Myoelectric signal acquisition method and system for acupuncture therapy

Abstract

The present invention relates to the field of acupuncture electromyographic signal acquisition, and specifically to an electromyographic signal acquisition method and system for acupuncture therapy. The method first clusters the sample points on the electromyographic signal of the acupuncture process to obtain multiple signal segments, performs abnormal analysis on each signal segment to obtain the possibility of abnormality, and screens out the abnormal signal segment based on the possibility of abnormality, and obtains the amplitude difference degree of each adjacent signal segment according to the difference in the amplitude of the extreme point between the abnormal signal segment and the adjacent signal segment; according to the difference between the time of each extreme point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment, and the amplitude of each extreme point, the amplitude of the extreme point on the abnormal signal segment is adjusted to obtain the adjusted electromyographic signal. The present invention can remove local abnormalities on the electromyographic signal and improve the quality of the collected electromyographic signal.

Description

Technical Field

[0001] The present invention relates to the field of acupuncture electromyographic signal acquisition, and in particular to a method and system for acquiring electromyographic signals for acupuncture therapy. Background Art

[0002] Acupuncture therapy is a traditional Chinese medicine method that uses tiny needles to regulate Qi and blood and balance the body's internal energy to treat diseases, relieve pain and improve health.

[0003] Related technologies usually use a method that combines modern technology with acupuncture therapy. For example, electromyographic signals of relevant parts during acupuncture are usually collected as a basis for evaluating the therapeutic effect of acupuncture. However, during the acupuncture process, due to unfavorable factors such as excessive muscle tension of the patient or improper acupuncture operation, the collected electromyographic signals may have local abnormal signal segments, resulting in poor quality of the collected electromyographic signals. Summary of the invention

[0004] In order to solve the technical problem that the collected electromyographic signals have local abnormal signal segments, resulting in poor quality of the collected electromyographic signals, the purpose of the present invention is to provide an electromyographic signal collection method and system for acupuncture therapy, and the technical scheme adopted is as follows:

[0005] The present invention proposes a method for collecting electromyographic signals for acupuncture therapy, the method comprising:

[0006] Acquire the electromyographic signal of the patient during acupuncture within a preset time period, wherein the electromyographic signal is composed of sample points, and the sample points are composed of time and amplitude; cluster the sample points on the electromyographic signal to obtain multiple signal segments;

[0007] Taking any signal segment as a target signal segment, obtaining the abnormal possibility of the target signal segment according to the amplitude of the extreme value points on the target signal segment, the proportion of the number of extreme value points in the number of sample points of the target signal segment, the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment; based on the abnormal possibility, screening out the abnormal signal segment from all signal segments;

[0008] Taking any abnormal signal segment as the target abnormal signal segment, taking the two signal segments adjacent to the target abnormal signal segment as adjacent signal segments, obtaining the amplitude difference degree of each adjacent signal segment according to the difference in amplitude of the extreme point between the target abnormal signal segment and each adjacent signal segment; obtaining the amplitude adjustment factor of each extreme point in the target abnormal signal segment according to the difference between the time of each extreme point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment, and the amplitude of each extreme point;

[0009] Based on the amplitude adjustment factor, the amplitude of the extreme point on the abnormal signal segment is adjusted to obtain an adjusted electromyographic signal.

[0010] Furthermore, the abnormal possibility of obtaining the target signal segment includes:

[0011] Analyze the overall level of the absolute values ​​of the amplitudes of all extreme value points on the target signal segment to obtain the fluctuation amplitude parameter of the target signal segment;

[0012] The number of all extreme value points on the target signal segment is used as the numerator, the number of all sample points on the target signal segment is used as the denominator, and the ratio is used as the fluctuation frequency parameter of the target signal segment;

[0013] The fluctuation amplitude parameter and the fluctuation frequency parameter are combined to obtain an initial abnormal parameter of the target signal segment;

[0014] According to the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment, the initial abnormality parameter is adjusted to obtain the abnormal possibility of the target signal segment.

[0015] Further, the adjusting the initial abnormal parameter to obtain the abnormal possibility of the target signal segment includes:

[0016] The difference between the initial time of the target signal segment and the initial time of the electromyographic signal is taken as the time distance of the target signal segment;

[0017] The difference between the end time and the initial time of the target signal segment is taken as the duration of the target signal segment;

[0018] According to the time distance and the duration, obtaining an abnormality confidence of the target signal segment, wherein the abnormality confidence is positively correlated with the time distance, and the abnormality confidence is negatively correlated with the duration;

[0019] The abnormality confidence is used to weight the initial abnormality parameters and perform normalization processing to obtain the abnormality possibility of the target signal segment.

[0020] Further, the filtering out abnormal signal segments from all signal segments based on the abnormal possibility includes:

[0021] The signal segment whose abnormal possibility is greater than the preset abnormal threshold is regarded as an abnormal signal segment.

[0022] Further, obtaining the amplitude difference degree of each adjacent signal segment includes:

[0023] The absolute value of the difference between the fluctuation amplitude parameter of each adjacent signal segment and the target abnormal signal segment is used as the amplitude difference degree of each adjacent signal segment.

[0024] Furthermore, the step of obtaining the amplitude adjustment factor of each extreme point in the target abnormal signal segment includes:

[0025] Any extreme point on the target abnormal signal segment is taken as the target extreme point;

[0026] According to the difference between the time of the target extreme value point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment is adjusted to obtain a first adjustment factor of the target extreme value point;

[0027] Normalizing the absolute value of the amplitude of the target extreme point to obtain a second adjustment factor of the target extreme point;

[0028] The first adjustment factor and the second adjustment factor are integrated and normalized to obtain the amplitude adjustment factor of the target extreme value point.

[0029] Furthermore, the first adjustment factor for obtaining the target extreme point includes:

[0030] The adjacent signal segment located on the left side of the target abnormal signal segment is regarded as the left adjacent signal segment, and the adjacent signal segment located on the right side of the target abnormal signal segment is regarded as the right adjacent signal segment;

[0031] The difference between the time of the target extreme value point and the initial time of the target abnormal signal segment is used as the numerator, the duration of the target abnormal signal segment is used as the denominator, and the ratio is used as the first weight of the left adjacent signal segment; the difference between the end time of the target abnormal signal segment and the time of the target extreme value point is used as the numerator, the duration of the target abnormal signal segment is used as the denominator, and the ratio is used as the second weight of the right adjacent signal segment;

[0032] The first weight and the second weight are used to perform weighted summation on the amplitude difference degrees of the left adjacent signal segment and the right adjacent signal segment, respectively, to obtain a first adjustment factor of the target extreme point.

[0033] Furthermore, the second adjustment factor for obtaining the target extreme point includes:

[0034] The absolute value of the amplitude of the target extreme point is used as the numerator, the maximum absolute value of the amplitude of all extreme points on the target abnormal signal segment is used as the denominator, and the ratio is used as the second adjustment factor of the target extreme point.

[0035] Further, obtaining the adjusted electromyographic signal includes:

[0036] The product value of the amplitude adjustment factor and the amplitude of each extreme point on the target abnormal signal segment is used as the amplitude adjustment amount of each extreme point;

[0037] The difference between the amplitude of each extreme point on the target abnormal signal segment and the amplitude adjustment amount is used as the adjusted amplitude of each extreme point, and the adjusted amplitude of each extreme point on the target abnormal signal segment is used as the new amplitude of each extreme point;

[0038] The extreme value points of all abnormal signal segments of the electromyographic signal are traversed, and the amplitudes of the extreme value points are adjusted to obtain the adjusted electromyographic signal.

[0039] The present invention also proposes an electromyographic signal acquisition system for acupuncture therapy, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements any one of the steps of an electromyographic signal acquisition method for acupuncture therapy when executing the computer program.

[0040] The present invention has the following beneficial effects:

[0041] The present invention takes into account unfavorable factors such as excessive muscle tension of the patient or improper acupuncture operation, which may cause local abnormal signal segments in the collected electromyographic signals, resulting in poor quality of the collected electromyographic signals. Therefore, the sample points on the acquired electromyographic signals are first clustered, and the electromyographic signals are divided into multiple signal segments. Since the amplitude and frequency of the fluctuation of the abnormal signal segments are large, and the amplitude and frequency of the fluctuation of the signal segments can be respectively reflected by the amplitude and number of extreme points, and the abnormal signal segments usually appear after a long time after the start of electromyographic signal collection, and the abnormal state lasts for a short time, based on the above characteristics, the possibility of abnormality in the target signal segment can be reflected by the abnormal possibility, and the abnormal signal segments can be screened out. Since the existence of abnormal signal segments reduces the quality of electromyographic signals, the present invention performs an analysis on the amplitude of the extreme points on the abnormal signal segments. Adjustments are made so that the abnormal signal segment is closer to the normal signal segment. First, adjacent signal segments are selected, and the adjacent signal segments are the normal signal segments on both sides of the target abnormal signal segment. The difference in amplitude is used to reflect the difference in fluctuation amplitude between the target abnormal signal segment and the adjacent signal segments. Considering that the greater the difference in fluctuation amplitude between the target abnormal signal segment and the adjacent signal segments, and the greater the amplitude of the extreme point of the target abnormal signal segment, the greater the degree of adjustment of the amplitude of the extreme point on the target abnormal signal segment is required, and different adjacent signal segments have different reference values ​​in the subsequent adjustment process of the amplitude of the extreme point. Therefore, based on this feature, the amplitude adjustment factor is used to reflect the adjustment degree of the amplitude of the extreme point, and then the amplitude of the extreme point on the abnormal signal segment is adjusted to obtain the adjusted electromyographic signal, remove the local abnormal phenomenon on the electromyographic signal, and improve the quality of the electromyographic signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of a method for collecting electromyographic signals for acupuncture therapy provided by one embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a local abnormal phenomenon of an electromyographic signal provided by an embodiment of the present invention;

[0045] Figure 3 A flow chart of a method for obtaining the abnormal possibility of a target signal segment provided by one embodiment of the present invention;

[0046] Figure 4 A flow chart of a method for obtaining an amplitude adjustment factor for each extreme point in a target abnormal signal segment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the electromyographic signal acquisition method and system for acupuncture therapy proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following is a detailed description of a method and system for collecting electromyographic signals for acupuncture therapy provided by the present invention in conjunction with the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of a method for collecting electromyographic signals for acupuncture therapy provided by an embodiment of the present invention, the method comprising:

[0051] Step S1: Acquire the electromyographic signal of the patient during acupuncture within a preset time period, where the electromyographic signal is composed of sample points, and the sample points are composed of time and amplitude; cluster the sample points on the electromyographic signal to obtain multiple signal segments.

[0052] In the embodiment of the present invention, an electromyographic signal collector, such as a surface electromyography device, is first arranged near the acupuncture site. During the acupuncture process, the electromyographic signal collector is used to obtain the electromyographic signal of the acupuncture site of the patient within a preset time period. The electromyographic signal is composed of sample points, and the sample points are two-dimensional data points composed of time and amplitude. The sampling frequency of the sample points is set to 1000HZ. The sampling frequency of the sample points can also be set by the implementer according to the specific implementation scenario, which is not limited here. During the acupuncture process, the time from needle insertion to withdrawal is usually 20-30 minutes, so the range of the preset time period is , in minutes. In one embodiment of the present invention, the preset time period is set to 25 minutes. The specific value of the preset time period can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0053] Acupuncture is often used to relax tense muscles. By needling specific acupuncture points, the nervous system can be induced to regulate muscles, causing them to relax, which in turn leads to a decrease in muscle discharge activity. This effect is reflected in the electromyographic signal as the amplitude and frequency of the electromyographic signal gradually decrease as acupuncture is performed. However, due to unfavorable factors such as excessive muscle tension or improper acupuncture operation, the collected electromyographic signal may have local abnormal signal segments. This abnormal signal segment is reflected in the electromyographic signal as a signal fluctuation with a large amplitude and frequency at a local position of a signal segment with a small amplitude and frequency. Please refer to Figure 2 , which shows a schematic diagram of the local abnormal phenomenon of the electromyographic signal provided by an embodiment of the present invention. Due to the existence of local abnormal fluctuations, the quality of the collected electromyographic signal is poor. Therefore, the embodiment of the present invention first clusters the sample points on the electromyographic signal, and divides the local sample points with similar fluctuations into one category to segment the electromyographic signal, thereby obtaining multiple signal segments, and subsequently analyzing the signal segments to identify abnormal signal segments and adjust the abnormal signal segments.

[0054] Preferably, in one embodiment of the present invention, the ISODATA algorithm is used to implement clustering operations. The ISODATA algorithm is a technical means well known to those skilled in the art and will not be described in detail here.

[0055] At this point, multiple signal segments are obtained.

[0056] Step S2: Take any signal segment as the target signal segment, and obtain the abnormal possibility of the target signal segment according to the amplitude of the extreme points on the target signal segment, the proportion of the number of extreme points in the number of sample points in the target signal segment, the difference in the initial time between the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment; based on the abnormal possibility, filter out the abnormal signal segments from all signal segments.

[0057] Since it is necessary to remove abnormal phenomena on the electromyographic signal, it is necessary to screen out abnormal signal segments. First, any signal segment is analyzed and any signal segment is taken as the target signal segment. Since the amplitude and frequency of the abnormal signal segment fluctuation are large, and the amplitude and frequency of the signal segment fluctuation can be respectively reflected by the amplitude and number of extreme points, and the abnormal signal segment usually appears for a long time after the start of electromyographic signal collection, and the abnormal state lasts for a short time, therefore, in one embodiment of the present invention, the existing APCA algorithm is first used to extract the extreme points on the electromyographic signal, including the maximum point and the minimum point. In other embodiments of the present invention, the extreme points can also be extracted by, for example, the Newton method, which is not limited here. Then, the amplitude of the extreme points on the target signal segment, the proportion of the number of extreme points in the number of sample points of the target signal segment, the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment are analyzed, and the possibility of abnormality in the target signal segment is reflected by the obtained abnormal possibility. Subsequently, the abnormal signal segment can be screened out based on the abnormal possibility.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the abnormal possibility of the target signal segment specifically includes:

[0059] See also Figure 3 , which shows a schematic diagram of a method for obtaining the abnormal possibility of a target signal segment provided by an embodiment of the present invention.

[0060] Step S201: Analyze the overall level of the absolute values ​​of the amplitudes of all extreme value points on the target signal segment to obtain the fluctuation amplitude parameter of the target signal segment.

[0061] From the above analysis, it can be seen that the amplitude of the abnormal signal segment fluctuation is large. Therefore, the overall level of the absolute value of the amplitude of all extreme points on the target signal segment can be analyzed to obtain the fluctuation amplitude parameter of the target signal segment. The larger the fluctuation amplitude parameter, the larger the amplitude of the target signal segment fluctuation, which means that the target signal segment is more likely to be abnormal.

[0062] In an embodiment of the present invention, the average or median of the absolute values ​​of the amplitudes of all extreme points on the target signal segment can be used as the fluctuation amplitude parameter of the target signal segment to achieve an overall level analysis of the absolute values ​​of the amplitudes of all extreme points on the target signal segment, which is not limited here.

[0063] Step S202: The number of all extreme value points on the target signal segment is used as the numerator, the number of all sample points on the target signal segment is used as the denominator, and the ratio is used as the fluctuation frequency parameter of the target signal segment.

[0064] From the above analysis, it can be seen that the frequency of abnormal signal segment fluctuations is also large, and the frequency of signal segment fluctuations can be reflected by the ratio of the number of extreme points appearing on the signal segment to the number of sample points in the signal segment. The larger the ratio of the number of extreme points, the greater the frequency of signal segment fluctuations. Therefore, the number of all extreme points on the target signal segment can be used as the numerator, and the number of all sample points on the target signal segment can be used as the denominator. The ratio can be used as the fluctuation frequency parameter of the target signal segment. The larger the fluctuation frequency parameter, the greater the frequency of fluctuations in the target signal segment, and thus the more likely the target signal segment is to be abnormal.

[0065] Step S203: synthesize the fluctuation amplitude parameter and the fluctuation frequency parameter to obtain the initial abnormal parameter of the target signal segment.

[0066] Then, the fluctuation amplitude parameters and the fluctuation frequency parameters can be integrated, and the possibility of abnormality in the target signal segment can be preliminarily reflected through the obtained initial abnormal parameters.

[0067] In the embodiment of the present invention, the sum or product of the fluctuation amplitude parameter and the fluctuation frequency parameter may be used as the initial abnormal parameter of the target signal segment to achieve a combination of the two, which is not limited here.

[0068] As an example, in one embodiment of the present invention, the expression of the initial abnormal parameter of the target signal segment may be specifically, for example, as follows:

[0069]

[0070] in, represents the initial abnormal parameters of the target signal segment; Indicates the number of all extreme points on the target signal segment; Represents the number of all sample points on the target signal segment; Indicates the first The amplitude of the extreme point; Indicates the fluctuation frequency parameter of the target signal segment; Indicates the fluctuation amplitude parameter of the target signal segment.

[0071] Step S204: According to the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment, the initial abnormality parameter is adjusted to obtain the abnormal possibility of the target signal segment.

[0072] As acupuncture progresses, muscles gradually change from a tense state to a relaxed state. Therefore, the electromyographic signal gradually changes from a fluctuation state with large amplitude and frequency to a fluctuation state with small amplitude and frequency. That is to say, in the early stage of acupuncture, the fluctuation amplitude and frequency of the electromyographic signal are also large. Therefore, this interference needs to be eliminated to avoid mistaking the normal signal segment in the early stage for an abnormal signal segment. Considering that the abnormal signal segment usually appears for a long time after the start of electromyographic signal collection, and the abnormal state lasts for a short time, based on this feature, the confidence of the initial abnormal parameters of the target signal segment can be calculated and analyzed, and the initial abnormal parameters of the target signal segment can be weighted and adjusted according to the confidence, so as to obtain the abnormal possibility of the target signal segment.

[0073] Preferably, in one embodiment of the present invention, the method for obtaining the abnormal possibility of the target signal segment further includes:

[0074] The difference between the initial time of the target signal segment and the initial time of the electromyographic signal is taken as the time distance of the target signal segment. The larger the time distance, the greater the confidence that the target signal segment is abnormal.

[0075] The difference between the end time and the initial time of the target signal segment is taken as the duration of the target signal segment. The smaller the duration, the greater the confidence that the target signal segment is abnormal.

[0076] According to the time distance and duration, the abnormality confidence of the target signal segment is obtained. The abnormality confidence is positively correlated with the time distance, and negatively correlated with the duration. The larger the abnormality confidence, the greater the confidence that the target signal segment has an abnormality when the initial abnormal parameters of the target signal segment are larger.

[0077] Then, the initial abnormal parameters are weighted and normalized using the abnormal confidence to obtain the abnormal possibility of the target signal segment.

[0078] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0079] As an example, in one embodiment of the present invention, the expression of the abnormal possibility of the target signal segment can be specifically, for example, as follows:

[0080]

[0081] in, Indicates the possibility of abnormality of the target signal segment; Indicates the initial time of the target signal segment; Indicates the initial moment of the electromyographic signal; Indicates the end time of the target signal segment; represents the initial abnormal parameters of the target signal segment; represents the time distance of the target signal segment; Indicates the duration of the target signal segment; Indicates the abnormal confidence of the target signal segment; Represents the normalization function.

[0082] It should be noted that in other embodiments of the present invention, the positive and negative correlations may be represented by other basic mathematical operations, which will not be elaborated herein.

[0083] The abnormal possibility of each signal segment can be obtained by the same method as above. The greater the abnormal possibility, the more likely the signal segment is to be abnormal. Therefore, based on the abnormal possibility, the abnormal signal segments are screened out from all signal segments.

[0084] Preferably, in one embodiment of the present invention, a signal segment with an abnormal possibility greater than a preset abnormal threshold is taken as an abnormal signal segment, and the preset abnormal threshold is set to 0.88. The specific value of the preset abnormal threshold can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0085] At this point, the abnormal signal segment has been obtained.

[0086] Step S3: Take any abnormal signal segment as the target abnormal signal segment, take the two signal segments adjacent to the target abnormal signal segment as adjacent signal segments, and obtain the amplitude difference degree of each adjacent signal segment according to the difference in the amplitude of the extreme point between the target abnormal signal segment and each adjacent signal segment; obtain the amplitude adjustment factor of each extreme point in the target abnormal signal segment according to the difference between the moment of each extreme point in the target abnormal signal segment and the initial moment and the end moment of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment, and the amplitude of each extreme point.

[0087] Since the fluctuation amplitude and frequency of abnormal signal segments are larger than those of normal signal segments, it is necessary to adjust the fluctuation of each abnormal signal segment to improve the quality of the electromyographic signal. Therefore, any abnormal signal segment is first taken as the target abnormal signal segment. In the process of adjusting the abnormal signal segment, it is necessary to make the fluctuation characteristics of the adjusted abnormal signal segment more inclined to the normal signal segment. Since multiple abnormal signal segments appear intermittently, the signal segments on both sides of them are normal signal segments. Therefore, the two signal segments adjacent to the target abnormal signal segment can be taken as adjacent signal segments. The target abnormal signal segment can be adjusted based on the adjacent signal segments later.

[0088] Since the amplitude and frequency of the fluctuation of the target abnormal signal segment are mainly reflected by the extreme points, the embodiment of the present invention adjusts the amplitude of the extreme points on the target abnormal signal segment in the subsequent steps. First, the difference in the amplitude of the extreme points between the target abnormal signal segment and each adjacent signal segment is analyzed to obtain the amplitude difference degree of each adjacent signal segment. The greater the amplitude difference degree, the greater the amplitude difference of the extreme points between the adjacent signal segment and the target abnormal signal segment, and further the greater the difference in the fluctuation characteristics between the adjacent signal segment and the target abnormal signal segment. Subsequently, based on the amplitude difference degree, the adjustment degree of the amplitude of the extreme points on the target abnormal signal segment can be analyzed, so that the fluctuation characteristics of the adjusted target abnormal signal segment are more inclined to the normal signal segments adjacent to both sides.

[0089] Preferably, in an embodiment of the present invention, the method for obtaining the amplitude difference degree of each adjacent signal segment specifically includes:

[0090] After the calculation in step S2 above, the fluctuation amplitude parameter of each signal segment can be obtained, and then the absolute value of the difference between the fluctuation amplitude parameter of each adjacent signal segment and the target abnormal signal segment can be used as the amplitude difference degree of each adjacent signal segment.

[0091] As an example, in one embodiment of the present invention, the expression of the amplitude difference degree of each adjacent signal segment may be specifically, for example, as follows:

[0092]

[0093] in, Indicates The degree of difference in amplitude between adjacent signal segments; Indicates The fluctuation amplitude parameter of adjacent signal segments; Indicates the fluctuation amplitude parameter of the target abnormal signal segment.

[0094] Since the greater the difference in fluctuation amplitude between the target abnormal signal segment and the adjacent signal segment, that is, the greater the amplitude difference, the greater the degree of amplitude adjustment of the extreme point on the target abnormal signal segment is required to ensure that the fluctuation amplitude of the adjusted abnormal signal segment is similar to that of the normal signal segment, and different adjacent signal segments have different reference values ​​in the process of adjusting the amplitude of the extreme point. At the same time, the greater the amplitude of the extreme point, the greater the fluctuation amplitude of the extreme point, and the greater the degree of adjustment is required. Therefore, the difference between the moment of each extreme point in the target abnormal signal segment and the initial moment and the end moment of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment, and the amplitude of each extreme point can be analyzed to obtain the amplitude adjustment factor of each extreme point in the target abnormal signal segment. The amplitude adjustment factor reflects the degree of amplitude adjustment of the extreme point. Subsequently, the amplitude of the extreme point can be adjusted based on the amplitude adjustment factor, thereby removing local abnormal phenomena appearing on the electromyographic signal.

[0095] Preferably, in one embodiment of the present invention, a method for obtaining the amplitude adjustment factor of each extreme point in the target abnormal signal segment specifically includes:

[0096] See also Figure 4 , which shows a schematic diagram of a method for obtaining the amplitude adjustment factor of each extreme point in a target abnormal signal segment provided by an embodiment of the present invention.

[0097] Step S301: taking any extreme point on the target abnormal signal segment as the target extreme point, adjusting the amplitude difference degree of each adjacent signal segment according to the difference between the time of the target extreme point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, and obtaining the first adjustment factor of the target extreme point.

[0098] First, any extreme point on the target abnormal signal segment is taken as the target extreme point. Since the amplitude difference degree of each adjacent signal segment is different, it means that the difference in the fluctuation characteristics between the target abnormal signal segment and each adjacent signal segment is different, which further means that in the process of adjusting the amplitude of the target extreme point, the reference values ​​of different adjacent signal segments are different, and the closer the target extreme point is to a certain adjacent signal segment, the greater the reference value of the adjacent signal segment. The distance between the target extreme point and the adjacent signal segment can be reflected by the difference between the moment of the target extreme point and the initial moment and the end moment of the target abnormal signal segment. Therefore, the amplitude difference degree of each adjacent signal segment can be adjusted according to the difference between the moment of the target extreme point in the target abnormal signal segment and the initial moment and the end moment of the target abnormal signal segment, and the first adjustment factor of the target extreme point is obtained. The larger the first adjustment factor, the greater the adjustment of the amplitude of the target extreme point needs to be. Subsequently, the amplitude adjustment factor of the target extreme point can be calculated based on the first adjustment factor.

[0099] Preferably, in one embodiment of the present invention, the method for obtaining the first adjustment factor of the target extreme value point specifically includes:

[0100] The adjacent signal segment located on the left side of the target abnormal signal segment is taken as the left adjacent signal segment, and the adjacent signal segment located on the right side of the target abnormal signal segment is taken as the right adjacent signal segment. That is to say, the end time of the left adjacent signal segment coincides with the initial time of the target abnormal signal segment, and the initial time of the right adjacent signal segment coincides with the end time of the target abnormal signal segment.

[0101] The difference between the moment of the target extreme point and the initial moment of the target abnormal signal segment is taken as the numerator, the duration of the target abnormal signal segment is taken as the denominator, and the ratio is used as the first weight of the left adjacent signal segment. The larger the first weight, the greater the reference value of the left adjacent signal segment. The difference between the end moment of the target abnormal signal segment and the moment of the target extreme point is taken as the numerator, the duration of the target abnormal signal segment is taken as the denominator, and the ratio is used as the second weight of the right adjacent signal segment. The larger the second weight, the greater the reference value of the right adjacent signal segment.

[0102] Then, the first weight and the second weight may be used to perform weighted summation on the amplitude difference between the left adjacent signal segment and the right adjacent signal segment, respectively, to obtain a first adjustment factor of the target extreme value point.

[0103] As an example, in one embodiment of the present invention, the expression of the first adjustment factor of the target extreme point may be specifically, for example, as follows:

[0104]

[0105] in, The first adjustment factor representing the target extreme point; and Respectively represent the amplitude difference between the left adjacent signal segment and the right adjacent signal segment; represents the moment of the target extreme point; and Respectively represent the initial time and the end time of the target abnormal signal segment; Indicates the duration of the target abnormal signal segment, that is, the difference between the end time and the initial time of the target abnormal signal segment; represents the first weight of the left adjacent signal segment; Represents the second weight of the right adjacent signal segment.

[0106] Step S302: normalizing the absolute value of the amplitude of the target extreme point to obtain a second adjustment factor of the target extreme point.

[0107] The larger the amplitude of the target extreme point, the greater the degree of adjustment required. Therefore, the absolute value of the amplitude of the target extreme point can be normalized to obtain the second adjustment factor of the target extreme point. The first adjustment factor and the second adjustment factor can be combined to accurately calculate the amplitude adjustment factor of the target extreme point.

[0108] Preferably, in one embodiment of the present invention, the method for obtaining the second adjustment factor of the target extreme value point specifically includes:

[0109] The absolute value of the amplitude of the target extreme point is used as the numerator, the maximum absolute value of the amplitude of all extreme points on the target abnormal signal segment is used as the denominator, and the ratio is used as the second adjustment factor of the target extreme point.

[0110] As an example, in one embodiment of the present invention, the expression of the second adjustment factor of the target extreme point may be specifically, for example, as follows:

[0111]

[0112] in, The second adjustment factor representing the target extreme point; Indicates the amplitude of the target extreme point; Indicates the first The amplitude of the extreme point; Indicates the maximum absolute value of the amplitude of all extreme points on the target abnormal signal segment, which is used to Perform normalization processing; Represents the maximum value function.

[0113] In other embodiments of the present invention, the maximum and minimum value normalization method may also be used to achieve normalization of the absolute value of the amplitude of the target extreme point, which is not limited here.

[0114] Step S303: The first adjustment factor and the second adjustment factor are integrated and normalized to obtain the amplitude adjustment factor of the target extreme value point.

[0115] The larger the first adjustment factor and the second adjustment factor of the target extreme point are, the greater the amplitude of the target extreme point needs to be adjusted in the future. Therefore, the first adjustment factor and the second adjustment factor can be combined and normalized to obtain the amplitude adjustment factor of the target extreme point.

[0116] In the embodiment of the present invention, the integration of the first adjustment factor and the second adjustment factor can be achieved by calculating the sum or product of the two factors, which is not limited here.

[0117] As an example, in one embodiment of the present invention, the expression of the amplitude adjustment factor of the target extreme point may be specifically, for example, as follows:

[0118]

[0119] in, Indicates the amplitude adjustment factor of the target extreme point; The first adjustment factor representing the target extreme point; The second adjustment factor representing the target extreme point; Represents the normalization function.

[0120] The amplitude adjustment factor of each extreme point in the target abnormal signal segment and the amplitude adjustment factor of each extreme point in each abnormal signal segment can be obtained by the same method as above.

[0121] Step S4: Based on the amplitude adjustment factor, the amplitude of the extreme point on the abnormal signal segment is adjusted to obtain an adjusted electromyographic signal.

[0122] After obtaining the amplitude adjustment factor of each extreme point on the abnormal signal segment, the larger the amplitude adjustment factor, the greater the degree of amplitude adjustment of the extreme point. Therefore, based on the amplitude adjustment factor, the amplitude of the extreme point on the abnormal signal segment can be adjusted to eliminate the local abnormal phenomenon of the electromyographic signal, so that the quality of the adjusted electromyographic signal is better.

[0123] Preferably, in one embodiment of the present invention, the method for acquiring the adjusted electromyographic signal specifically includes:

[0124] First, the product of the amplitude adjustment factor and the amplitude of each extreme point on the target abnormal signal segment is used as the amplitude adjustment amount of each extreme point.

[0125] Since the amplitude of the extreme point fluctuation in the abnormal signal segment is larger than that in the normal signal segment, the amplitude of the extreme point fluctuation on the abnormal signal segment needs to be lowered. In other words, the amplitude of the extreme point with an amplitude less than 0 needs to be increased, and the amplitude of the extreme point with an amplitude greater than 0 needs to be decreased. Therefore, the difference between the amplitude of each extreme point on the target abnormal signal segment and the amplitude adjustment amount is used as the adjusted amplitude of each extreme point, and the adjusted amplitude of each extreme point on the target abnormal signal segment is used as the new amplitude of each extreme point, thereby achieving adjustment of the amplitude of the extreme point.

[0126] As an example, in one embodiment of the present invention, the expression of the adjusted amplitude of each extreme point on the target abnormal signal segment may be specifically, for example, as follows:

[0127]

[0128] in, Indicates the first The adjustment amplitude of the extreme points; Indicates the first The amplitude of the extreme point; Indicates the first Amplitude adjustment factor for each extreme point; Indicates the first The amplitude adjustment amount for each extreme point.

[0129] Then, the extreme value points of all abnormal signal segments of the electromyographic signal can be traversed, and the amplitudes of the extreme value points can be adjusted in the same way, so as to obtain the adjusted electromyographic signal.

[0130] An embodiment of the present invention provides an electromyographic signal acquisition system for acupuncture therapy, the system comprising a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and when the computer program runs in the processor, the method described in steps S1 to S4 can be implemented.

[0131] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for collecting electromyographic signals for acupuncture therapy, characterized in that: The method comprises: Acquire the electromyographic signal of the patient during acupuncture within a preset time period, wherein the electromyographic signal is composed of sample points, and the sample points are composed of time and amplitude; cluster the sample points on the electromyographic signal to obtain multiple signal segments; Taking any signal segment as a target signal segment, obtaining the abnormal possibility of the target signal segment according to the amplitude of the extreme value points on the target signal segment, the proportion of the number of extreme value points in the number of sample points of the target signal segment, the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment; based on the abnormal possibility, screening out the abnormal signal segment from all signal segments; Taking any abnormal signal segment as the target abnormal signal segment, taking the two signal segments adjacent to the target abnormal signal segment as adjacent signal segments, obtaining the amplitude difference degree of each adjacent signal segment according to the difference in amplitude of the extreme point between the target abnormal signal segment and each adjacent signal segment; obtaining the amplitude adjustment factor of each extreme point in the target abnormal signal segment according to the difference between the time of each extreme point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment, and the amplitude of each extreme point; Based on the amplitude adjustment factor, the amplitude of the extreme point on the abnormal signal segment is adjusted to obtain an adjusted electromyographic signal; The abnormal possibilities of obtaining the target signal segment include: Analyze the overall level of the absolute values ​​of the amplitudes of all extreme value points on the target signal segment to obtain the fluctuation amplitude parameter of the target signal segment; The number of all extreme value points on the target signal segment is used as the numerator, the number of all sample points on the target signal segment is used as the denominator, and the ratio is used as the fluctuation frequency parameter of the target signal segment; The fluctuation amplitude parameter and the fluctuation frequency parameter are combined to obtain an initial abnormal parameter of the target signal segment; According to the difference between the initial time of the target signal segment and the electromyographic signal, and the difference between the initial time and the end time of the target signal segment, the initial abnormal parameter is adjusted to obtain the abnormal possibility of the target signal segment; The adjusting the initial abnormal parameter to obtain the abnormal possibility of the target signal segment includes: The difference between the initial time of the target signal segment and the initial time of the electromyographic signal is taken as the time distance of the target signal segment; The difference between the end time and the initial time of the target signal segment is taken as the duration of the target signal segment; According to the time distance and the duration, obtaining an abnormality confidence of the target signal segment, wherein the abnormality confidence is positively correlated with the time distance, and the abnormality confidence is negatively correlated with the duration; Using the abnormality confidence, the initial abnormality parameters are weighted and normalized to obtain the abnormality possibility of the target signal segment; The obtaining of the amplitude difference degree of each adjacent signal segment comprises: The absolute value of the difference between each adjacent signal segment and the target abnormal signal segment is used as the amplitude difference degree of each adjacent signal segment; The step of obtaining the amplitude adjustment factor of each extreme point in the target abnormal signal segment includes: Any extreme point on the target abnormal signal segment is taken as the target extreme point; According to the difference between the time of the target extreme value point in the target abnormal signal segment and the initial time and the end time of the target abnormal signal segment, the amplitude difference degree of each adjacent signal segment is adjusted to obtain a first adjustment factor of the target extreme value point; Normalizing the absolute value of the amplitude of the target extreme point to obtain a second adjustment factor of the target extreme point; The first adjustment factor and the second adjustment factor are integrated and normalized to obtain an amplitude adjustment factor of a target extreme value point; The first adjustment factor for obtaining the target extreme value point includes: The adjacent signal segment located on the left side of the target abnormal signal segment is regarded as the left adjacent signal segment, and the adjacent signal segment located on the right side of the target abnormal signal segment is regarded as the right adjacent signal segment; The difference between the time of the target extreme value point and the initial time of the target abnormal signal segment is used as the numerator, the duration of the target abnormal signal segment is used as the denominator, and the ratio is used as the first weight of the left adjacent signal segment; the difference between the end time of the target abnormal signal segment and the time of the target extreme value point is used as the numerator, the duration of the target abnormal signal segment is used as the denominator, and the ratio is used as the second weight of the right adjacent signal segment; Using the first weight and the second weight, weighted summation is performed on the amplitude difference degrees of the left adjacent signal segment and the right adjacent signal segment respectively to obtain a first adjustment factor of the target extreme value point; The second adjustment factor for obtaining the target extreme value point includes: The absolute value of the amplitude of the target extreme point is used as the numerator, the maximum absolute value of the amplitude of all extreme points on the target abnormal signal segment is used as the denominator, and the ratio is used as the second adjustment factor of the target extreme point.

2. A method for collecting electromyographic signals for acupuncture therapy according to claim 1, characterized in that: The step of screening out abnormal signal segments from all signal segments based on the abnormal possibility includes: The signal segment whose abnormal possibility is greater than the preset abnormal threshold is regarded as an abnormal signal segment.

3. The method for collecting electromyographic signals for acupuncture therapy according to claim 1, characterized in that: The obtaining of the adjusted electromyographic signal comprises: The product value of the amplitude adjustment factor and the amplitude of each extreme point on the target abnormal signal segment is used as the amplitude adjustment amount of each extreme point; The difference between the amplitude of each extreme point on the target abnormal signal segment and the amplitude adjustment amount is used as the adjusted amplitude of each extreme point, and the adjusted amplitude of each extreme point on the target abnormal signal segment is used as the new amplitude of each extreme point; The extreme value points of all abnormal signal segments of the electromyographic signal are traversed, and the amplitudes of the extreme value points are adjusted to obtain the adjusted electromyographic signal.

4. A myoelectric signal acquisition system for acupuncture therapy, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

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