CN114569112A - Scoliosis assessment method applying surface electromyography thin-film electrode - Google Patents

Scoliosis assessment method applying surface electromyography thin-film electrode Download PDF

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CN114569112A
CN114569112A CN202210104098.XA CN202210104098A CN114569112A CN 114569112 A CN114569112 A CN 114569112A CN 202210104098 A CN202210104098 A CN 202210104098A CN 114569112 A CN114569112 A CN 114569112A
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scoliosis
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王玮
李光林
刘志远
赵阳
李青松
孙静
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Shenzhen Institute of Advanced Technology of CAS
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Abstract

The invention relates to a scoliosis assessment method applying a surface electromyography thin-film electrode, in particular to a quantitative assessment method for judging whether scoliosis risks exist or not. The method includes the steps that electromyographic signals are collected on the basis of a surface electromyographic thin film electrode array, a muscle cooperation matrix is further obtained, symmetry indexes of electromyographic activities on the left side and the right side of a spine in each horizontal direction are calculated through the muscle cooperation matrix, and whether the possibility of scoliosis exists or not is further evaluated. Compared with the traditional medical image evaluation method, the method has the advantages that the detection speed is increased and the cost is reduced; compared with visual evaluation, the reliability and the precision of detection are improved; compared with the method for evaluating only the absolute myoelectric value related parameters, the method not only improves the reliability of evaluation, but also can accurately position the area with abnormal myoelectric activity.

Description

Scoliosis assessment method applying surface electromyography thin-film electrode
Technical Field
The disclosure relates to a medical detection technology, in particular to a scoliosis assessment method applying a surface electromyography thin film electrode.
Background
Idiopathic scoliosis is a spinal deformity disease which is highly developed in adolescents, and the pathogenesis of the disease is unknown up to now. The current clinic diagnosis of scoliosis mainly takes bony deformity obtained by medical imaging as an auxiliary to visually evaluate the body surface characteristics of a tested person. However, the medical image has the problems of radiation, time-consuming process, more site requirements and high economic cost, and is not suitable for large-scale screening of scoliosis of teenagers; in the early lateral bending stage, the body surface characteristics of the patient usually have no obvious symptoms and have limited response degree to the disease condition, so that the visual evaluation of the body surface characteristics has higher false negative and false positive rates under the condition.
As an important tissue for maintaining the posture and the function of the spine, a plurality of domestic and foreign researches prove that the back muscle of a patient with lateral bending presents abnormal myoelectric activity characteristics, but the lateral bending of the spine generally causes the form change of a large-area spine area, the area dimension related to the back muscle is large, the muscle groups are various, the information of the conventional single myoelectric is only a point outline, and the myoelectric information of the large-area lateral bending area cannot be obtained. Meanwhile, the daily activity range of the spine area is large, skin pulling and muscle torsion deformation can be large when the spine is bent, the existing discrete high-density electrode array has the limitations of easy pulling and falling, long time consumption for preparation arrangement and the like, and the fitting degree of a measuring point cannot be ensured.
Therefore, there is currently a lack of efficient, reliable and rapid methods for detecting and assessing the electrophysiological activity of the scoliotic muscles.
Disclosure of Invention
In view of this, the main object of the present invention is to provide a quantitative evaluation method for determining whether there is a scoliosis risk based on measured back multichannel electromyographic signals, wherein the method evaluates the weight and symmetry of the electromyographic activities on the left and right sides of the spine in each horizontal direction by analyzing the cooperation condition of each group of muscles and then comprehensively evaluating, and can improve the reliability of evaluation and accurately locate the area with abnormal electromyographic activities compared with the conventional method in which only the absolute electromyographic value related parameters are evaluated.
The technical scheme includes that the scoliosis assessment method applying the surface electromyographic membrane electrodes collects electromyographic signals based on a surface electromyographic membrane electrode array, then obtains a muscle cooperation matrix, calculates symmetry indexes of electromyographic activities of the left side and the right side of the spine in each horizontal direction through the muscle cooperation matrix, and then assesses whether possibility of scoliosis exists.
Preferably, in the method, the raw data collected based on the surface electromyography thin film electrode array comprises the following steps:
s100, uniformly distributing surface electromyography thin film electrode arrays on two sides of a spinal midline of a tested person;
s200, recording the electromyographic signals while completing the maximum amplitude dynamic continuous forward bending and backward stretching actions of the spine on the sagittal plane of the human body by the tested person, and obtaining the original data.
Preferably, in the method, the muscle synergy matrix is obtained by:
s101, forming a matrix A by the collected myoelectric data after all channels are preprocessed under t-column time framem×tM is the number of all myoelectric channels;
s201, a non-negative matrix factorization algorithm pair Am×tFactorizing to obtain Wm×nSYN，HnSYN×tSo that | | | A-Wm×nSYNHnSYN×t||＜ε；
Wherein, Wm×nSYNAs a muscle synergy matrix, HnSYN×tA muscle time sequence activation coefficient matrix; nSYN is the number of muscle synergies; ε is a first set threshold.
Preferably, in the method, the symmetry indicator is obtained by:
the collection efficiency and quality are greatly improved; based on the measured back multichannel electromyographic signals, comprehensive quantitative evaluation is carried out by analyzing the cooperation condition of each group of muscles, the weight and symmetry of electromyographic activities of the left side and the right side of each horizontal direction of the spine are evaluated, and whether the possibility of scoliosis exists is evaluated. Compared with the traditional medical image evaluation method, the method has the advantages that the detection speed is increased, and the cost is reduced; compared with visual evaluation, the reliability and the precision of detection are improved; compared with the method for evaluating only the absolute myoelectric value related parameters, the method not only improves the reliability of evaluation, but also can accurately position the area with abnormal myoelectric activity.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a schematic diagram of the back arrangement of the method of the present invention when applying the surface electromyographic membrane electrode to detect scoliosis;
FIG. 2 is a schematic diagram of a structure of a single-chip surface electromyography thin film electrode according to the present invention;
FIG. 3 is a diagram of SI according to the present inventionlThe values plot a schematic of the distribution along the vertical direction;
in the figure: 1 is a surface electromyography electrode; 2 is a signal bus; 3 is spinal midline; 4 is the back of the detected person; 5 is a signal main interface; 1-1 is an elastic substrate; 1-2 are electric signal monitoring points; 1-3 are conductive paths; 1-4 are signal interfaces.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In one embodiment, a scoliosis assessment method using surface electromyography thin film electrodes is used to quantitatively assess the presence or absence of scoliosis risk. The method includes the steps that electromyographic signals are collected on the basis of a surface electromyographic thin film electrode array, a muscle cooperation matrix is further obtained, symmetry indexes of electromyographic activities on the left side and the right side of a spine in each horizontal direction are calculated through the muscle cooperation matrix, and whether the possibility of scoliosis exists or not is further evaluated. Compared with the traditional medical image evaluation method, the method has the advantages that the detection speed is increased and the cost is reduced; compared with visual evaluation, the reliability and the precision of detection are improved; compared with the method for evaluating only the absolute myoelectric value related parameters, the method not only improves the reliability of evaluation, but also can accurately position the area with abnormal myoelectric activity.
In the method, the raw data collected based on the surface electromyography thin film electrode array comprises the following steps:
s100, uniformly distributing surface electromyography thin film electrode arrays on two sides of a spinal midline of a tested person;
s200, recording the electromyographic signals while the tested person completes the maximum amplitude dynamic continuous forward bending and backward stretching actions of the spine on the sagittal plane of the human body, and obtaining the original data.
Specifically, a set of surface electromyography thin film electrode array is prepared, as shown in fig. 1, wherein the array comprises 8 surface electromyography thin film electrodes 1, and the single surface electromyography thin film electrode 1 is composed of an elastic substrate 1-1, electric signal monitoring points 1-2, conductive paths 1-3 and signal interfaces 1-4, as shown in fig. 2. Each thin film electromyographic electrode is connected to a signal main interface 5 through a signal bus 2, 8 thin film electromyographic electrodes are uniformly distributed on the back 4 of a detected person by taking a spinal central line 3 as a center, and an electrode array with 16 rows and 8 columns sharing 128 channels is formed. The specific preparation method of the membrane electromyography electrode is not limited. When the surface electromyography thin-film electrode array is prepared, the number of the surface electromyography thin-film electrodes is determined according to needs, and then 128 channels of electrodes are evenly distributed. The film can be conveniently adhered to the back of a tested person, and is not easy to fall off from the body of the tested person due to the soft adhesion of the film. Optionally, the monolithic surface electromyography membrane electrode is reusable.
The whole set of membrane electrodes are arranged on the back of a person to be tested according to the figure 1, and then a signal main interface 5 is connected to an electromyographic signal acquisition system and further transmitted to a computer through a data transmission line. The method can be further improved, and data transmission is carried out in a wireless mode.
The testee completes the maximum amplitude dynamic continuous forward bending and backward stretching actions of the spine on the sagittal plane of the human body within a certain time at a constant speed according to the voice command, and simultaneously starts the electromyographic signal acquisition system to synchronously record the electromyographic signal so as to complete data acquisition. The certain time can be set, for example, 3 seconds, 5 seconds, 10 seconds, and the like.
Next, obtaining a muscle synergy matrix, comprising the steps of:
s101, forming a matrix A by the collected myoelectric data after all channels are preprocessed under t-column time framem×tM is the number of all myoelectric channels;
s201, a non-negative matrix factorization algorithm pair Am×tFactorizing to obtain Wm×nSYN，HnSYN×tSo that | | | A-Wm×nSYNHnSYN×t||＜ε；
Wherein, Wm×nSYNAs a muscle synergy matrix, HnSYN×tA muscle time sequence activation coefficient matrix; nSYN is the number of muscle synergies;
ε is a first set threshold.
In the step S101, the preprocessing is to preprocess the electromyographic signals, and includes extracting 20 to 300Hz band-pass waves and removing 50Hz power frequency notch in the original signals by using a filter, and extracting and removing the electrocardiographic signals by using a principal component factor analysis method, thereby reducing interference signals such as motion noise and power frequency.
In step S201, muscle synergy refers to the property that each muscle in the muscle control layer is stored and activated in the form of a certain weight constant value.
All channels under the acquired t-column time frameElectromyographic data after channel preprocessing form a matrix Am×tEach row of the matrix A is data of a single myoelectric channel in a time frame of t columns, and the number of rows is equal to the number of myoelectric channels. To Am×tAnd (4) carrying out non-negative matrix decomposition, and obtaining two non-negative matrixes W and H through decomposition, wherein the column number and the H row number of W are the values of nSYN in the current iteration. The number of rows m of the non-negative matrix W is 128 in the present embodiment. The number of columns in H is t. Non-negative matrix factorization is an iterative process that terminates when the following conditions are met:
||A-Wm×nSYNHnSYN×t||＜ε
ε is the first set threshold, typically set at 5%. The value range of epsilon is 5-10%.
The invention provides a specific non-negative matrix factorization method, as shown in the figure:
s103, forming a matrix A by the collected myoelectric data after all the channels are preprocessed under the t-column time framem×tM is the number of all myoelectric channels; setting the initial value of nSYN as 1;
s203, based on non-negative matrix factorization algorithm, pair Am×tFactorizing to obtain Wm×nSYN，HnSYN×tComputing a reconstruction matrix
Figure BDA0003492524270000071
S303, calculating the reconstruction goodness of fit (VAF) according to the following formula:
Figure BDA0003492524270000072
s403, if VAF < second set threshold, increasing nSYN by 1, and returning to S203.
Reconstructed muscle point signal matrix when terminating iteration
Figure BDA0003492524270000073
The degree of variation of the data relative to the original matrix A is smaller than a first set threshold value, or the reconstructed muscle point signal matrix
Figure BDA0003492524270000074
The goodness of fit of the data of (2) to the original matrix A is greater than or equal to a second set threshold value.
When the iteration is stopped, the current nSYN is the muscle cooperation number of the movement electromyogram data, and the W at the moment is used as a muscle cooperation matrix and is used for calculating the symmetry indexes of the electromyogram activities on the left side and the right side of the spinal column in each horizontal direction.
Preferably, in the method, the symmetry indicator is obtained by:
s102, for each column in the muscle cooperation matrix, arranging elements in the column into a matrix W at the row and column positions of the back according to the myoelectricity arrayi；
S202, according to the matrix WiThe sum of the weight elements on the left and right sides centered on the spinal midline at each spinal level, l, is calculated and recorded as Wisumleft，lAnd Wisumright，l；
S302, calculating the symmetry index SIlAnd according to SIlThe values plot a profile along the vertical direction:
if Wisumright，l＞Wisumleft，lThen, then
Figure BDA0003492524270000081
If Wisumright，l＜Wisumleft，lThen, then
Figure BDA0003492524270000082
If Wisumright，l＝Wisumleft，lIf equal to 0, then SIl＝0。
Specifically, the muscle cooperation matrix W obtained above is a matrix of m rows and n syn columns, that is, each column in the W matrix is 1 muscle cooperation group, where each element in each column is a muscle activity weight corresponding to the corresponding myoelectric channel in the muscle cooperation group, and a value is [0, 1]. Arranging each row of elements into a matrix W at the row and column positions of the back according to the myoelectricity arrayiFor the current embodiment, WiA matrix of 16 rows and 8 columns.
As can be seen from the formulas (1) to (3), the symmetry index SIlHas a value range of [ -1, 1 [)]。SIlA value closer to 0 indicates better left-right symmetry of the myoelectricity. Through experiments, the patient SI of the scoliosis can be knownlFall within the value range of [0.2, 1 ]]And [ -0.2, -1 [)]In the interval, it is considered that there is an obvious asymmetry of left and right myoelectric activities. Therefore [ -0.2, 0.2] can be added]Is selected as an empirical balance area.
Thus, for the method, one preferred scoliosis assessment modality is: sequentially judging the symmetry indexes SI corresponding to each group of muscles in cooperationlWhether it is within the equilibrium region; if there are more than N1 consecutive symmetry indexes SIlIf the detected person does not belong to the balance area, judging that the detected person has the possibility of suffering from scoliosis; n1 is the set value.
In the present embodiment, N1 is set to 3, i.e., there are continuous more than 3 SIlThe point belongs to an unbalanced area, and the detected person is judged to have the possibility of suffering from scoliosis. According to the calculated SIlThe values plot a profile in the vertical direction, as shown in FIG. 3, the circles represent SIlThe dotted line is a fitted back spine curve diagram, so that whether the spine is laterally curved can be more intuitively seen.
Furthermore, in order to avoid that the surface electromyography electrodes are pulled to fall off, the arrangement consumes long time and the fitting degree of the measurement points cannot be guaranteed, the flexible stretchable surface electromyography thin film electrodes are preferably adopted in the invention to improve the skin attachment of the electrodes, so that the acquisition efficiency and the acquisition quality are improved. The present invention is not limited to the material or production technique of the surface electromyogram membrane electrode.
To sum up, the above embodiment applies a set of surface electromyography thin film electrodes to perform scoliosis assessment, the arrangement preparation is simple, the collected original data is preprocessed and the muscle cooperation matrix is extracted by recording large-area back muscle electrical information of the tested person when the spine continuously moves forwards and backwards in the sagittal plane of the human body, the weight and symmetry indexes of the left and right electromyography activities of each horizontal spine are calculated, the area with abnormal electromyography activities is located, and whether the tested person has the risk of scoliosis is judged. Compared with the traditional medical image evaluation method, the method has the advantages that the detection speed is increased and the cost is reduced; compared with visual evaluation, the reliability and the precision of detection are improved; compared with the method for evaluating only the absolute myoelectric value related parameters, the method not only improves the reliability of evaluation, but also can accurately position the area with abnormal myoelectric activity.
From the above description of the embodiments, it is clear to those skilled in the art that the signal acquisition, data processing and calculation involved in the method of the present invention can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memories, dedicated components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, software program implementation is a more preferred implementation for more of the present disclosure.
Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments and application fields, and the above-described embodiments are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.

Claims (8)

1. A scoliosis assessment method applying surface electromyographic membrane electrodes is characterized in that electromyographic signals are collected on the basis of a surface electromyographic membrane electrode array, a muscle cooperation matrix is further obtained, symmetry indexes of electromyographic activities on the left side and the right side of a spine in each horizontal direction are calculated through the muscle cooperation matrix, and whether scoliosis is possible is further assessed.
2. The method according to claim 1, wherein the raw data collected based on the surface electromyography thin film electrode array comprises the steps of:
s100, uniformly distributing surface electromyography thin film electrode arrays on two sides of a spinal midline of a tested person;
s200, recording electromyographic signals while completing the maximum amplitude dynamic continuous forward bending and backward stretching actions of the spine of the tested person on the sagittal plane of the human body, and obtaining original data.
3. The method of claim 1, wherein the muscle synergy matrix is obtained by:
s101, forming a matrix A by the collected myoelectric data after all channels are preprocessed under t-column time framem×tM is the number of all myoelectric channels;
s201, a non-negative matrix factorization algorithm pair Am×tFactorizing to obtain Wm×nSYN，HnSYN×tSo that | | | A-Wm× nSYNHnSYN×t||＜ε；
Wherein, Wm×nSYNAs a muscle synergy matrix, HnSYN×tA muscle time sequence activation coefficient matrix; nSYN is the number of muscle synergies;
ε is a first set threshold.
4. The method of claim 1, wherein the symmetry indicator is obtained by:
s102, for each column in the muscle cooperation matrix, arranging elements in the column into a matrix W at the row and column positions of the back according to the myoelectricity arrayi；
S202, according to the matrix WiThe sum of the weight elements on the left and right sides centered on the spinal midline at each spinal level, l, is calculated and recorded as Wisumleft，lAnd Wisumright，l；
S302, calculating the symmetry index SIlAnd according to SIlThe values plot a profile along the vertical direction:
if Wisumright，l＞Wisumleft，lThen, then
Figure FDA0003492524260000021
If Wisumright，l＜Wisumleft，lThen, then
Figure FDA0003492524260000022
If Wisumright，l＝Wisumleft，lIf equal to 0, then SIl＝0。
5. The method of claim 1, wherein the evaluating is accomplished by:
sequentially judging the symmetry indexes SI corresponding to each group of muscles in cooperationlWhether it is within the equilibrium region;
if there are more than N1 consecutive symmetry indexes SIlIf the detected person does not belong to the balance area, judging that the detected person has the possibility of suffering from scoliosis;
n1 is the set value.
6. The method of claim 5, wherein the equilibrium region is [ -0.2, 0.2 ].
7. The method according to claim 1, characterised in that the surface electromyographic membrane electrodes array is made up of 8 surface electromyographic membrane electrodes.
8. The method according to claim 1, characterized in that the surface electromyographic membrane electrode has a flexible stretchability.
CN202210104098.XA 2022-01-27 2022-01-27 Scoliosis assessment method applying surface electromyography thin-film electrode Pending CN114569112A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116269242A (en) * 2023-05-17 2023-06-23 广州培生智能科技有限公司 Old person health status real-time monitoring system based on internet
WO2024001281A1 (en) * 2022-06-30 2024-01-04 中国科学院深圳先进技术研究院 Wearable spine health monitoring device and system based on multi-channel myoelectricity
CN117752324A (en) * 2023-12-05 2024-03-26 上海脊合医疗科技有限公司 Scoliosis detection method and system based on muscle current signals

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024001281A1 (en) * 2022-06-30 2024-01-04 中国科学院深圳先进技术研究院 Wearable spine health monitoring device and system based on multi-channel myoelectricity
CN116269242A (en) * 2023-05-17 2023-06-23 广州培生智能科技有限公司 Old person health status real-time monitoring system based on internet
CN116269242B (en) * 2023-05-17 2023-07-18 广州培生智能科技有限公司 Old person health status real-time monitoring system based on internet
CN117752324A (en) * 2023-12-05 2024-03-26 上海脊合医疗科技有限公司 Scoliosis detection method and system based on muscle current signals
CN117752324B (en) * 2023-12-05 2024-06-11 上海脊合医疗科技有限公司 Scoliosis detection method and system based on muscle current signals

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