WO2018214103A1 - 慢性腰痛患者肌肉活动状态判断方法及系统 - Google Patents

慢性腰痛患者肌肉活动状态判断方法及系统 Download PDF

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WO2018214103A1
WO2018214103A1 PCT/CN2017/085953 CN2017085953W WO2018214103A1 WO 2018214103 A1 WO2018214103 A1 WO 2018214103A1 CN 2017085953 W CN2017085953 W CN 2017085953W WO 2018214103 A1 WO2018214103 A1 WO 2018214103A1
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muscle
signal
module
back pain
feature extraction
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French (fr)
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杜文静
李慧慧
王磊
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]

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  • the invention relates to a method and a system for judging the state of muscle activity of a patient with chronic low back pain.
  • Low back pain refers to a group of pain syndromes in which the back, lumbosacral, buttocks pain and discomfort are the main symptoms.
  • Low back pain is not a disease, nor a pathological diagnosis, but a clinical syndrome. According to the duration of low back pain, it is divided into: acute low back pain, subacute low back pain and chronic low back pain (CLBP).
  • Chronic low back pain means that the pain lasts longer than 3 months.
  • chronic low back pain is a common disease second only to upper respiratory tract infection, and its lifetime prevalence can be as high as 60%-90%. The age of prone to occur is between 35-55 years old, including chronic non-specific low back pain. 85%-90% of patients with clinical low back pain.
  • the susceptible population mainly includes car drivers, ambulance workers, surgeons, nurses, heavy manual workers and professional athletes. 40% of patients deliberately reduce the amount of activity, 20% of people are significantly restricted in their daily lives, and 5% of patients have severely restricted activities in daily life.
  • Chronic low back pain is also a major cause of disability in Western countries. According to reports, the prevalence rate in the United States is 10.2%, the prevalence rate in Australia is 10%, and the prevalence in Europe is 5.9%-23%.
  • the number of patients with low back pain in China has reached more than 200 million, and has been on the rise for many years. Chronic low back pain has become an important cause of dysfunction, disability, increased social and economic burdens and affecting the quality of people's daily lives. There are many factors that cause low back pain, including personal, professional, and psychological factors.
  • the stability of the lumbar spine is mainly determined by the lumbar stability muscle.
  • the lumbar stability muscle is a muscle group distributed in the lumbar vertebrae and the entire trunk to maintain lumbar stability and achieve lumbar protection.
  • the lumbar spine and the external oblique muscle play a major stabilizing effect. Therefore, timely understanding of the functional status of the lumbar stabilized muscles can not only effectively assist patients with low back pain in the advancement of lumbar disc herniation. Prevention, and can help the doctor to assess the patient's rehabilitation by stabilizing the muscle activity.
  • the invention provides a method for judging muscle activity state of a patient with chronic low back pain, the method comprising the steps of: a. collecting a multi-cleft muscle EMG signal of the tester; b. pre-processing the collected EMG signal; c. The processed EMG signal is subjected to wavelet packet decomposition, and the low-frequency signal is extracted by using wavelet packet for signal reconstruction; d. Feature extraction is performed on the decomposed and reconstructed EMG signal; and e. EMG signal after feature extraction Perform muscle activity status judgment.
  • the step a specifically includes: attaching the disposable electrode sheet to the lumbar vertebra of the tester, and the tester transmits the analog signal generated by the muscle to the MP150 receiving device through the BIOPAC transmitting module during the bending of the trunk, and the receiving device performs Analog-to-digital conversion converts muscle analog signals into one-dimensional random voltage signals.
  • the pre-processing includes: band pass filtering and power frequency denoising.
  • the step d specifically includes: extracting the root mean square value of the resolved and reconstructed myoelectric signal and the median frequency of the frequency domain.
  • the muscle activity states include: increased muscle strength, muscle fatigue, decreased muscle strength, and muscle recovery.
  • the invention provides a muscle activity state judging system for a patient with chronic low back pain, the system comprising the system comprising an acquisition module, a preprocessing module, a decomposition reconstruction module, a feature extraction module and a state determination module, wherein: the acquisition module is used for collecting and testing The multi-cleft muscle EMG signal; the pre-processing module is configured to pre-process the acquired EMG signal; the Decomposition and Reconstruction Module is configured to perform wavelet packet decomposition on the pre-processed EMG signal, and extract low The frequency band signal uses a wavelet packet for signal reconstruction; The module is used for feature extraction of the decomposed and reconstructed myoelectric signals; the state judging module is configured to perform muscle activity state determination according to the electromyographic signals extracted by the feature.
  • the collecting module is specifically used for: attaching the disposable electrode sheet to the lumbar vertebra of the tester, and the tester transmits the analog signal generated by the muscle to the MP150 receiving device through the BIOPAC transmitting module during the bending of the trunk, the receiving device Perform analog-to-digital conversion to convert the muscle analog signal into a one-dimensional random voltage signal.
  • the pre-processing includes: band pass filtering and power frequency denoising.
  • the feature extraction module is specifically configured to: extract a root mean square value of the resolved and reconstructed myoelectric signal and a median frequency of the frequency domain.
  • the muscle activity states include: increased muscle strength, muscle fatigue, decreased muscle strength, and muscle recovery.
  • the invention collects and maintains the lumbar spine stability multi-fissure muscle muscle electromyogram signal in a non-invasive and objective manner, and extracts time domain parameters and frequency domain parameters representing muscle characteristics through wavelet packet decomposition and low-band signal reconstruction theory.
  • the frequency parameter changes with time, so as to judge the activity state of the multi-cleft muscle, the present invention has the following advantages:
  • the current activity state of the muscle is evaluated by the transformation characteristics of the time domain parameter and the frequency domain parameter.
  • FIG. 1 is a flow chart of a method for determining a muscle activity state of a patient with chronic low back pain according to the present invention
  • FIG. 2 is a schematic diagram showing the energy distribution of each frequency band of the myoelectric signal according to the embodiment of the present invention, wherein FIG. 2(a) is a schematic diagram showing the energy percentage of the myoelectric signal of the left multi-fissure muscle, and FIG. 2(b) is a right multi-cleft muscle myoelectricity. Schematic diagram of signal energy percentage;
  • FIG. 3 is a schematic diagram of a wavelet packet EMG signal decomposition and reconstruction tree structure according to an embodiment of the present invention, wherein FIG. 3(a) is a schematic diagram of an original signal wavelet packet decomposition tree structure, and FIG. 3(b) is a reconstruction signal tree type. Schematic;
  • FIG. 4 is a schematic diagram showing the distribution of time domain and frequency domain parameters of patients with chronic low back pain and healthy persons according to the embodiment of the present invention
  • FIG. 5 is a schematic diagram of a criterion for determining a muscle activity state according to an embodiment of the present invention.
  • Fig. 6 is a hardware architecture diagram of a muscle activity state judging system for a patient with chronic low back pain according to the present invention.
  • FIG. 1 there is shown a flow chart of a preferred embodiment of a method for determining the state of muscle activity of a patient with chronic low back pain according to the present invention.
  • step S1 the tester is subjected to myoelectric signal acquisition.
  • the tester is subjected to myoelectric signal acquisition. in particular:
  • the tester maintains a multi-fissure muscle signal while maintaining lumbar stability.
  • the disposable electrode sheet was attached to the lumbar vertebrae of the tester, and the tester maintained the stability of the lumbar spine, and the surface of the multifidus muscle which was wiped by 75% alcohol was adhered along the direction of the muscle fibers; the tester launched the BIOPAC during the bending of the trunk.
  • the module transmits the analog signal generated by the muscle to the MP150 receiving device, and the receiving device performs analog-to-digital conversion to convert the muscle analog signal into a one-dimensional random voltage signal.
  • step S2 the acquired myoelectric signal is preprocessed.
  • the acquired myoelectric signal is preprocessed. in particular:
  • Preprocessing of the acquired EMG signals mainly includes band pass filtering and power frequency denoising.
  • the 35-500 Hz band pass filter is used to perform band pass filtering on the collected EMG signals, and the power frequency denoising is performed on the 50 Hz power frequency interference.
  • the effective frequency band of the myoelectric signal is between 10 and 500 Hz, the power frequency interference of 50 Hz generated by the voltage of 220 V in China has a great influence on the myoelectric signal, and the power frequency signal needs to be filtered out.
  • the main frequency band of the ECG signal is 0.25-35Hz, which will have some interference to the electrical signal generated by the muscle movement. For this reason, the collected EMG signal is subjected to bandpass filtering of 35-500Hz. .
  • step S3 the pre-processed myoelectric signal is decomposed and reconstructed.
  • the pre-processed myoelectric signal is decomposed and reconstructed.
  • the pre-processed EMG signal is subjected to wavelet packet decomposition, and the low-band signal is extracted by wavelet packet for signal reconstruction.
  • the myoelectric signal has a limited high frequency band of 500 Hz and a sampling rate of 1000 Hz. According to the Nyquist theorem, the myoelectric signal can be decomposed by the wavelet packet and obtain 16 bands, as shown in Fig. 2(a) and Fig. 2(b). Shown; where the lowest frequency range is 0-31.25 Hz and the highest frequency range is 468.75-500 Hz.
  • the original signal wavelet packet decomposition tree structure is shown in Figure 3(a). Since more than 80% of the energy of the EMG signal is mainly concentrated in the low frequency band of the 1-8 band, the signal of the low frequency band can exhibit most of the characteristics of the EMG signal, and the signal extracted from the low frequency band is reconstructed by using a wavelet packet to reconstruct the signal.
  • the tree structure is shown in Fig. 3(b), and the reconstructed signal is normalized by the maximum normalization method.
  • step S4 feature extraction is performed on the resolved and reconstructed myoelectric signals.
  • feature extraction is performed on the resolved and reconstructed myoelectric signals.
  • the root-mean square (RMS) of the above-mentioned decomposed and reconstructed myoelectric signal and the median frequency (MF) of the frequency domain are extracted.
  • the rms value of the tester's myoelectric signal is obtained by Matlab software.
  • the amplitude of the myoelectric signal with time can reflect the change characteristics of the muscle during the time period. Please refer to Figure 4.
  • step S5 the muscle activity state is judged according to the myoelectric signal after the feature extraction.
  • the muscle activity states include: increased muscle strength, muscle fatigue, decreased muscle strength, and muscle recovery. Specific steps are as follows:
  • the rms value of the EMG signal in the time domain index increases with the increase of muscle strength and fatigue.
  • the median frequency of the frequency domain index increases with the increase of muscle strength and decreases with the increase of fatigue.
  • the increase in muscle strength refers to a state in which the muscle is in a state in which the strength of the muscle is continuously increased so that the root mean square value of the muscle is increased, and the muscle is not yet fatigued, so that the median frequency is also increased;
  • Muscle fatigue refers to the state in which the muscle is in a state in which the rms value of the muscle discharge increases and the muscle load gradually increases, resulting in a decrease in the median frequency;
  • the square root value is reduced, and the muscle load is increased to cause the muscle to be in a state where the median frequency is decreased; the muscle recovery is: when the muscle discharge is continuously reduced, the root mean square value is decreased, the muscle strength is increased, and the muscle is increased.
  • a decrease in load causes the median frequency to increase, causing the muscles to gradually recover. Therefore, it is possible to determine which active state the muscle is in according to the change of the rms value of the tester's time domain index and the median frequency of the frequency domain index, as shown in FIG.
  • FIG. 6 there is shown a hardware architecture diagram of the muscle activity state judging system 10 of the chronic low back pain patient of the present invention.
  • the system includes: an acquisition module 101, a pre-processing module 102, an decomposition reconstruction module 103, a feature extraction module 104, and a state determination module 105.
  • the acquisition module 101 is configured to perform electromyographic signal acquisition on a tester. in particular:
  • the acquisition module 101 collects the multi-cleft muscle muscle signal while the tester maintains the lumbar spine stable.
  • the acquisition module 101 mainly includes signal transmission and reception.
  • the disposable electrode sheet was attached to the lumbar vertebrae of the tester, and the tester maintained the stability of the lumbar spine, and the surface of the multifidus muscle which was wiped by 75% alcohol was adhered along the direction of the muscle fibers; the tester launched the BIOPAC during the bending of the trunk.
  • the module transmits the analog signal generated by the muscle to the MP150 receiving device, and the receiving device performs analog-to-digital conversion to convert the muscle analog signal into a one-dimensional random voltage signal.
  • the pre-processing module 102 is configured to pre-process the acquired myoelectric signals. in particular:
  • the preprocessing module 102 preprocesses the acquired myoelectric signals mainly including band pass filtering and power frequency denoising.
  • the 35-500 Hz band pass filter is used to perform band pass filtering on the collected EMG signals, and the power frequency denoising is performed on the 50 Hz power frequency interference.
  • the effective frequency band of the myoelectric signal is between 10 and 500 Hz, the power frequency interference of 50 Hz generated by the voltage of 220 V in China has a great influence on the myoelectric signal, and the power frequency signal needs to be filtered out.
  • the main frequency band of the ECG signal is 0.25-35Hz, which will have some interference to the electrical signal generated by the muscle movement. For this reason, the collected EMG signal is subjected to bandpass filtering of 35-500Hz. .
  • the decomposition reconstruction module 103 is configured to decompose and reconstruct the pre-processed myoelectric signal. in particular:
  • the decomposition reconstruction module 103 performs wavelet packet decomposition on the pre-processed myoelectric signal, and extracts a low-band signal using a wavelet packet for signal reconstruction.
  • the myoelectric signal has a limited high frequency band of 500 Hz and a sampling rate of 1000 Hz. According to the Nyquist theorem, the myoelectric signal can be decomposed by the wavelet packet and obtain 16 bands, as shown in Fig. 2(a) and Fig. 2(b). Shown; where the lowest frequency range is 0-31.25 Hz and the highest frequency range is 468.75-500 Hz.
  • the original signal wavelet packet decomposition tree structure is shown in Figure 3(a). Since more than 80% of the energy of the EMG signal is mainly concentrated in the low frequency band of the 1-8 band, the signal of the low frequency band can exhibit most of the characteristics of the EMG signal, and the signal extracted from the low frequency band is reconstructed by using a wavelet packet to reconstruct the signal.
  • the tree structure is shown in Fig. 3(b), and the reconstructed signal is normalized by the maximum normalization method.
  • the feature extraction module 104 is configured to perform feature extraction on the decomposed and reconstructed myoelectric signals. in particular:
  • the feature extraction module 104 extracts a root-mean square (RMS) of the resolved and reconstructed myoelectric signal and a median frequency (MF) of the frequency domain.
  • RMS root-mean square
  • MF median frequency
  • the state judging module 105 is configured to perform muscle activity state determination according to the electromyogram signal after the feature extraction.
  • the muscle activity states include: increased muscle strength, muscle fatigue, decreased muscle strength, and muscle recovery. details as follows:
  • the rms value of the EMG signal in the time domain index increases with the increase of muscle strength and fatigue.
  • the median frequency of the frequency domain index increases with the increase of muscle strength and decreases with the increase of fatigue.
  • the increase in muscle strength refers to a state in which the muscle is in a state in which the strength of the muscle is continuously increased so that the root mean square value of the muscle is increased, and the muscle is not yet fatigued, so that the median frequency is also increased;
  • Muscle fatigue refers to the state in which the muscle is in a state in which the rms value of the muscle discharge increases and the muscle load gradually increases, resulting in a decrease in the median frequency;
  • the square root value is reduced, and the muscle load is increased to cause the muscle to be in a state where the median frequency is decreased; the muscle recovery is: when the muscle discharge is continuously reduced, the root mean square value is decreased, the muscle strength is increased, and the muscle is increased.
  • a decrease in load causes the median frequency to increase, causing the muscles to gradually recover. Therefore, it is possible to determine which active state the muscle is in according to the change of the rms value of the tester's time domain index and the median frequency of the frequency domain index, as shown in FIG.
  • the invention adopts an objective, scientific, simple and rapid manner to perform functional judgment on the lumbar stabilizing muscle of patients with chronic low back pain, and adopts wavelet packet decomposition and reconstruction by collecting lumbar multi-cleft muscle electromyographic signals for maintaining lumbar stability.
  • the algorithm system device can judge the functional activity state of the stable muscle of chronic low back pain, and can not only guide the doctor to have a certain understanding of the muscle function of the patients with chronic low back pain, but also help the doctor to make a more correct pathological diagnosis and select appropriate, Effective treatment to help patients achieve early recovery.

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Abstract

一种慢性腰痛患者肌肉活动状态判断方法及判断系统（10），其中，判断方法包括如下步骤：采集测试者的多裂肌肌电信号（S1）；对采集的肌电信号进行预处理（S2）；对预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构（S3）；对分解和重构后的肌电信号进行特征提取（S4）；及根据特征提取后的肌电信号进行肌肉活动状态判断（S5）。慢性腰痛患者肌肉活动状态判断方法及判断系统（10）操作简单、无创采集，诊断时间快，病人易接受。

Description

慢性腰痛患者肌肉活动状态判断方法及系统 技术领域
本发明涉及一种慢性腰痛患者肌肉活动状态判断方法及系统。
背景技术
腰痛，是指一组以下背、腰骶、臀部疼痛和不适为主要症状的疼痛综合症。腰痛不是一种疾病，也不是一种病理诊断，而是一种临床综合症。根据腰痛的持续时间分为：急性腰痛、亚急性腰痛和慢性腰痛(chronic low back pain，CLBP)。慢性腰痛是指疼痛持续时间超过3个月。根据流行病学调查研究表明，慢性腰痛是仅次于上呼吸道感染的常见疾病，其终身流行率可高达60％-90％，易发人群年龄介于35-55岁，其中慢性非特异性腰痛占临床腰痛就诊患者的85％-90％。易患人群主要包括，汽车司机、伏安工作人群、手术医生、护士、重体力劳动者和职业运动员等。患者中40％的人有意减少活动量，20％的人日常生活明显受限，5％的患者日常生活活动严重受限。慢性腰痛也是西方国家人群致残的一个主要原因。据报道，美国的患病率为10.2％，澳大利亚的患病率为10％，欧洲患病率为5.9％-23％。而我国腰痛患者认识已经超过2亿，且多年以来一直呈上升趋势。慢性腰痛已经成为引起功能障碍，致残务工，增加社会经济负担和影响人们日常生活质量的重要原因。导致腰痛的因素很多，包括个人、职业、心里等因素。越来越多的基础和临床医学发现，虽然慢性腰痛的治病因素很多，病理机制也很复杂，但是各种原因的导致都与慢性腰痛腰椎稳定性有一定的关系。而影响腰椎稳定性主要由腰椎稳定肌决定。腰椎稳定肌是分布于腰椎局部和躯干整体部位的具有维持腰椎稳定性和实现腰椎保护的肌群，其中腰椎多裂肌和腹外斜肌起主要的稳定作用。因此，及时的了解腰椎稳定肌肉的功能状态，不仅能有效的帮助腰痛患者进行腰椎间盘突出症的预 防，而且能帮助医生通过稳定肌的活动状态对患者的康复效果进行评估。
国内外研究学者最初采用针电极插入肌肉检测肌电图，其干扰小，定位性好，易识别，但是由于它是一种有创的检测方法，患者不易接受，使得该应用受到一定限制。经过长期的探究，人们发现表面肌电信号同样可以采集到肌肉的运动信息，由于表面肌电信号是一种无创的检测方法，是通过肌肉表面粘贴的电极片记录下来神经肌肉活动时发放的生物电信号，操作简单，病人易接受，因此表面肌电信号在临床医学、生物医学工程界得到了广泛的应用。与针电极检测相比，表面肌电信号的应用还处于发展阶段。它与神经肌肉的基础研究紧密相关，神经肌肉的生理学研究为肌电信号的应用提供了坚实的基础，而表面肌电信号的检测为神经肌肉的研究提供了较好的检测方法。
慢性腰痛患者，肌肉萎缩、收缩力量降低、耐力下降等均可以引起腰部脊柱稳定性下降，造成椎间小关节活动紊乱，骨关节炎，周围韧带以及椎间盘损伤从而引起腰痛。以上机制的设计的重要概念，即腰椎稳定性，而肌肉起维持腰椎稳定性的80％以上的作用。因此腰部肌肉功能评价无论是对与慢性腰痛患者的诊断，还是疗效评价都具体有十分重要的应用价值。
慢性腰痛患者大部分存在腰部稳定功能丧失或出现障碍等现象，主要原因是腰肌稳定破坏，引起躯干活动过程中腰椎小关节紊乱和骨关节炎、局部肌肉痉挛、周围韧带以及椎间盘损伤。而维持腰椎稳定性的稳定肌起主导作用，稳定肌的功能决定着腰椎稳定性的高低。在正常的生理条件下，腰椎稳定肌往往在各种外力干扰条件下维持腰椎稳定性和保护腰椎生理功能。肌肉水平下降被认为是造成慢性腰痛的初始原因。虽然目前，有很多方式比如X光线，CT或者MRI等对患者的腰椎进行诊断，但是患者的腰部肌肉功能如何却很难评估出。早期临床和康复医学对腰部肌肉的功能评价，一般采用等长、等张和等速耐力测试，这些使用通常要求患者腰背部进行相应的运动负荷病以最大持续时间或活动次数来评价。虽然这些方法有时可以对患者的肌肉功能进行评价，但是受患者主观努力程度和不适耐受性的影响，使得结果存在很大的不确定性和 主观性，而缺乏详细客观的评估肌肉的系统装置，很多时候为了了解腰部肌肉的真实功能状态会让患者进行最大自主收缩运动，这样很可能会引起患者腰部的二次伤害。
发明内容
有鉴于此，有必要提供一种慢性腰痛患者肌肉活动状态判断方法及系统。
本发明提供一种慢性腰痛患者肌肉活动状态判断方法，该方法包括如下步骤：a.采集测试者的多裂肌肌电信号；b.对采集的肌电信号进行预处理；c.对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构；d.对分解和重构后的肌电信号进行特征提取；及e.根据特征提取后的肌电信号进行肌肉活动状态判断。
其中，所述的步骤a具体包括：将一次性电极片粘贴于测试者腰椎，测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
所述的预处理包括：带通滤波和工频去噪。
所述的步骤d具体包括：提取上述分解和重构后的肌电信号的均方根值和频域的中位频率。
所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。
本发明提供一种慢性腰痛患者肌肉活动状态判断系统，该系统包括该系统包括采集模块、预处理模块、分解重构模块、特征提取模块、状态判断模块，其中：所述采集模块用于采集测试者的多裂肌肌电信号；所述预处理模块用于对采集的肌电信号进行预处理；所述分解重构模块用于对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构；所述特征提 取模块用于对分解和重构后的肌电信号进行特征提取；所述状态判断模块用于根据特征提取后的肌电信号进行肌肉活动状态判断。
其中，所述的采集模块具体用于：将一次性电极片粘贴于测试者腰椎，测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
所述的预处理包括：带通滤波和工频去噪。
所述的特征提取模块具体用于：提取上述分解和重构后的肌电信号的均方根值和频域的中位频率。
所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。
本发明通过无创式、客观的方式采集维持腰椎稳定性多裂肌肌肉肌电信号，并通过小波包分解和低频段信号重构理论提取出表现肌肉特性的时域参数和频域参数，得到时频参数随时间变化特性，以此对多裂肌肌肉的活动状态进行判断，本发明具有以下几个优点：
1、解决慢性腰痛患者时域、频域参数随时间变化的特性。
2、通过时域参数和频域参数的变换特性评估肌肉当前活动状态。
3、通过客观、简便的方式实现对肌肉活动状态的评估。
附图说明
图1为本发明慢性腰痛患者肌肉活动状态判断方法的流程图；
图2为本发明实施例肌电信号各频段能量分布图示意图，其中，图2(a)为左侧多裂肌肌电信号能量百分比示意图，图2(b)为右侧多裂肌肌电信号能量百分比示意图；
图3为本发明实施例小波包肌电信号分解和重构树型结构示意图，其中，图3(a)为原始信号小波包分解树型结构示意图，图3(b)为重构信号树型结构示意图；
图4为本发明实施例慢性腰痛患者与健康者时域和频域参数随时间的分布示意图；
图5为本发明实施例肌肉活动状态判断标准示意图。
图6为本发明慢性腰痛患者肌肉活动状态判断系统的硬件架构图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
下面结合附图及具体实施例对本发明作进一步详细的说明。
参阅图1所示，是本发明慢性腰痛患者肌肉活动状态判断方法较佳实施例的作业流程图。
步骤S1，对测试者进行肌电信号采集。具体而言：
测试者维持腰椎稳定的情况下，采集其多裂肌肌肉信号。
进一步地，主要包括信号发射和接收。将一次性电极片粘贴于测试者腰椎，在测试者维持腰椎稳定性，经75％酒精擦拭过的多裂肌表面，沿肌肉纤维方向粘贴；测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
步骤S2，对采集的肌电信号进行预处理。具体而言：
对采集的肌电信号进行预处理主要包括带通滤波和工频去噪。本实施例采用35-500Hz的带通滤波器对上述采集的肌电信号进行带通滤波，并对50Hz的工频干扰进行工频去噪。
进一步地，由于肌电信号的有效频段为10-500Hz之间，我国电压220v所产生工频干扰50Hz对肌电信号有较大的影响，为此需滤掉工频信号。由于人体上半身受心脏跳动的影响，心电信号主要频段为0.25-35Hz对肌肉运动产生的电信号会有一定的干扰，为此对采集到的肌电信号进行了35-500Hz的带通滤波处理。
步骤S3，对预处理后的肌电信号进行分解和重构。具体而言：
对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构。
进一步地，肌电信号有限高频带500Hz，采样率1000Hz，根据奈奎斯特定理，肌电信号可以进行小波包4层分解，获得16频段，如图2(a)、图2(b)所示；其中最低频带范围0-31.25Hz，最高频段范围468.75-500Hz。
原始信号小波包分解树型结构如图3(a)所示。由于肌电信号能量80％以上主要集中在第1-8频带低频段，因此，低频段的信号可以表现出肌电信号的大部分特性，提取低频段信号采用小波包进行重构，重构信号树型结构如图3(b)所示，重构后的信号采用最大值归一化法进行标准化处理。
步骤S4，对分解和重构后的肌电信号进行特征提取。具体而言：
本实施例提取上述分解和重构后的肌电信号的均方根值(root-mean square，RMS)和频域的中位频率(median frequency，MF)。测试者肌电信号的均方根值通过Matlab软件获取，肌电信号随着时间的幅值变化情况可以体现该时间段内肌肉的变化特性，请参考图4。
步骤S5，根据特征提取后的肌电信号进行肌肉活动状态判断。所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。具体步骤如下：
通过比较慢性腰痛患者和健康者的肌电参数，根据测试者肌电信号的均方根值和中位频率变化情况，判断测试者肌肉处于哪种活动状态。时域指标肌电信号均方根值会随着肌肉力量和疲劳程度的增加而增加，频域指标中位频率会随着肌力的增加而增加，随着疲劳程度的增加而降低。
在本实施例中，所述的肌力增加是指：当肌肉的力量不断增加使得肌电均方根值增加，肌肉还未产生疲劳使得中位频率也增加时肌肉所处的状态；所述的肌肉疲劳是指：当肌肉放电量均方根值增加，肌肉负荷逐渐增大导致中位频率减小时，肌肉所处的状态；所述的肌力下降是指：当肌肉的力量降低使得均方根值减小，肌肉负荷增强导致中位频率减小时，肌肉所处的状态；所述的肌肉恢复是指：当肌肉的放电量不断减少导致均方根值减小，肌肉力量增加，肌肉负荷降低导致中位频率增加，使得肌肉逐渐达到恢复状态。因此，可以根据测试者的时域指标肌电均方根值和频域指标中位频率的变换来判断肌肉所处于哪种活动状态，如图5所示。
通过对慢性腰痛患者和健康者的特征参数比较，发现慢性腰痛患者的肌肉活动状态随时间的变换情况，见下表所示。
多裂肌肌肉状态评估
Figure PCTCN2017085953-appb-000001
Figure PCTCN2017085953-appb-000002
比较慢性腰痛患者多裂肌肌肉特性发现：在完成躯干弯曲过程中，左侧多裂肌出现肌力增加(8次)、肌肉疲劳(9次)、肌力下降(6次)、肌肉恢复(8次)；右侧多裂肌出现肌力增加(5次)、肌肉疲劳(13次)、肌力下降(2次)、肌肉恢复(11次)；完成相同躯干弯曲动作，健康者左侧多裂肌出现肌力增加(5次)、肌肉疲劳(5次)、肌力下降(6次)、肌肉恢复(1次)；右侧多裂肌出现肌力增加(5次)、肌肉疲劳(4次)、肌力下降(5次)、肌肉恢复(3次)。无论肌肉处于哪个阶段，慢性腰痛患者均比健康者的肌肉活动状态变换频繁，说明慢性腰痛患者在躯干弯曲活动中为了维持腰椎的稳定性需要不断的激活肌肉以此产生足够的力量来维持腰椎运动功能。
参阅图6所示，是本发明慢性腰痛患者肌肉活动状态判断系统10的硬件架构图。该系统包括：采集模块101、预处理模块102、分解重构模块103、特征提取模块104、状态判断模块105。
所述采集模块101用于对测试者进行肌电信号采集。具体而言：
所述采集模块101在测试者维持腰椎稳定的情况下，采集其多裂肌肌肉信号。
进一步地，所述采集模块101主要包括信号发射和接收。将一次性电极片粘贴于测试者腰椎，在测试者维持腰椎稳定性，经75％酒精擦拭过的多裂肌表面，沿肌肉纤维方向粘贴；测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
所述预处理模块102用于对采集的肌电信号进行预处理。具体而言：
所述预处理模块102对采集的肌电信号进行预处理主要包括带通滤波和工频去噪。本实施例采用35-500Hz的带通滤波器对上述采集的肌电信号进行带通滤波，并对50Hz的工频干扰进行工频去噪。
进一步地，由于肌电信号的有效频段为10-500Hz之间，我国电压220v所产生工频干扰50Hz对肌电信号有较大的影响，为此需滤掉工频信号。由于人体上半身受心脏跳动的影响，心电信号主要频段为0.25-35Hz对肌肉运动产生的电信号会有一定的干扰，为此对采集到的肌电信号进行了35-500Hz的带通滤波处理。
所述分解重构模块103用于对预处理后的肌电信号进行分解和重构。具体而言：
所述分解重构模块103对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构。
进一步地，肌电信号有限高频带500Hz，采样率1000Hz，根据奈奎斯特定理，肌电信号可以进行小波包4层分解，获得16频段，如图2(a)、图2(b)所示；其中最低频带范围0-31.25Hz，最高频段范围468.75-500Hz。
原始信号小波包分解树型结构如图3(a)所示。由于肌电信号能量80％以上主要集中在第1-8频带低频段，因此，低频段的信号可以表现出肌电信号的大部分特性，提取低频段信号采用小波包进行重构，重构信号树型结构如图3(b)所示，重构后的信号采用最大值归一化法进行标准化处理。
所述特征提取模块104用于对分解和重构后的肌电信号进行特征提取。具体而言：
本实施例中，所述特征提取模块104提取上述分解和重构后的肌电信号的均方根值(root-mean square，RMS)和频域的中位频率(median frequency，MF)。测试者肌电信号的均方根值通过Matlab软件获取，肌电信号随着时间 的幅值变化情况可以体现该时间段内肌肉的变化特性，请参考图4。
所述状态判断模块105用于根据特征提取后的肌电信号进行肌肉活动状态判断。所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。具体如下：
通过比较慢性腰痛患者和健康者的肌电参数，根据测试者肌电信号的均方根值和中位频率变化情况，判断测试者肌肉处于哪种活动状态。时域指标肌电信号均方根值会随着肌肉力量和疲劳程度的增加而增加，频域指标中位频率会随着肌力的增加而增加，随着疲劳程度的增加而降低。
在本实施例中，所述的肌力增加是指：当肌肉的力量不断增加使得肌电均方根值增加，肌肉还未产生疲劳使得中位频率也增加时肌肉所处的状态；所述的肌肉疲劳是指：当肌肉放电量均方根值增加，肌肉负荷逐渐增大导致中位频率减小时，肌肉所处的状态；所述的肌力下降是指：当肌肉的力量降低使得均方根值减小，肌肉负荷增强导致中位频率减小时，肌肉所处的状态；所述的肌肉恢复是指：当肌肉的放电量不断减少导致均方根值减小，肌肉力量增加，肌肉负荷降低导致中位频率增加，使得肌肉逐渐达到恢复状态。因此，可以根据测试者的时域指标肌电均方根值和频域指标中位频率的变换来判断肌肉所处于哪种活动状态，如图5所示。
通过对慢性腰痛患者和健康者的特征参数比较，发现慢性腰痛患者的肌肉活动状态随时间的变换情况，见下表所示。
多裂肌肌肉状态评估
Figure PCTCN2017085953-appb-000003
Figure PCTCN2017085953-appb-000004
比较慢性腰痛患者多裂肌肌肉特性发现：在完成躯干弯曲过程中，左侧多裂肌出现肌力增加(8次)、肌肉疲劳(9次)、肌力下降(6次)、肌肉恢复(8次)；右侧多裂肌出现肌力增加(5次)、肌肉疲劳(13次)、肌力下降(2次)、肌肉恢复(11次)；完成相同躯干弯曲动作，健康者左侧多裂肌出现肌力增加(5次)、肌肉疲劳(5次)、肌力下降(6次)、肌肉恢复(1次)；右侧多裂肌出现肌力增加(5次)、肌肉疲劳(4次)、肌力下降(5次)、肌肉恢复(3次)。无论肌肉处于哪个阶段，慢性腰痛患者均比健康者的肌肉活动状态变换频繁，说明慢性腰痛患者在躯干弯曲活动中为了维持腰椎的稳定性需要不断的激活肌肉以此产生足够的力量来维持腰椎运动功能。
本发明采用客观的、科学的、简易的、快速的方式，对慢性腰痛患者的腰部稳定肌进行功能判断，通过采集维持腰椎稳定性的腰部多裂肌肌电信号，采用小波包分解和重构算法系统装置，对慢性腰痛患的稳定肌进行功能活动状态判断，不仅可以指导医生对慢性腰痛患者的肌肉功能有一定的了解，而且还可以帮助医生做出更加正确的病理诊断，选择出合适、有效的治疗方式，帮助患者早日实现康复。
虽然本发明参照当前的较佳实施方式进行了描述，但本领域的技术人员应能理解，上述较佳实施方式仅用来说明本发明，并非用来限定本发明的保护范围，任何在本发明的精神和原则范围之内，所做的任何修饰、等效替换、改进等，均应包含在本发明的权利保护范围之内。

Claims (10)

  1. 一种慢性腰痛患者肌肉活动状态判断方法，其特征在于，该方法包括如下步骤：
    a.采集测试者的多裂肌肌电信号；
    b.对采集的肌电信号进行预处理；
    c.对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构；
    d.对分解和重构后的肌电信号进行特征提取；及
    e.根据特征提取后的肌电信号进行肌肉活动状态判断。
  2. 如权利要求1所述的方法，其特征在于，所述的步骤a具体包括：
    将一次性电极片粘贴于测试者腰椎，测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
  3. 如权利要求2所述的方法，其特征在于，所述的预处理包括：带通滤波和工频去噪。
  4. 如权利要求3所述的方法，其特征在于，所述的步骤d具体包括：
    提取上述分解和重构后的肌电信号的均方根值和频域的中位频率。
  5. 如权利要求4所述的方法，其特征在于，所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。
  6. 一种慢性腰痛患者肌肉活动状态判断系统，其特征在于，该系统包括采集模块、预处理模块、分解重构模块、特征提取模块、状态判断模块，其中：
    所述采集模块用于采集测试者的多裂肌肌电信号；
    所述预处理模块用于对采集的肌电信号进行预处理；
    所述分解重构模块用于对上述预处理后的肌电信号进行小波包分解，提取低频段信号采用小波包进行信号重构；
    所述特征提取模块用于对分解和重构后的肌电信号进行特征提取；
    所述状态判断模块用于根据特征提取后的肌电信号进行肌肉活动状态判断。
  7. 如权利要求6所述的系统，其特征在于，所述的采集模块具体用于：
    将一次性电极片粘贴于测试者腰椎，测试者在进行躯干弯曲的过程中，通过BIOPAC发射模块将肌肉产生的模拟信号传输给MP150接收装置，接收装置进行模数转换，将肌肉模拟信号转换为一维随机电压信号。
  8. 如权利要求7所述的系统，其特征在于，所述的预处理包括：带通滤波和工频去噪。
  9. 如权利要求8所述的系统，其特征在于，所述的特征提取模块具体用于：
    提取上述分解和重构后的肌电信号的均方根值和频域的中位频率。
  10. 如权利要求9所述的系统，其特征在于，所述的肌肉活动状态包括：肌力增加、肌肉疲劳、肌力下降、肌肉恢复。
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