protect nerves from damage and allows the electrodes to be applied to the skin in a flat shape.

[0080] Electrode line 12 is used to transmit the neural signals captured by the electrodes to the monitoring instrument for analysis and display.

[0081] Electrode needle 13 is used to insert into specific nerves or muscle tissue. The location of the nerve is determined by stimulating the electrode needle and observing the patient's reaction. It can also monitor the functional status of the nerve in real time.

[0082] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A lower limb nerve detection device during total hip arthroplasty, characterized in that, include: Electrode caps are used to control the electrodes and prevent bacterial infection. Electrical limit switches are used to restrict joint movement to protect nerves from damage, allowing electrodes to be applied to the skin in a flat shape. Electrode wires are used to transmit the neural signals captured by the electrodes to the monitoring instrument for analysis and display; Electrode needles are used to insert into specific nerves or muscle tissue. By stimulating the electrode needles and observing the patient's response, the location of the nerve can be determined, and the functional status of the nerve can be monitored in real time. The detection method of the device includes: The performance status data of the lower limb nerve detection device was acquired, and the intraoperative environmental data of total hip arthroplasty was monitored. The nerve detection accuracy assessment value was obtained by comprehensive analysis, and the waveform abnormality assessment correction parameter was obtained by matching the nerve detection accuracy assessment value. The performance status data of the lower limb nerve detection equipment is processed to obtain the equipment accuracy evaluation index. The specific processing procedure is as follows: the performance status data of the lower limb nerve detection equipment includes the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval duration; the critical number of amplifier channels, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current, and critical adjacent maintenance interval duration are extracted from the lower limb nerve database, and the equipment accuracy evaluation index is obtained through comprehensive analysis. The equipment accuracy assessment index, specifically expressed as follows: Where Ma represents the equipment accuracy assessment index, B represents the number of amplifier channels, B0 represents the critical number of amplifier channels, U represents the operating voltage, U0 represents the reference standard operating voltage, ΔU represents the allowable deviation operating voltage, I represents the operating current, I0 represents the reference standard operating current, ΔI represents the allowable deviation operating current, e represents the natural constant, t represents the adjacent maintenance interval duration, t0 represents the critical adjacent maintenance interval duration, ω1 represents the equipment accuracy assessment influence factor corresponding to the set number of amplifier channels, ω2 represents the equipment accuracy assessment influence factor corresponding to the set operating voltage, ω3 represents the equipment accuracy assessment influence factor corresponding to the set operating current, and ω4 represents the equipment accuracy assessment influence factor corresponding to the set adjacent maintenance interval duration. Deploy neural electrodes to provide periodic weak current stimulation, record electrode pair waveform data, and comprehensively analyze the waveform abnormality assessment value of the electrode pair; The waveform anomaly assessment threshold is obtained by processing the waveform anomaly assessment correction parameters, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. Anomaly feedback is then given based on the comparison result.

2. The lower limb nerve detection device during total hip arthroplasty according to claim 1, characterized in that: The comprehensive analysis yielded an assessment value for the accuracy of neural detection. The specific analysis process is as follows: The performance status data of the lower limb nerve detection equipment is processed to obtain the equipment accuracy evaluation index; Based on the equipment accuracy assessment index and intraoperative environmental data, a comprehensive analysis was conducted to obtain the accuracy assessment value for nerve detection.

3. The lower limb nerve detection device during total hip arthroplasty according to claim 2, characterized in that: The matching process yields waveform anomaly evaluation and correction parameters. The specific matching process is as follows: The accuracy assessment values ​​of nerve detection are input into the lower limb nerve database to obtain waveform abnormality assessment and correction parameters corresponding to each range of accuracy assessment values ​​of nerve detection.

4. The lower limb nerve detection device during total hip arthroplasty according to claim 2, characterized in that: The performance status data of the lower limb nerve detection device is processed to obtain the device accuracy evaluation index. The specific processing procedure is as follows: The performance status data of the lower limb nerve detection device includes the number of amplifier channels, operating voltage, operating current, and adjacent maintenance interval. The critical amplifier channel number, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current, and critical adjacent maintenance interval duration are extracted from the lower limb nerve database, and a comprehensive analysis is performed to obtain the equipment accuracy evaluation index.

5. The lower limb nerve detection device during total hip arthroplasty according to claim 1, characterized in that: The comprehensive analysis yields an evaluation value for the waveform anomaly of the electrode pair. The specific analysis process is as follows: The electrode pair waveform data includes the waveform frequency, peak value, and trough value of each electrode pair waveform within the monitoring period. Standard electrode pair waveform data is obtained by processing the electrode pair waveform data; The permissible deviation waveform frequency, permissible deviation peak value, and permissible deviation trough value are extracted from the lower limb nerve database, and the abnormality assessment value of the electrode pair waveform is obtained by comprehensive analysis.

6. The lower limb nerve detection device during total hip arthroplasty according to claim 5, characterized in that: The waveform data of the electrode pair is processed to obtain the waveform data of the standard electrode pair. The specific processing procedure is as follows: The average values ​​of waveform frequency, peak value, and trough value for each electrode pair are obtained and marked as reference standard waveform frequency, reference standard peak value, and reference standard trough value, respectively.

7. The lower limb nerve detection device during total hip arthroplasty according to claim 5, characterized in that: The waveform anomaly assessment threshold is obtained by processing the waveform anomaly assessment correction parameters, and the waveform anomaly assessment value and the waveform anomaly assessment threshold are compared. Anomaly feedback is then provided based on the comparison result. The specific analysis process is as follows: The preset initial waveform abnormality assessment threshold is extracted from the lower limb nerve database. The initial waveform abnormality assessment threshold is added to the waveform abnormality assessment correction parameter to obtain the waveform abnormality assessment threshold. The waveform abnormality assessment value is compared with the waveform abnormality assessment threshold. If the waveform abnormality assessment value is greater than or equal to the waveform abnormality assessment threshold, an abnormality warning is immediately issued. If the waveform abnormality assessment value is less than the waveform abnormality assessment threshold, no additional operation is performed.

8. The lower limb nerve detection device during total hip arthroplasty according to claim 7, characterized in that: The abnormal feedback process also includes: During the monitoring period, waveform acquisition points are deployed, and the spatial positions of waveforms at each acquisition point are collected and compared. The spatial positions where the waveforms appear most frequently are statistically analyzed and marked as high-frequency points. A standard waveform is output based on the waveform frequency, peak value, trough value, and high-frequency point. If the generated waveform differs by more than 50% in any of the waveform frequency, peak value, trough value, or high-frequency point, an alert sound is emitted to remind the operator of the waveform abnormality.

9. The lower limb nerve detection device during total hip arthroplasty according to claim 2, characterized in that: The specific numerical expression for the neural detection accuracy assessment value is as follows: Wherein, Ta represents the neural detection accuracy assessment value, Ma represents the equipment accuracy assessment index, e represents the natural constant, S represents the light intensity, S0 represents the reference standard light intensity, ΔS represents the allowable deviation light intensity, z represents the noise intensity, z0 represents the critical noise intensity, ρ1 represents the neural detection accuracy assessment influence factor corresponding to the set equipment accuracy assessment index, ρ2 represents the neural detection accuracy assessment influence factor corresponding to the set light intensity, and ρ3 represents the neural detection accuracy assessment influence factor corresponding to the set noise intensity.

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