CN112773379B - Myoelectric signal processing method, processing equipment and storage medium - Google Patents
Myoelectric signal processing method, processing equipment and storage medium Download PDFInfo
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- CN112773379B CN112773379B CN201911082011.8A CN201911082011A CN112773379B CN 112773379 B CN112773379 B CN 112773379B CN 201911082011 A CN201911082011 A CN 201911082011A CN 112773379 B CN112773379 B CN 112773379B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Abstract
The application discloses a processing method, processing equipment and storage medium of an electromyographic signal, wherein the processing method of the electromyographic signal comprises the following steps: acquiring an electromyographic signal to be processed; detecting a contraction real wave of the electromyographic signal; calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave; and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value. By the mode, the determined shrinkage peak value judgment threshold value can be more in line with different electromyographic signals, the peak value starting and stopping points of the electromyographic signals are more accurately determined, and the extraction of subsequent parameters and the detection of signals are facilitated.
Description
Technical Field
The present application relates to the technical field of electromyographic signal detection, and in particular, to a method and apparatus for processing an electromyographic signal, and a storage medium.
Background
The surface electromyographic signal (SurfaceElectromyogram, SEMG) is an important human body biological signal, and is a potential signal generated when human body muscle movement is acquired on the surface of human skin through a human body surface electrode. The source is bioelectric signals emitted by neuromuscular activity during autonomous movement of a human body, the bioelectric signals propagate along muscle fibers and are comprehensively overlapped in time and space at a skin surface detection electrode after being filtered by a volume conductor formed by skin and fat, and finally, surface electromyographic signals are formed. Because different actions trigger different muscle groups to act, the generated myoelectric signals are different, the myoelectric signals have unique advantages in the aspect of identifying human actions, and are widely applied to the fields of artificial limb control, rehabilitation training, clinical medicine, sports science and the like.
After the surface electromyographic signals are detected, how to obtain the needed parameters from the surface electromyographic signals has a vital effect on the subsequent human health detection and rehabilitation.
Disclosure of Invention
In order to solve the problems, the application provides a processing method, processing equipment and a storage medium of an electromyographic signal, which can enable a determined shrinkage peak value judgment threshold value to be more in line with different electromyographic signals, enable the peak value starting and stopping points of the electromyographic signal to be more accurately determined, and facilitate the extraction of subsequent parameters and the detection of signals.
The application adopts a technical scheme that: provided is a method for processing an electromyographic signal, comprising: acquiring an electromyographic signal to be processed; detecting a contraction real wave of the electromyographic signal; calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave; and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value.
Wherein, detect the real wave of contraction of electromyographic signal, include: detecting a contraction starting point of the electromyographic signals to determine a plurality of effective contraction waves; acquiring amplitude information and width information of a plurality of effective contraction waves; and determining a real shrinkage wave from the plurality of effective shrinkage waves according to the amplitude information and the width information of the plurality of effective shrinkage waves.
Wherein determining a real shrinkage wave from the plurality of effective shrinkage waves based on the amplitude information and the width information of the plurality of effective shrinkage waves, comprises: calculating an average amplitude and/or an average width of the plurality of effective contraction waves; calculating a first ratio of the amplitude information of the current effective shrinkage wave to the average amplitude, and determining that the current effective shrinkage wave is a shrinkage real wave when the first ratio accords with a set first preset range; and/or calculating a second ratio of the width information of the current effective shrinkage wave to the average width, and determining that the current effective shrinkage wave is a shrinkage real wave when the second ratio accords with a set second preset range.
Wherein detecting a contraction start-stop point of the electromyographic signal to determine a plurality of effective contraction waves comprises: determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction starting point of the electromyographic signal is detected to determine a plurality of effective contraction waves.
Wherein, according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value, including: acquiring a plurality of actual electromyographic signal values in the electromyographic signals; comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value; when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; or when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is smaller than the set proportion threshold value, regulating down the first baseline frequency regulating value to obtain a second baseline frequency regulating value.
Wherein detecting a contraction start-stop point of the electromyographic signal based on the second baseline frequency adjustment value comprises: comparing the electromyographic signal values with a second baseline frequency adjustment value in sequence; when the detected first electromyographic signal value is larger than the second baseline frequency adjustment value, taking the first electromyographic signal value as a starting point; and taking the second electromyographic signal value as an end point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the second baseline frequency adjustment value.
According to the amplitude information and the variation coefficient of the shrinkage real wave, calculating a shrinkage peak value judgment threshold value comprises the following steps: calculating the average amplitude and the variation coefficient of the shrinkage real wave; when the variation coefficient is smaller than a set coefficient threshold value, determining a set proportion value of the average amplitude value as a shrinkage peak value judgment threshold value; or when the variation coefficient is larger than the set coefficient threshold value, determining the sum of the set proportion value of the average amplitude and the variation coefficient as the shrinkage peak value judgment threshold value.
Wherein, confirm the shrink peak value starting point of the electromyographic signal according to shrink peak value judgement threshold value, include: comparing each electromyographic signal value in the current contraction real wave with a contraction peak value judgment threshold value in sequence; when the detected first electromyographic signal value is larger than the contraction peak value judgment threshold value, the first electromyographic signal value is used as a contraction peak value starting point; and taking the second electromyographic signal value as a contraction peak value ending point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the contraction peak value judgment threshold value.
The application adopts another technical scheme that: there is provided a processing device of electromyographic signals comprising a processor and a memory, interconnected, the memory having stored therein program data, the processor being adapted to execute the program data to carry out a method as described above.
The application adopts another technical scheme that: there is provided a computer storage medium having stored therein program data which, when executed by a processor, is adapted to carry out a method as described above.
The electromyographic signal processing method provided by the application comprises the following steps: acquiring an electromyographic signal to be processed; detecting a contraction real wave of the electromyographic signal; calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave; and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value. By the mode, the shrinkage peak value judgment threshold value can be dynamically regulated according to the electromyographic signals, so that the determined shrinkage peak value judgment threshold value can be more in line with different electromyographic signals, the determination of the peak value starting and stopping points of the electromyographic signals is more accurate, and the extraction of subsequent parameters and the detection of signals are facilitated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a schematic flow chart of a first embodiment of a method for processing an electromyographic signal according to the application;
FIG. 2 is a flow chart of determining a first baseline frequency adjustment value in one embodiment;
FIG. 3 is a schematic diagram of the amplitude-frequency distribution of the electromyographic signals in an embodiment;
FIG. 4 is a flowchart illustrating an adjustment of a first baseline frequency adjustment value according to an embodiment;
FIG. 5 is a flow chart of determining a shrink start-stop point in one embodiment;
fig. 6 is a schematic flow chart of a second embodiment of a method for processing an electromyographic signal according to the application;
FIG. 7 is a schematic flow chart of correcting the contraction start point of the electromyographic signal in an embodiment;
FIG. 8 is a flow chart of detecting a contraction peak start/stop point of an electromyographic signal according to an embodiment;
fig. 9 is a schematic flow chart of a third embodiment of a method for processing an electromyographic signal according to the application;
FIG. 10 is a flow chart of detecting a contracting real wave of an electromyographic signal in an embodiment;
FIG. 11 is a flowchart of calculating a shrinkage peak judgment threshold value according to an embodiment;
FIG. 12 is a flow chart of determining a contraction peak start-stop point of an electromyographic signal in an embodiment;
fig. 13 is a schematic structural view of an electromyographic signal processing apparatus provided by the present application;
fig. 14 is a schematic structural view of a computer storage medium according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," and the like in this disclosure are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 is a flow chart of a first embodiment of a method for processing an electromyographic signal, where the method includes:
step 11: and acquiring an electromyographic signal to be processed.
The surface electromyographic signal (SurfaceElectromyogram, SEMG) is an important human body biological signal, and is a potential signal generated when human body muscle movement is acquired on the surface of human skin through a human body surface electrode. The source is bioelectric signals emitted by neuromuscular activity during autonomous movement of a human body, the bioelectric signals propagate along muscle fibers and are comprehensively overlapped in time and space at a skin surface detection electrode after being filtered by a volume conductor formed by skin and fat, and finally, surface electromyographic signals are formed.
Alternatively, the electromyographic signal may specifically be a pelvic floor electromyographic signal, i.e. an electromyographic signal acquired by detecting the pelvic floor muscle through the vaginal electrode.
Step 12: a first baseline frequency adjustment value of the electromyographic signal is determined.
The baseline frequency adjustment value is used to determine the systolic band in the electromyographic signal, and it is understood that a band having an electromyographic signal value greater than the baseline frequency adjustment value is used as the effective systolic wave, and a band having an electromyographic signal value less than the baseline frequency adjustment value is used as the ineffective systolic wave.
As shown in fig. 2, fig. 2 is a flowchart illustrating determining a first baseline frequency adjustment value in an embodiment, and step 12 may include:
step 121: the electromyographic signals are preprocessed.
Alternatively, the preprocessing may include both a filtering process and an amplitude normalization process. Specifically, after the electromyographic signals are subjected to filtering treatment, the electromyographic signals are subjected to moving average treatment by adopting a set window function; that is, a certain point in the electromyographic signals is set as the center of the window function, the average value of a plurality of electromyographic signal values in the window function is set as the electromyographic signal value of the point, and then the window is slid until the above-described processing is completed for each electromyographic signal value. And then normalizing the electromyographic signals according to the maximum value and the minimum value of the electromyographic signals after the moving average processing. For example, the maximum electromyographic signal value is taken as 1, the minimum electromyographic signal value is taken as 0, and the other electromyographic signal values are normalized to a value between 0 and 1.
Step 122: and performing gain adjustment on a plurality of electromyographic signal values of the electromyographic signal after preprocessing according to the set electromyographic signal fixed value.
Wherein the myoelectric signal fixed value is preset for being used as a reference value.
Alternatively, step 122 may specifically be: carrying out integer processing on the electromyographic signals; and taking the set fixed value of the electromyographic signals as a reference, and performing gain adjustment on a plurality of electromyographic signal values in the electromyographic signals so as to adjust the maximum electromyographic signal value in the electromyographic signals to be the same as the set fixed value of the electromyographic signals.
For example, the myoelectric signal values are distributed as follows: s is S 1 、S 2 、……S n The myoelectric signal has a fixed value of S g . Then first from S 1 、S 2 、……S n In (1) detecting the maximum myoelectric signal value, e.g. S i Calculating a gain coefficient: x=s g /S i And then performing gain adjustment on each electromyographic signal value according to the calculated gain coefficient x. In particular for the electromyographic signal value S therein i Becomes the fixed value S after adjustment g 。
Step 123: and counting the frequency distribution condition of a plurality of electromyographic signal values of the electromyographic signal after gain adjustment.
As shown in fig. 3, fig. 3 is a schematic diagram of amplitude-frequency distribution of an electromyographic signal in an embodiment. Wherein the abscissa is frequency (f/Hz) and the ordinate is amplitude (U/V).
In an alternative embodiment, the frequency distribution of a certain electromyographic signal is in the range of 100Hz-1000Hz.
Step 124: and determining a first baseline frequency adjustment value of the electromyographic signal when the frequency value corresponding to the target electromyographic signal value in the plurality of electromyographic signal values is the maximum value in a set width window taking the target electromyographic signal as the center.
Step 124 may specifically be: acquiring a frequency distribution curve of the electromyographic signals; calculating the total area corresponding to the frequency distribution curve; determining a target electromyographic signal value corresponding to a set proportion position of a corresponding total area in a frequency distribution curve; restoring the target electromyographic signal value into an actual electromyographic signal value before preprocessing; the actual electromyographic signal value is taken as a first baseline frequency adjustment value.
For example, the curve of fig. 3, the area S of the curve from 100Hz to 1000Hz is calculated, and the electromyographic signal value corresponding to S/3 is further restored to the actual electromyographic signal value before preprocessing, and the restored actual electromyographic signal value is used as the first baseline frequency adjustment value. Wherein the area coefficient 1/3 can be set according to actual requirements, and is not limited herein.
Step 13: and adjusting the first baseline frequency adjustment value according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value so as to obtain a second baseline frequency adjustment value.
As shown in fig. 4, fig. 4 is a schematic diagram of an adjustment flow of the first baseline frequency adjustment value in an embodiment, and step 13 may specifically include:
step 131: a plurality of actual electromyographic signal values in the electromyographic signal are acquired.
Note that, in the foregoing step, the electromyographic signals are subjected to integers, gain adjustment, and the like, but the electromyographic signals used in this step are the actual electromyographic signal values acquired.
Step 132: and comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value.
After step 132, step 133 or step 134 is optionally performed.
For example, the electromyographic signal value S may be acquired for each acquisition according to time sequence 1 、S 2 、……S n And comparing the magnitude of the first baseline frequency adjustment value with the magnitude of the first baseline frequency adjustment value, and counting the quantity larger than the first baseline frequency adjustment value. A proportional threshold value may be preset for comparing the number greater than the first baseline frequency adjustment value to the ratio of the total number of electromyographic signal values.
Step 133: and when the ratio of the target number to the total number of the actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value.
Step 134: and when the ratio of the target number to the total number of the actual electromyographic signal values is smaller than the set proportion threshold value, regulating down the first baseline frequency regulating value to obtain a second baseline frequency regulating value.
Step 14: based on the second baseline frequency adjustment value, a contraction start-stop point of the electromyographic signal is detected.
Optionally, as shown in fig. 5, fig. 5 is a schematic flow chart of determining a contraction start-stop point in an embodiment, and step 14 may specifically include:
step 141: the plurality of electromyographic signal values of the electromyographic signal are sequentially compared to a second baseline frequency adjustment value.
Step 142: and taking the first electromyographic signal value as a starting point when the detected first electromyographic signal value is larger than the second baseline frequency adjustment value.
Step 143: and when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the second baseline frequency adjustment value, taking the second electromyographic signal value as an end point.
For example, the number of the cells to be processed,the electromyographic signal value S to be acquired 1 、S 2 、……S n Comparing with the second baseline frequency adjustment value A, if S 1 Less than A and S 2 Greater than A, S is 2 The corresponding point serves as a starting point. Further, if S 2 -S n-1 All signal values in between are greater than A and S n Less than A, S is n As end point.
It will be appreciated that the above-identified start and stop points of the compressional wave may be active or inactive because of the large signal values at which the acquired signal may appear to be abrupt. In an embodiment, a plurality of ratios of a plurality of electromyographic signal values between a starting point and an ending point to a maximum electromyographic signal value, respectively, may be detected; when the plurality of ratios are within the set threshold range, the start point and the end point are determined as contraction start points of an effective contraction wave.
Unlike the prior art, the method for processing the electromyographic signal provided by the embodiment includes: acquiring an electromyographic signal to be processed; determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction start-stop point of the electromyographic signal is detected. By correcting the baseline frequency adjustment value in the mode, the determined start and stop points of the contraction wave can be more accurate, the accuracy of subsequent parameter extraction is ensured, and the electromyographic signal detection quality is further improved.
Referring to fig. 6, fig. 6 is a flow chart of a second embodiment of a method for processing an electromyographic signal, which includes:
step 61: and acquiring an electromyographic signal to be processed.
Step 62: and detecting the contraction starting point of the electromyographic signal.
Step 62 may specifically include: determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction start-stop point of the electromyographic signal is detected.
When the first baseline frequency adjustment value is corrected, a plurality of actual electromyographic signal values in the electromyographic signals can be obtained; comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value; when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; or when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is smaller than the set proportion threshold value, regulating down the first baseline frequency regulating value to obtain a second baseline frequency regulating value.
It will be appreciated that, in the present embodiment, the step 62 may specifically be adopted in the manner of the embodiment shown in fig. 1-5 when detecting the contraction start-stop point of the electromyographic signal, and the steps and principles are similar, and will not be repeated here.
Step 63: correcting the contraction starting point of the electromyographic signals.
As shown in fig. 7, fig. 7 is a schematic flow chart of correcting the contraction start-stop point of the electromyographic signal in an embodiment, and step 63 may specifically include:
step 631: and determining the correction type of the electromyographic signals according to the contraction starting point of the electromyographic signals.
The differential signal of the electromyographic signal can be calculated first, a stable section and a non-stable section of the differential signal are determined, and then the correction type of the electromyographic signal is determined by utilizing the matching degree of the stable section and the non-stable section of the differential signal and the contraction starting point of the electromyographic signal.
Alternatively, the original electromyographic signal may be subjected to a second differential process to obtain the differential signal described above.
Specifically, a window function with a certain length is selected, corresponding short-time energy is calculated for the secondary differential signal in the window function with a specified length, the calculated short-time energy value in the window is recorded as a time value corresponding to a middle point of the window function, so that a secondary differential short-time energy spectrum of the electromyographic signal is further obtained, the short-time energy spectrum is compared with a certain preset threshold, if a certain point energy value is detected to be higher than the preset threshold and a previous point energy value is detected to be lower than the preset threshold, the point is recorded as a starting endpoint, and if a certain point energy value is detected to be lower than the preset threshold and a previous point energy value is detected to be higher than the preset threshold, the point is recorded as an ending endpoint. And marks the data segment between the corresponding start endpoint and end endpoint as a non-stationary data segment, and the data segment between the last end endpoint and the next start endpoint as a stationary segment.
The myoelectric signal correction type mainly comprises the following three cases:
first, if the contraction start-stop point of the electromyographic signal is matched with the end point start-stop point of the non-stationary section of the differential signal, determining as the first correction type.
Second, if the contraction starting point of the electromyographic signal is matched with only one end point of the end point starting point of the non-stationary section of the differential signal, determining as the second correction type.
And thirdly, determining the third correction type if the contraction starting point of the electromyographic signal is not matched with the starting point of the end point of the non-stationary section of the differential signal.
Step 632: correcting the contraction starting point of the electromyographic signal based on the correction type.
For the three different types, the correction is performed in different ways.
For the first correction type, an average value of the contraction starting point of the electromyographic signal and the end point starting point of the non-stationary section of the differential signal is adopted as a new contraction starting point of the electromyographic signal.
For example, the contraction starting points of the electromyographic signals are respectively t 1 And t 2 The starting and ending points of the non-stationary section are respectively T 1 And T 2 Will (t) 1 +T 1 ) And (t) 2 +T 2 ) And/2 is used as a new contraction starting point of the electromyographic signals.
And for the second correction type, when the endpoint detection value corresponding to the non-matched target endpoint is smaller than the start and stop point detection value corresponding to the non-matched target start and stop point, the electromyographic signal value between the target endpoint and the target start and stop point is in a descending trend, and the electromyographic signal value corresponding to the target endpoint is larger than the set detection baseline threshold, the contraction start and stop point of the electromyographic signal is unchanged, and otherwise, the target endpoint is used as a new contraction start and stop point of the electromyographic signal.
For example, the contraction starting points of the electromyographic signals are respectively t 1 And t 2 The starting and ending points of the non-stationary section are respectively T 1 And T 2 . Wherein t is 1 And T 1 And do not match. Then, if T 1 The corresponding electromyographic signal value is less than t 1 Corresponding myoelectric signal value, and T 1 To t 1 The electromyographic signal value between the two is in a descending trend and T 1 When the corresponding electromyographic signal value is larger than the set detection baseline threshold value, t is still set 1 And (3) taking the T1 as the contraction starting point of the electromyographic signal, otherwise taking the T1 as the contraction starting point of the electromyographic signal.
And for the third correction type, taking one of the contraction starting point and the end point starting point which is more in accordance with the set width range as a new contraction starting point of the electromyographic signals.
For example, a width range is preset, and which of the contraction start point and the end point start point is more consistent with the preset width range is detected, and the more consistent one is used as a new contraction start point of the electromyographic signal.
Step 64: and detecting the contraction peak starting point of the electromyographic signal between the contraction starting points of the corrected electromyographic signal.
As shown in fig. 8, fig. 8 is a schematic flow chart of detecting a contraction peak start-stop point of the electromyographic signal in an embodiment, and step 64 may specifically include:
step 641: the maxima and minima of the differential signal between the shrink start and stop points are determined.
The differential signal corresponding to the portion between the contraction start points and the contraction start points of the electromyographic signals can be determined first, the differential signal is subjected to smoothing processing, and the maximum value and the minimum value of the differential signal after the smoothing processing are obtained.
Step 642: the maximum value is taken as the starting point value of the contraction peak value of the electromyographic signal.
Step 643: the minimum value is taken as the end point value of the contraction peak value of the electromyographic signal.
Optionally, after determining the start-stop point of the contraction peak of the electromyographic signal, the parameters required therein may be further detected.
For example, the electromyographic signal maximum value, the electromyographic signal mean value and the signal variation coefficient between the contraction peak start and stop points of the electromyographic signal are extracted. The coefficient of variation, also called "discrete coefficient" (coefficient of variation), is a normalized measure of the degree of dispersion of the probability distribution, defined as the ratio of standard deviation to average.
For example, a relaxation period of the electromyographic signals is marked between the last contraction ending point and the next contraction starting point, and the maximum value of the electromyographic signals, the average value of the electromyographic signals and the signal variation coefficient in the relaxation period are calculated.
For example, the recruitment time of the electromyographic signal is detected from the contraction starting point and the contraction peak starting point of the electromyographic signal. The recruitment time refers to the time of recruitment exercise when the muscle is excited.
For example, the recovery time of the disc bottom electromyographic signal is detected from the contraction end point and the contraction peak end point of the electromyographic signal.
Unlike the prior art, the method for processing the electromyographic signal provided by the embodiment includes: acquiring an electromyographic signal to be processed; detecting a contraction starting point of the electromyographic signal; correcting a contraction starting point of the electromyographic signal; and detecting the contraction peak starting point of the electromyographic signal between the contraction starting points of the corrected electromyographic signal. By means of the mode, the contraction starting and stopping points are corrected before the contraction peak starting and stopping points of the electromyographic signals are detected, accuracy of the contraction peak starting and stopping points is guaranteed, guarantee is provided for subsequent parameter extraction and detection, and electromyographic signal detection quality is improved.
Referring to fig. 9, fig. 9 is a schematic flow chart of a third embodiment of a method for processing an electromyographic signal, which includes:
step 91: and acquiring an electromyographic signal to be processed.
Step 92: and detecting the contraction real wave of the electromyographic signal.
As shown in fig. 10, fig. 10 is a schematic flow chart of detecting a real contraction wave of an electromyographic signal in an embodiment, and step 92 may specifically include:
step 921: the contraction onset points of the electromyographic signals are detected to determine a plurality of effective contraction waves.
Step 921 may specifically include: determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction start-stop point of the electromyographic signal is detected.
When the first baseline frequency adjustment value is corrected, a plurality of actual electromyographic signal values in the electromyographic signals can be obtained; comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value; when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; or when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is smaller than the set proportion threshold value, regulating down the first baseline frequency regulating value to obtain a second baseline frequency regulating value.
Wherein, when detecting the contraction start-stop point of the electromyographic signal based on the second baseline frequency adjustment value, the electromyographic signal values can be sequentially compared with the second baseline frequency adjustment value; when the detected first electromyographic signal value is larger than the second baseline frequency adjustment value, taking the first electromyographic signal value as a starting point; and taking the second electromyographic signal value as an end point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the second baseline frequency adjustment value.
It can be appreciated that, in the step 921 of this embodiment, the manner of detecting the contraction start-stop point of the electromyographic signal as in the embodiment of fig. 1 to 5 may be specifically adopted, and the steps and principles are similar, and are not repeated here.
Step 922: amplitude information and width information of a plurality of effective contraction waves are acquired.
The amplitude information refers to an electromyographic signal value corresponding to each acquisition time point in an effective contraction wave band, and the width information refers to the time length between the starting time and the ending time of the effective contraction wave band.
Step 923: and determining a real shrinkage wave from the plurality of effective shrinkage waves according to the amplitude information and the width information of the plurality of effective shrinkage waves.
Alternatively, the shrinkage real wave may be determined in the following manner:
calculating an average amplitude and/or an average width of the plurality of effective contraction waves; calculating a first ratio of the amplitude information of the current effective shrinkage wave to the average amplitude, and determining that the current effective shrinkage wave is a shrinkage real wave when the first ratio accords with a set first preset range; and/or calculating a second ratio of the width information of the current effective shrinkage wave to the average width, and determining that the current effective shrinkage wave is a shrinkage real wave when the second ratio accords with a set second preset range.
For example, first average amplitude values of all effective shrinkage wave bands are calculated, then a ratio of the second average amplitude value to the first average amplitude value of the current effective shrinkage wave is judged, if the ratio accords with a set first preset range, the current effective shrinkage wave is determined to be a shrinkage real wave, and otherwise, the current effective shrinkage wave is determined to be a shrinkage pseudo wave.
For example, the average width value of all the effective contraction bands is calculated, then the ratio of the width value of the current effective contraction wave to the average width value is judged, if the ratio accords with the second preset range, the current effective contraction wave is determined to be the contraction real wave, otherwise, the current effective contraction wave is determined to be the contraction pseudo wave.
Step 93: and calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave.
As shown in fig. 11, fig. 11 is a flowchart illustrating calculation of the shrinkage peak judgment threshold value in an embodiment, and step 93 may specifically include:
step 931: and calculating the average amplitude and the variation coefficient of the shrinkage real wave.
The method comprises the steps of firstly calculating the average amplitude and standard deviation of the current shrinkage real wave, and taking the ratio of the standard deviation to the average amplitude as a variation coefficient.
Step 932: and when the variation coefficient is smaller than the set coefficient threshold value, determining the set proportion value of the average amplitude value as a shrinkage peak value judgment threshold value.
For example, if the ratio is 80%, the average amplitude is 80% of the shrinkage peak judgment threshold value when the coefficient of variation is smaller than the set coefficient threshold value.
Step 933: and when the variation coefficient is larger than the set coefficient threshold value, determining the sum of the set proportion value of the average amplitude and the variation coefficient as a shrinkage peak value judgment threshold value.
For example, if the ratio is 80%, the sum of 80% of the average amplitude and the coefficient of variation is the shrinkage peak judgment threshold value when the coefficient of variation is larger than the set coefficient threshold value.
Step 94: and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value.
As shown in fig. 12, fig. 12 is a schematic flow chart of determining a contraction peak start-stop point of the electromyographic signal in an embodiment, and step 94 may specifically include:
step 941: and comparing each electromyographic signal value in the current contraction real wave with a contraction peak value judgment threshold value in sequence.
Step 942: and when the detected first electromyographic signal value is larger than the contraction peak judgment threshold value, taking the first electromyographic signal value as a contraction peak starting point.
Step 943: and when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the contraction peak value judgment threshold value, taking the second electromyographic signal value as a contraction peak value end point.
For example, the electromyographic signal value S to be acquired 1 、S 2 、……S n Comparing with a shrinkage peak value judgment threshold B, if S 1 Less than A and S 2 Greater than BWill S 2 The corresponding point serves as a starting point. Further, if S 2 -S n-1 All signal values in between are greater than B and S n Less than B, S is n As end point.
Unlike the prior art, the method for processing the electromyographic signal provided by the embodiment includes: acquiring an electromyographic signal to be processed; detecting a contraction real wave of the electromyographic signal; calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave; and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value. By the mode, the shrinkage peak value judgment threshold value can be dynamically regulated according to the electromyographic signals, so that the determined shrinkage peak value judgment threshold value can be more in line with different electromyographic signals, the determination of the peak value starting and stopping points of the electromyographic signals is more accurate, and the extraction of subsequent parameters and the detection of signals are facilitated.
Referring to fig. 13, fig. 13 is a schematic structural diagram of an electromyographic signal processing device provided by the present application, where the processing device 130 includes a processor 131 and a memory 132 that are connected to each other, where the memory 132 stores program data, and the processor 131 is configured to execute the program data to implement the following method steps:
acquiring an electromyographic signal to be processed; detecting a contraction real wave of the electromyographic signal; calculating a shrinkage peak value judgment threshold according to the amplitude information and the variation coefficient of the shrinkage real wave; and determining a contraction peak value starting and stopping point of the electromyographic signal according to the contraction peak value judging threshold value.
Optionally, in an embodiment, the processor 131 is further configured to perform: detecting a contraction starting point of the electromyographic signals to determine a plurality of effective contraction waves; acquiring amplitude information and width information of a plurality of effective contraction waves; and determining a real shrinkage wave from the plurality of effective shrinkage waves according to the amplitude information and the width information of the plurality of effective shrinkage waves.
Optionally, in an embodiment, the processor 131 is further configured to perform: calculating an average amplitude and/or an average width of the plurality of effective contraction waves; calculating a first ratio of the amplitude information of the current effective shrinkage wave to the average amplitude, and determining that the current effective shrinkage wave is a shrinkage real wave when the first ratio accords with a set first preset range; and/or calculating a second ratio of the width information of the current effective shrinkage wave to the average width, and determining that the current effective shrinkage wave is a shrinkage real wave when the second ratio accords with a set second preset range.
Optionally, in an embodiment, the processor 131 is further configured to perform: determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction starting point of the electromyographic signal is detected to determine a plurality of effective contraction waves.
Optionally, in an embodiment, the processor 131 is further configured to perform: acquiring a plurality of actual electromyographic signal values in the electromyographic signals; comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value; when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; or when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is smaller than the set proportion threshold value, regulating down the first baseline frequency regulating value to obtain a second baseline frequency regulating value.
Optionally, in an embodiment, the processor 131 is further configured to perform: comparing the electromyographic signal values with a second baseline frequency adjustment value in sequence; when the detected first electromyographic signal value is larger than the second baseline frequency adjustment value, taking the first electromyographic signal value as a starting point; and taking the second electromyographic signal value as an end point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the second baseline frequency adjustment value.
Optionally, in an embodiment, the processor 131 is further configured to perform: calculating the average amplitude and the variation coefficient of the shrinkage real wave; when the variation coefficient is smaller than a set coefficient threshold value, determining a set proportion value of the average amplitude value as a shrinkage peak value judgment threshold value; or when the variation coefficient is larger than the set coefficient threshold value, determining the sum of the set proportion value of the average amplitude and the variation coefficient as the shrinkage peak value judgment threshold value.
Optionally, in an embodiment, the processor 131 is further configured to perform: comparing each electromyographic signal value in the current contraction real wave with a contraction peak value judgment threshold value in sequence; when the detected first electromyographic signal value is larger than the contraction peak value judgment threshold value, the first electromyographic signal value is used as a contraction peak value starting point; and taking the second electromyographic signal value as a contraction peak value ending point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the contraction peak value judgment threshold value.
Referring to fig. 14, fig. 14 is a schematic structural diagram of a computer storage medium provided in the present application, in which program data 141 is stored in the computer storage medium 140, and the program data 141, when executed by a processor, is configured to implement the following method steps:
acquiring an electromyographic signal to be processed; determining a first baseline frequency adjustment value of the electromyographic signal; according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; based on the second baseline frequency adjustment value, a contraction start-stop point of the electromyographic signal is detected.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units of the other embodiments described above may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes according to the present application and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the present application.
Claims (5)
1. A method of processing an electromyographic signal, the method comprising:
acquiring an electromyographic signal to be processed;
determining a first baseline frequency adjustment value of the electromyographic signal;
according to the comparison condition of a plurality of electromyographic signal values in the electromyographic signals and the first baseline frequency adjustment value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value;
detecting a contraction start-stop point of the electromyographic signal based on the second baseline frequency adjustment value to determine a plurality of effective contraction waves;
acquiring amplitude information and width information of the plurality of effective contraction waves;
calculating an average amplitude and/or an average width of the plurality of effective shrinkage waves;
calculating a first ratio of the amplitude information of the current effective shrinkage wave to the average amplitude, and determining that the current effective shrinkage wave is a shrinkage real wave when the first ratio accords with a first preset range; and/or calculating a second ratio of the width information of the current effective shrinkage wave to the average width, and determining that the current effective shrinkage wave is a shrinkage real wave when the second ratio accords with a set second preset range;
calculating the average amplitude and the variation coefficient of the shrinkage real wave; wherein the variation coefficient is the ratio of the standard deviation and the average amplitude of the shrinkage real wave;
when the variation coefficient is smaller than a set coefficient threshold value, determining a set proportion value of the average amplitude as a shrinkage peak value judgment threshold value; or when the variation coefficient is larger than the set coefficient threshold value, determining that the sum of the set proportion value of the average amplitude and the variation coefficient is a shrinkage peak value judgment threshold value;
comparing each electromyographic signal value in the current contraction real wave with the contraction peak value judgment threshold value in sequence;
when the detected first electromyographic signal value is larger than the contraction peak value judgment threshold value, the first electromyographic signal value is used as a contraction peak value starting point; and taking the second electromyographic signal value as a contraction peak end point when the first detected second electromyographic signal value after the first electromyographic signal value is smaller than the contraction peak judgment threshold value.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the step of adjusting the first baseline frequency adjustment value according to the comparison condition of the electromyographic signal values and the first baseline frequency adjustment value to obtain a second baseline frequency adjustment value includes:
acquiring a plurality of actual electromyographic signal values in the electromyographic signals;
comparing the actual electromyographic signal values with the first baseline frequency adjustment value respectively, and counting the target number of the actual electromyographic signal values which are larger than the first baseline frequency adjustment value;
when the ratio of the target number to the total number of the plurality of actual electromyographic signal values is greater than a set proportion threshold value, the first baseline frequency adjustment value is adjusted to obtain a second baseline frequency adjustment value; or (b)
And when the ratio of the target number to the total number of the actual electromyographic signal values is smaller than a set proportion threshold value, reducing the first baseline frequency adjustment value to obtain a second baseline frequency adjustment value.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the detecting a contraction start-stop point of the electromyographic signal based on the second baseline frequency adjustment value includes:
comparing a plurality of electromyographic signal values of the electromyographic signal with the second baseline frequency adjustment value in sequence;
when the detected first electromyographic signal value is greater than the second baseline frequency adjustment value, taking the first electromyographic signal value as a starting point; and
and when a first detected second electromyographic signal value after the first electromyographic signal value is smaller than the second baseline frequency adjustment value, taking the second electromyographic signal value as an end point.
4. A processing device of electromyographic signals, characterized in that it comprises a processor and a memory connected to each other, said memory having stored therein program data, said processor being adapted to execute said program data to implement the method according to any of claims 1-3.
5. A computer storage medium, characterized in that the computer storage medium has stored therein program data, which, when being executed by a processor, is adapted to carry out the method according to any one of claims 1-3.
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