Method and device for detecting lower limb nerves in total hip arthroplasty

Abstract

The invention relates to the technical field of clinical medicine, and particularly discloses a lower limb nerve detection method and device in artificial total hip replacement. The method comprises the following steps: acquiring performance state data of lower limb nerve detection equipment, monitoring intra-operative environment data of artificial total hip replacement, and comprehensively analyzing to obtain a nerve detection accuracy evaluation value; recording electrode pair waveform data, and comprehensively analyzing to obtain an electrode pair waveform anomaly evaluation value; and processing according to the waveform abnormity evaluation correction parameter to obtain a waveform abnormity evaluation threshold value, and performing abnormity feedback. According to the lower limb nerve detection method and device in the artificial total hip replacement, a doctor can be helped to identify the existence and degree of nerve injuries, different types of nerve injuries can be distinguished, so that a targeted treatment scheme is formulated, the nerve injuries can be accurately positioned, and the treatment efficiency is improved. The recovery progress of the nerve function can be monitored, the treatment scheme can be adjusted in time, and the treatment effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical medicine, and in particular to a method and a device for detecting nerves of lower limbs in total hip replacement surgery. Background Art

[0002] Currently, lower limb nerve detection during total hip replacement surgery is a very important part of clinical medicine and bioinformatics. With the advancement of technology and the growth of demand, providing more refined lower limb nerve detection methods during total hip replacement surgery has become the norm. Efficient and accurate lower limb nerve detection devices during total hip replacement surgery are of great significance to improving the safety of surgery and user experience.

[0003] For example, the invention patent with announcement number CN111714339B is a brain-electromyography fusion small-world neural network prediction method for lower limb movement of the human body. It detects surface electromyography signals and electroencephalogram signals in real time, extracts electroencephalogram signal feature vectors and surface electromyography signal feature vectors, and fuses them to obtain a new brain-muscle feature vector O; it simultaneously records the three-dimensional coordinates of the human lower limb walking movement, and calculates the movement angles of the lower limb joints through the human lower limb kinematic modeling method; it inputs the fused brain-electromyography feature vectors and the lower limb joint movement angles into the small-world neural network, uses the small-world neural network to accurately decode the movement, and predicts the corresponding lower limb joint movement angles.

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

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the invention, the present inventors found that the above technology has at least the following technical problems: currently there is a lack of methods for monitoring sciatic nerve damage during total hip replacement surgery, and it is difficult to ensure that the patient's pain is minimized. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and device for detecting lower limb nerves in total hip replacement surgery, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A first aspect of the present invention provides a method for lower limb nerve detection during total hip replacement surgery, comprising: acquiring performance status data of a lower limb nerve detection device, monitoring intraoperative environmental data of the total hip replacement surgery, comprehensively analyzing to obtain a nerve detection accuracy evaluation value, and obtaining a waveform abnormality evaluation correction parameter based on matching of the nerve detection accuracy evaluation value.

[0009] Neural electrodes are deployed to provide periodic weak current stimulation, and the electrode pair waveform data is recorded, and comprehensive analysis is performed to obtain the electrode pair waveform abnormality assessment value.

[0010] The waveform anomaly assessment threshold is obtained according to the waveform anomaly assessment correction parameter processing, and the waveform anomaly assessment value is compared with the waveform anomaly assessment threshold, and anomaly feedback is given according to the comparison result.

[0011] Optionally, the comprehensive analysis obtains a nerve detection accuracy evaluation value, and the specific analysis process is: processing the performance status data of the lower limb nerve detection equipment to obtain an equipment accuracy evaluation index.

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

[0013] Optionally, the matching obtains waveform abnormality assessment correction parameters, and the specific matching process is: inputting the nerve detection accuracy assessment value into the lower limb nerve database to match and obtain the waveform abnormality assessment correction parameters corresponding to each nerve detection accuracy assessment value interval.

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

[0015] The number of critical amplifier channels, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current and critical adjacent maintenance interval were extracted from the lower limb nerve database, and the equipment accuracy evaluation index was obtained through comprehensive analysis.

[0016] Optionally, the comprehensive analysis obtains an abnormal evaluation value of the electrode pair waveform, and the specific analysis process is: 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 electrode pair waveform data are processed to obtain standard electrode pair waveform data.

[0018] The allowable deviation waveform frequency, allowable deviation peak value and allowable deviation trough value were extracted from the lower limb nerve database, and the electrode pair waveform abnormality assessment value was obtained through comprehensive analysis.

[0019] Optionally, the electrode pair waveform data is processed to obtain standard electrode pair waveform data, and the specific processing process is: obtain the average value of the waveform frequency, peak value and trough value of each electrode pair waveform, and mark them as reference standard waveform frequency, reference standard peak value and reference standard trough value respectively.

[0020] Optionally, the waveform abnormality assessment threshold is obtained according to the waveform abnormality assessment correction parameter processing, and the waveform abnormality assessment value and the waveform abnormality assessment threshold are compared, and abnormal feedback is performed according to the comparison result. The specific analysis process is: extracting a preset initial waveform abnormality assessment threshold from the lower limb nerve database, adding the initial waveform abnormality assessment threshold and the waveform abnormality assessment correction parameter to obtain the waveform abnormality assessment threshold, and comparing the waveform abnormality assessment value and the waveform abnormality assessment threshold. If the waveform abnormality assessment value is greater than or equal to the waveform abnormality assessment threshold, an abnormal warning is immediately issued. If the waveform abnormality assessment value is less than the waveform abnormality 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 waveform spatial position of each waveform acquisition point, counting the spatial positions where the waveform appears most frequently, marking them as high-frequency points, and outputting a standard waveform according to the waveform frequency, peak value, trough value, and high-frequency points; if the generated waveform differs by more than 50% in any one of the waveform frequency, peak value, trough value, and high-frequency points, a prompt sound is issued to remind the operator that the waveform is abnormal.

[0022] Optionally, the neural detection accuracy evaluation value is specifically expressed as:

[0023]

[0024] Among them, Ta represents the neural detection accuracy evaluation value, Ma represents the equipment accuracy evaluation index, e represents the natural constant, S represents the light intensity, and S 0 represents the reference standard light intensity, ΔS represents the allowable deviation light intensity, z represents the noise intensity, 0 represents the critical noise intensity, ρ 1 Represents the neural detection accuracy assessment impact factor corresponding to the set device accuracy assessment index, ρ 2 Represents the influence factor of the neural detection accuracy assessment corresponding to the set light intensity, ρ 3 Indicates the impact factor of neural detection accuracy assessment corresponding to the set noise intensity.

[0025] A second aspect of the present invention provides a lower limb nerve detection device during total hip replacement surgery, comprising: an electrode cap for controlling electrodes to prevent bacterial infection.

[0026] Electrode limiters are used to limit joint movement to protect nerves from damage, allowing electrodes to be attached to the skin in a flat shape.

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

[0028] Electrode needles are used to be inserted near specific nerves or into muscle tissue. The location of the nerves can be determined by stimulating the electrode needles and observing the patient's response. The functional status of the nerves can also 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 scheme 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 and monitor the progress of nerve function recovery, which helps to adjust the treatment plan in a timely manner to improve the treatment effect.

[0031] By evaluating the device accuracy assessment index, we can ensure that the device can accurately detect and analyze lower limb nerve signals in actual applications, which will help doctors more accurately judge the patient's nerve condition and develop more effective treatment plans. It can also discover possible defects or deficiencies in the performance of the device, and then take targeted optimization measures to provide patients with higher quality medical services.

[0032] By evaluating the accuracy of neurological testing, the patient's neurological function status can be more accurately reflected, thereby reducing improper treatment or delays caused by misdiagnosis or missed diagnosis, helping doctors to develop more accurate treatment plans for patients, improve treatment effects, reduce treatment risks, and further enhance patients' trust and satisfaction with medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

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

[0035] Figure 2 This is a diagram of the device of the present invention, which includes an electrode cap 10 , an electrode limiter 11 , an electrode wire 12 and an electrode needle 13 .

[0036] Figure 3 It is a schematic diagram of the functional relationship between the device accuracy evaluation index of the present invention and the number of amplifier channels. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work 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 replacement surgery, including: acquiring performance status data of lower limb nerve detection equipment, and monitoring intraoperative environmental data of total hip replacement surgery, comprehensively analyzing to obtain a nerve detection accuracy evaluation value, and matching the nerve detection accuracy evaluation value to obtain a waveform abnormality evaluation correction parameter.

[0039] Neural electrodes are deployed to provide periodic weak current stimulation, and the electrode pair waveform data is recorded, and comprehensive analysis is performed to obtain the electrode pair waveform abnormality assessment value.

[0040] The waveform anomaly assessment threshold is obtained according to the waveform anomaly assessment correction parameter processing, and the waveform anomaly assessment value is compared with the waveform anomaly assessment threshold, and anomaly feedback is given according to the comparison result.

[0041] Specifically, a comprehensive analysis is performed to obtain a nerve detection accuracy evaluation value, and the specific analysis process is: the performance status data of the lower limb nerve detection equipment is processed to obtain the equipment accuracy evaluation index.

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

[0043] Among them, the performance status data of the lower limb nerve detection equipment is processed to obtain the equipment accuracy evaluation index. The specific processing process is: the performance status data of the lower limb nerve detection equipment includes the number of amplifier channels, working voltage, working current and the length of the adjacent maintenance interval.

[0044] The number of critical amplifier channels, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current and critical adjacent maintenance interval were extracted from the lower limb nerve database, and the equipment accuracy evaluation index was obtained through comprehensive analysis.

[0045] In a specific embodiment, the number of amplifier channels generally 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, which can be obtained through the device manual; the operating voltage is the power supply voltage required for the device to work normally, and the operating current is the current consumed by the device when working. 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 adjacent maintenance time point of the device and the current time point. Maintenance can promptly discover and deal with potential problems of the equipment and avoid failures, thereby improving the stability and reliability of the equipment. It can be obtained through the equipment maintenance log query.

[0046] Specifically, the equipment accuracy evaluation index has a specific numerical expression as follows:

[0047]

[0048] Where Ma represents the equipment accuracy evaluation index, B represents the number of amplifier channels, and B 0 represents the number of critical amplifier channels, U represents the operating voltage, and U 0 It indicates the reference standard working voltage, ΔU indicates the allowable deviation working voltage, I indicates the working current, I 0 represents the reference standard working current, ΔI represents the allowable deviation working current, e represents the natural constant, t represents the duration of the adjacent maintenance interval, t 0 represents the critical adjacent maintenance interval duration, ω 1 Indicates the device accuracy assessment impact factor corresponding to the set number of amplifier channels, ω 2 Indicates the equipment accuracy assessment impact factor corresponding to the set working voltage, ω 3 Indicates the equipment accuracy assessment impact factor corresponding to the set working current, ω 4 Indicates the equipment accuracy assessment impact factor corresponding to the set adjacent maintenance interval duration.

[0049] The algorithm of this embodiment combines the number of amplifier channels, operating voltage, operating current and the length of the adjacent maintenance interval, and comprehensively analyzes to obtain the equipment accuracy evaluation index. The increase in the number of channels will increase the power consumption of the equipment, thereby requiring a higher operating voltage to provide sufficient power. At the same time, as the number of channels increases, the equipment requires more operating current to support the simultaneous operation of multiple channels; under certain resistance conditions, an increase in the operating voltage will lead to an increase in the operating current; and if the equipment works for a long time under high voltage and high current conditions, it may cause circuit aging, component damage and other problems, thereby shortening the maintenance interval. Comprehensive analysis can obtain a more comprehensive equipment accuracy evaluation index.

[0050] like Figure 3 As shown, in a specific embodiment, B 0 =16, U=90V, U 0 =100V, ΔU=10V, I=105mA, I 0 =120mA, ΔI=15mA, t 0 =30 days,ω 1 =ω 2 =0.3,ω 3 =ω 4 =0.4. When t=6, the functional relationship between the device accuracy evaluation index and the number of amplifier channels is shown in curve a; when t=9, the functional relationship between the device accuracy evaluation index and the number of amplifier channels is shown in curve b; when t=15, the functional relationship between the device accuracy evaluation index and the number of amplifier channels is shown in curve c.

[0051] It should be explained that four key factors are considered in this embodiment, namely, the number of amplifier channels, the operating voltage, the operating current, and the length of the adjacent maintenance interval. Reasonable setting of the operating voltage and the operating current can avoid problems such as overheating and overloading of the equipment and extend the service life of the equipment. At the same time, the stable voltage and current supply can ensure that the equipment can work normally in various environments. A reasonable maintenance plan can extend the service life of the equipment and reduce the frequency of equipment replacement. Multi-channel equipment can provide more comprehensive detection data to help doctors make more accurate diagnoses. Stable equipment can reduce uncertainty and errors in the detection process and improve patients' trust in the test results. Reasonable maintenance intervals and convenient maintenance processes can reduce patients' waiting time and improve detection efficiency, thereby improving patients' overall satisfaction. By standardizing the number of amplifier channels, the operating voltage, the operating current, and the length of the adjacent maintenance interval, ensuring that they are compared at the same level, the fairness and comparability of the evaluation are improved. At the same time, B 0 , U 0 ,I 0 and t 0The setting helps to avoid equipment overload, ensure the accuracy and stability of signal processing, protect the equipment from voltage fluctuations and current overload, ensure that the equipment is maintained and maintained in time, and prevent safety problems caused by equipment aging or damage. By weighting the influence of the number of amplifier channels, operating voltage, operating current and the length of the adjacent maintenance interval, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula very adaptable. It is not difficult to see that the larger the number of amplifier channels, the smaller the operating voltage deviation, the smaller the operating current deviation, or the smaller the adjacent maintenance interval, the larger the equipment accuracy evaluation index. By evaluating the equipment accuracy evaluation index, it can be ensured that the equipment can accurately detect and analyze lower limb nerve signals in actual applications, which helps doctors to more accurately judge the patient's nerve condition and develop more effective treatment plans. It can also find possible defects or deficiencies in the performance of the equipment, and then take targeted optimization measures to further ensure that the equipment is always in the best condition, provide patients with higher quality medical services, and help enhance patients' confidence in treatment and improve treatment effects and satisfaction.

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

[0053] In a specific embodiment, the equipment accuracy evaluation index is an important indicator for measuring the performance of the equipment. High-precision equipment can more accurately reflect the patient's lower limb nerve function status and provide doctors with a reliable basis for diagnosis. Light intensity and noise intensity refer to the light intensity and noise level of the environment where the lower limb nerve detection equipment 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 test results and improve the accuracy and reliability of the test. The light intensity can be measured by a portable illuminance meter, and the noise intensity can be measured by a sound level meter.

[0054] Specifically, the neural detection accuracy evaluation value is expressed as:

[0055]

[0056] Among them, Ta represents the neural detection accuracy evaluation value, Ma represents the equipment accuracy evaluation index, e represents the natural constant, S represents the light intensity, and S 0 represents the reference standard light intensity, ΔS represents the allowable deviation light intensity, z represents the noise intensity, 0 represents the critical noise intensity, ρ 1 Represents the neural detection accuracy assessment impact factor corresponding to the set device accuracy assessment index, ρ 2 Represents the influence factor of the neural detection accuracy assessment corresponding to the set light intensity, ρ 3 Indicates the impact factor of neural detection accuracy assessment corresponding to the set noise intensity.

[0057] The algorithm of this embodiment combines the device accuracy evaluation index, light intensity and noise intensity, and comprehensively analyzes to obtain the neural detection accuracy evaluation value. The 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 signal received by the device sensor, thereby affecting the device accuracy evaluation index; at the same time, excessive noise intensity will interfere with the signal received by the device sensor, causing deviations in the detection results, and further affecting the device accuracy evaluation index. Comprehensive analysis can obtain a more comprehensive, accurate and in-depth neural detection accuracy evaluation value.

[0058] Table 1. Examples of data on neural detection accuracy evaluation values

[0059]

[0060]

[0061] As shown in Table 1, the neural detection accuracy evaluation value is jointly determined by the equipment accuracy evaluation index, light intensity and noise intensity. In a specific embodiment, the reference standard light intensity is 100lx, the allowable deviation light intensity is 10lx, the critical noise intensity is 38dB, the neural detection accuracy evaluation impact factor corresponding to the set equipment accuracy evaluation index is 0.4, the neural detection accuracy evaluation impact factor corresponding to the set light intensity is 0.3, and the neural detection accuracy evaluation impact factor corresponding to the set noise intensity is 0.3. This formula takes into account three key factors, namely the equipment accuracy evaluation index, light intensity and noise intensity, which can reduce misdiagnosis caused by equipment errors, improve the accuracy and reliability of diagnosis, and help improve the medical level and competitiveness of hospitals. At the same time, the appropriate light intensity can help the equipment capture neural signals more clearly, improve the recognition rate of signals, and thus enhance the accuracy of detection. In a low-noise environment, the device can receive clearer neural signals, improve the quality of signals, and thus enhance the accuracy of detection. By standardizing the device accuracy evaluation index, light intensity, and noise intensity to ensure that they are compared at the same level, the fairness and comparability of the evaluation are improved. 0 The setting can ensure that the light in the detection environment is neither too strong nor too weak, thus avoiding the interference of light on the detection results. 0 The setting can limit the interference of environmental noise on the detection equipment and reduce misdiagnosis or missed diagnosis caused by noise. By weighting the impact of the equipment accuracy evaluation index, light intensity and noise intensity, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the model highly adaptable. It is not difficult to see that the larger the equipment accuracy evaluation index or the smaller the light intensity deviation or the smaller the noise intensity deviation, the larger the nerve detection accuracy evaluation value. By evaluating the nerve detection accuracy evaluation value, the patient's neurological function status can be more accurately reflected, thereby reducing improper treatment or delays caused by misdiagnosis or missed diagnosis, helping doctors to develop more accurate treatment plans for patients, and accurate nerve detection can reduce the possibility of patients undergoing unnecessary examinations.

[0062] In a specific embodiment, the value range of the neural detection accuracy assessment influencing factor corresponding to the device accuracy assessment index, light intensity and noise intensity is between 0 and 1, which represents the numerical value of the 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 neural database. By adjusting the value of the influencing factor, the influence of different factors on the final neural detection accuracy assessment value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the device accuracy assessment index, light intensity and noise intensity and the weight factors corresponding to the device accuracy assessment index, light intensity and noise intensity preset in the lower limb neural database form a mapping set, and the real-time device accuracy assessment index, light intensity and noise intensity are brought into the mapping set to obtain the weight factors corresponding to the device accuracy assessment index, light intensity and noise intensity. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0063] Among them, waveform abnormality assessment correction parameters are obtained by matching, and the specific matching process is: the nerve detection accuracy assessment value is input into the lower limb nerve database to match the waveform abnormality assessment correction parameters corresponding to each nerve detection accuracy assessment value interval.

[0064] Specifically, the electrode pair waveform abnormality evaluation value is obtained by comprehensive analysis, and the specific analysis process is: the electrode pair waveform data includes the waveform frequency, peak value and trough value of each electrode pair waveform within the monitoring period.

[0065] The electrode pair waveform data are processed to obtain standard electrode pair waveform data.

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

[0067] The allowable deviation waveform frequency, allowable deviation peak value and allowable deviation trough value were extracted from the lower limb nerve database, and the electrode pair waveform abnormality assessment value was obtained through comprehensive analysis.

[0068] In a specific embodiment, the waveform frequency can reflect the conduction speed of nerve impulses and the excitability of neurons. By measuring the waveform frequency, the integrity of nerve fibers and whether the conduction of nerve impulses is normal can be determined; the peak value represents the intensity or amplitude of nerve signals, and is an important indicator for evaluating neuronal excitability and nerve fiber integrity. Changes in the peak value can intuitively reflect changes in nerve signal intensity; the trough value reflects the degree of attenuation or inhibition of nerve signals during conduction. By measuring the trough value, the degree of damage to nerve fibers and interference factors in the conduction of nerve impulses can be understood, which can be measured by electrodes and neuroelectrophysiological instruments.

[0069] It should be explained that there are six pairs of electrodes in this embodiment, which are marked as: A1, A2, B1, B2, C1, C2, D1, D2, E1, E2, F1, F2. The specific positions are: Electrodes A1 and A2 are femoral nerve stimulation electrodes. Insert electrode A1 5 cm outside the midpoint of the inguinal ligament, and insert electrode A2 5 cm inside the midpoint of the inguinal ligament; B1 and B2 are sciatic nerve stimulation electrodes. Insert B1 into the outer 1 / 5 position of the line connecting the posterior superior iliac spine and the ischial tuberosity, and insert B2 into the inner 1 / 5 position of the line connecting the posterior superior iliac spine and the ischial tuberosity; C1 and C2 are rectus femoris electrodes. Insert C1 10 cm above the patella, and insert C2 20 cm above the patella; D1, D2 For the vastus lateralis electrode, D1 was inserted 10 cm outside the midline 20 cm above the patella, and D2 was inserted 10 cm inside the midline 20 cm above the patella; EI and E2 were gastrocnemius electrodes. E1 was inserted 3 cm to the left of the midline 8 cm below the popliteal fossa, and E2 was inserted 3 cm to the right of the midline 8 cm below the popliteal fossa; F1 and F2 were tibialis anterior muscles. F1 was inserted 1.5 cm outside the anterior edge of the tibia 10 cm below the patella, and F2 was inserted 1.5 cm outside the anterior edge of the tibia 20 cm below the patella. Pinch the electrode cap and insert the electrode into the thickest part of the rectus femoris, gastrocnemius, tibialis anterior, and flexor hallucis muscles, so that the electrode limiter is close to the skin. Then press the electrode limiter and bend the electrode cap to the side. The connection between the electrode cap and the electrode limiter can be broken. Then throw away the electrode cap, and the electrode can be attached to the skin in a flat shape. At this time, the electrode can be covered with a commercially available surgical film to avoid interference with surgical disinfection due to the presence of the electrode.

[0070] Among them, the specific numerical expression of the electrode pair waveform abnormality evaluation value is:

[0071]

[0072] Where Wd represents the electrode pair waveform abnormality evaluation value, e represents the natural constant, and f i represents the waveform frequency of the ith electrode pair, f 0 represents the reference standard waveform frequency, Δf represents the allowable deviation waveform frequency, T i represents the peak value of the ith electrode pair, T 0 It indicates the reference standard peak value, ΔT indicates the allowable deviation peak value, D i represents the trough value of the i-th electrode pair, D 0 represents the reference standard trough value, ΔD represents the allowable deviation trough value, β 1 Indicates the influence factor of the electrode pair waveform abnormality assessment corresponding to the set waveform frequency, β 2 Indicates the influence factor of the electrode on the waveform abnormality assessment corresponding to the set peak value, β 3It represents the influence factor of the electrode pair waveform abnormality assessment corresponding to the set trough peak value, i represents the number of each electrode pair, i=1,2,3,...,m, m represents the total number of electrode pairs, m=5, which are A1A2, B1B2, C1C2, D1D2, E1E2 and F1F2 electrode pairs respectively.

[0073] The algorithm of this embodiment combines the waveform frequency, peak value and trough value of each electrode pair waveform, and comprehensively analyzes to obtain the electrode pair waveform abnormality evaluation value. Under normal circumstances, the increase in waveform frequency is usually accompanied by an increase in the peak value, because as the conduction speed of nerve impulses increases, the strength of nerve signals will also increase accordingly. However, in certain pathological conditions, the increase in waveform frequency is not accompanied by an increase in the peak value, and the peak value may even decrease. This is due to damage to nerve fibers or reduced excitability of neurons. There is usually no direct linear relationship between waveform frequency and trough value, but in certain circumstances, such as damage to nerve fibers or obstruction of nerve impulse conduction, the decrease in waveform frequency is accompanied by an increase in the trough value, which reflects the attenuation of nerve signals or an increase in the degree of inhibition during the conduction process. Comprehensive analysis can obtain more comprehensive, accurate and in-depth electrode pair waveform abnormality evaluation values.

[0074] It should be explained that the present embodiment takes into account three key factors, namely the waveform frequency, peak value and trough value of each electrode pair waveform, which can timely detect abnormal changes in nerve function, help early diagnosis of the disease, and have important significance for the treatment and rehabilitation of patients. Intervention measures can be taken as early as possible to prevent the disease from worsening, and can provide a scientific basis for doctors to formulate treatment plans and formulate personalized treatment plans. By standardizing the waveform frequency, peak value and trough value of each electrode pair waveform, ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved, and at the same time, f 0 , T 0 and D 0The setting can be used to evaluate whether the waveform frequency, peak value and trough value actually detected are within the normal range. By weighting the influence of each electrode pair on the waveform frequency, peak value and trough value, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is not difficult to see that the smaller the deviation of the waveform frequency, peak value or trough value, the greater the abnormal waveform evaluation value of the electrode pair. By evaluating the abnormal waveform evaluation value of the electrode pair, it can help doctors identify the presence and extent of nerve damage. For example, abnormal waveform frequency may indicate a slowing of nerve conduction velocity, while abnormal peak or trough values ​​may reflect a weakening or abnormal enhancement of nerve signals, which helps to distinguish different types of nerve damage, such as complete damage and incomplete damage, so as to develop targeted treatment plans, which can more accurately locate nerve damage and monitor the progress of nerve function recovery, which helps to adjust the treatment plan in time to improve the treatment effect.

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

[0076] Specifically, a waveform abnormality assessment threshold is obtained according to the waveform abnormality assessment correction parameter processing, and the waveform abnormality assessment value and the waveform abnormality assessment threshold are compared. The specific analysis process is: extracting the preset initial waveform abnormality assessment threshold from the lower limb nerve database, adding the initial waveform abnormality assessment threshold and the waveform abnormality assessment correction parameter to obtain the waveform abnormality assessment threshold, and comparing the waveform abnormality assessment value and the waveform abnormality assessment threshold. If the waveform abnormality assessment value is greater than or equal to the waveform abnormality assessment threshold, an abnormal 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 in this embodiment, abnormal feedback is provided based on the comparison results, and also includes: deploying waveform acquisition points within the monitoring period, acquiring and comparing the waveform spatial positions at 1 / 5, 2 / 5, 3 / 5, 4 / 5, and 5 / 5 of each period, counting the spatial positions where the waveform appears most frequently, marking them as high-frequency points, and outputting a standard waveform based on the waveform frequency, peak value, trough value, and high-frequency points; if the generated waveform differs by more than 50% in any one of the waveform frequency, peak value, trough value, and high-frequency points, a prompt sound is issued to remind the operator that the waveform is abnormal.

[0078] Reference Figure 2 As shown, the second aspect of the present invention provides a lower limb nerve detection device during total hip replacement surgery, including: an electrode cap 10, which is used to control the electrode to prevent bacterial infection.

[0079] The electrode limiter 11 is used to limit joint movement to protect nerves from damage, and can enable the electrode to be attached to the skin in a flat shape.

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

[0081] The electrode needle 13 is used to be inserted near a specific nerve or into muscle tissue. The location of the nerve can be determined by stimulating the electrode needle and observing the patient's response. The functional status of the nerve can also be monitored in real time.

[0082] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting lower limb nerves during total hip replacement, characterized in that: include: Obtain the performance status data of the lower limb nerve detection equipment, monitor the intraoperative environmental data of total hip replacement, obtain the nerve detection accuracy evaluation value through comprehensive analysis, and obtain the waveform abnormality evaluation correction parameter according to the matching of the nerve detection accuracy evaluation value; Deploy neural electrodes to provide periodic weak current stimulation, record electrode pair waveform data, and conduct comprehensive analysis to obtain electrode pair waveform abnormality assessment values; The waveform anomaly assessment threshold is obtained according to the waveform anomaly assessment correction parameter processing, and the waveform anomaly assessment value is compared with the waveform anomaly assessment threshold, and anomaly feedback is given according to the comparison result.

2. The method for detecting lower limb nerves during total hip replacement according to claim 1, characterized in that: The comprehensive analysis obtains the neural detection accuracy evaluation value, and the specific analysis process is as follows: Process the performance status data of the lower limb nerve detection equipment to obtain the equipment accuracy evaluation index; Based on the equipment accuracy evaluation index and intraoperative environmental data, a comprehensive analysis was performed to obtain the nerve detection accuracy evaluation value.

3. The method for detecting lower limb nerves during total hip replacement according to claim 2, characterized in that: The matching obtains the waveform anomaly assessment correction parameter, and the specific matching process is: The nerve detection accuracy assessment value is input into the lower limb nerve database to match and obtain the waveform abnormality assessment correction parameters corresponding to each nerve detection accuracy assessment value interval.

4. The method for detecting lower limb nerves during total hip replacement 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 process 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 the duration of the adjacent maintenance interval; The number of critical amplifier channels, reference standard operating voltage, allowable deviation operating voltage, reference standard operating current, allowable deviation operating current and critical adjacent maintenance interval were extracted from the lower limb nerve database, and the equipment accuracy evaluation index was obtained through comprehensive analysis.

5. The method for detecting lower limb nerves during total hip replacement according to claim 1, characterized in that: The comprehensive analysis obtains the electrode pair waveform abnormality evaluation value, and 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; Processing the electrode pair waveform data to obtain standard electrode pair waveform data; The allowable deviation waveform frequency, allowable deviation peak value and allowable deviation trough value were extracted from the lower limb nerve database, and the electrode pair waveform abnormality assessment value was obtained through comprehensive analysis.

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

7. The method for detecting lower limb nerves during total hip replacement according to claim 5, characterized in that: The waveform anomaly assessment correction parameter is processed according to the waveform anomaly assessment to obtain the waveform anomaly assessment threshold, and the waveform anomaly assessment value is compared with the waveform anomaly assessment threshold, and anomaly feedback is performed according to the comparison result. The specific analysis process is as follows: A 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 and the waveform abnormality assessment threshold are compared, and if the waveform abnormality assessment value is greater than or equal to the waveform abnormality assessment threshold, an abnormality warning is immediately issued, and if the waveform abnormality assessment value is less than the waveform abnormality assessment threshold, no additional operation is performed.

8. The method for detecting lower limb nerves during total hip replacement according to claim 7, characterized in that: The abnormal feedback further includes: Waveform collection points are deployed during the monitoring period, the spatial position of the waveform of each waveform collection point is collected and compared, the spatial positions where the waveform appears most frequently are counted and marked as high-frequency points, and a standard waveform is output based on the waveform frequency, peak value, trough value, and high-frequency points. If the generated waveform differs by more than 50% in any of the waveform frequency, peak value, trough value, and high-frequency points, a prompt sound is issued to remind the operator that the waveform is abnormal.

9. The method for detecting lower limb nerves during total hip replacement according to claim 2, characterized in that: The neural detection accuracy evaluation value is specifically expressed as: Among them, Ta represents the neural detection accuracy evaluation value, Ma represents the equipment accuracy evaluation 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 evaluation influencing factor corresponding to the set equipment accuracy evaluation index, ρ2 represents the neural detection accuracy evaluation influencing factor corresponding to the set light intensity, and ρ3 represents the neural detection accuracy evaluation influencing factor corresponding to the set noise intensity.

10. A device for detecting lower limb nerves in total hip replacement surgery using the method according to any one of claims 1 to 9, characterized in that: include: Electrode cap, used to control the electrode and prevent bacterial infection; Electrode limiters are used to limit joint movement to protect nerves from damage, allowing electrodes to be attached to the skin in a flat shape; Electrode wires, used to transmit neural signals captured by the electrodes to monitoring instruments for analysis and display; Electrode needles are used to be inserted near specific nerves or into muscle tissue. The location of the nerves can be determined by stimulating the electrode needles and observing the patient's response. The functional status of the nerves can also be monitored in real time.

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