WO2022160334A1 - 一种推拿疗效评估方法、装置、系统、以及存储介质 - Google Patents

一种推拿疗效评估方法、装置、系统、以及存储介质 Download PDF

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WO2022160334A1
WO2022160334A1 PCT/CN2021/074649 CN2021074649W WO2022160334A1 WO 2022160334 A1 WO2022160334 A1 WO 2022160334A1 CN 2021074649 W CN2021074649 W CN 2021074649W WO 2022160334 A1 WO2022160334 A1 WO 2022160334A1
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massage
therapeutic effect
artifact
pdi
emg
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French (fr)
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李慧慧
王磊
王博
谯小豪
颜延
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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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms

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  • the invention relates to the technical field of data statistical analysis, in particular to a method, device, system and storage medium for evaluating the therapeutic effect of massage.
  • Drug therapy such as opioid analgesics used for chronic musculoskeletal pain (CMP) has great side effects, and it is easy to damage the kidneys, induce gastric ulcers, leukopenia, and liver damage. Long-term use of analgesics will cause drug resistance. Some are even addicted to sexual problems. According to the US Centers for Disease Control and Prevention, between 1999 and 2017, a total of 218,000 people in the United States died of opioid overdose, the main drugs including OxyContin and fentanyl. Non-drug therapy is low-cost and safe, and it is an alternative therapy for chronic pain. Acupunture), massage therapy (Massage therapy, MT).
  • MT massage therapy
  • Non-drug therapies such as acupuncture and massage therapy have shown positive effects in many conditions, such as antenatal depression, premature infants, term infants, autism, skin diseases, pain syndromes (including arthritis, fibromyalgia, hypertension) , autoimmune diseases (including asthma, multiple sclerosis), human immune diseases (such as AIDS, breast cancer), and senile diseases such as Parkinson's and Alzheimer's disease.
  • the evaluation criteria for the efficacy of acupuncture and massage therapy in CMP were clinical scale (self-reported pain) and response rate.
  • Current tuina studies include comparisons of tuina therapy with standard treatment control groups, such as different forms of Swiss tuina versus Thai tuina, different structures of tuina (shaking and stretching), relaxation tuina (soothing), and tuina and exercise Therapy comparison.
  • standard treatment control groups such as different forms of Swiss tuina versus Thai tuina, different structures of tuina (shaking and stretching), relaxation tuina (soothing), and tuina and exercise Therapy comparison.
  • Many current studies are based on self-reported scales, and the reliability cannot be guaranteed.
  • An ideal nonpharmacological research protocol should include psychological, physical, physiological, and biochemical measurements and note the effects of multiple parameters.
  • the present invention provides a method, device, system and storage medium for evaluating the therapeutic effect of massage.
  • the present invention provides a method for evaluating the therapeutic effect of massage, the method comprising the following steps:
  • S1 Collect the EEG signals of the patient before and after massage
  • the step S2 includes:
  • MEMD Multivariate empirical mode decomposition
  • CCA canonical correlation analysis
  • Wavelet-independent component analysis method was used to remove eye movement artifacts in EEG signals.
  • the removing the EMG artifact in the EEG signal comprises:
  • the first step is to use the joint analysis method of the multivariate empirical mode decomposition method and the canonical correlation analysis method to remove the artifacts with low CCA correlation;
  • the artifact is removed for the second time by the multivariate empirical mode decomposition method.
  • the removal of EMG artifacts in the EEG signal includes the following steps:
  • Identify the EMG artifact component If the EMG artifact component is identified, set the EMG artifact component to 0, and then perform the inverse calculation of the multivariate empirical mode decomposition. If the EMG artifact component is not identified, directly Perform the inverse calculation of the multi-experience mode decomposition, and obtain the EEG signal after performing the multi-experience mode decomposition method and the canonical correlation analysis method;
  • the multivariate empirical mode decomposition calculation is performed again to obtain the EEG signal after removing the EMG artifact.
  • the removing eye movement artifacts in the EEG signal includes the following steps:
  • a bandpass filtering operation is performed for the EEG signal after removing the EMG artifacts
  • the eye movement artifact component is identified. If the eye movement artifact component is identified, the eye movement artifact peak detection and peak correction are performed, and then the inverse transformation of the independent component analysis is performed. If the eye movement artifact component is not identified, then The inverse transform of the independent component analysis is directly performed to obtain the EEG signal after removing the artifact.
  • the calculation formula of the PDI characteristic value is as follows:
  • X t [x(t),x(t+L),...,x(t+(m-1)L)] T (1);
  • x and y represent the m-dimensional EEG signal time series of two adjacent channels before or after massage, and the two time series x and y are mapped to the vectors X t and Y t , L represents the time interval in the permutation entropy, m represents the embedding dimension in the permutation entropy, and t represents the time;
  • ⁇ i represents the symbol vector
  • n represents the number of occurrences of the time series x and y mapped to the specified sequence ⁇ i
  • N represents the total number of sampling points of the time series x and y
  • the probability p x, y ( ⁇ i ) represents the proportion of the number of the same permutations in the total number of permutations when the time series x and y calculate the permutation entropy
  • the symbol vector ⁇ i [ ⁇ 1 , ⁇ 2 ,..., ⁇ m ]
  • ⁇ represents the time delay
  • the high value of parameter ⁇ represents the super-Gaussian distribution
  • the low value represents the sub-Gaussian distribution
  • PDI(X, Y) represents the PDI eigenvalue between the vectors X t and Y t .
  • the step S4 includes:
  • the PDI eigenvalues of different channels before and after tuina were divided into two categories, and then the one-way ANOVA was used to analyze the PDI eigenvalues of the two types of PDI eigenvalues to obtain the significant difference of the PDI eigenvalues of the quantitative index of massage efficacy;
  • the index is judged to be sensitive to the efficacy of massage.
  • the present invention provides an apparatus comprising a memory and a processor coupled to the memory, wherein:
  • the memory stores program instructions for implementing the above-mentioned method for evaluating the therapeutic effect of massage
  • the processor is configured to execute the program instructions stored in the memory to control the execution of the method for evaluating the therapeutic effect of massage.
  • the present invention provides a massage curative effect evaluation system, comprising:
  • the signal acquisition module is used to collect the EEG signals of patients before and after massage;
  • the artifact removal module is used to remove the artifacts from the collected EEG signals
  • a feature extraction module for extracting a permutation disorder index (PDI) feature value from the artifact-removed EEG signal
  • the analysis module is used to perform a significant analysis of the therapeutic effect of massage according to the extracted PDI characteristic values.
  • the present invention provides a storage medium storing program instructions executable by a processor, where the program instructions are used to execute the above method for evaluating the therapeutic effect of massage.
  • the embodiment of the present invention provides a method, device, system and storage medium for evaluating the therapeutic effect of massage.
  • the collected EEG signals are removed from the artifact.
  • the PDI feature value was extracted from the EEG signal after the artifact, and according to the extracted PDI feature value, a significant analysis of the therapeutic effect of massage was performed to evaluate the therapeutic effect of the patient's massage.
  • the embodiment of the present invention has the beneficial effects of improving the accuracy and reliability of the evaluation result of massage curative effect.
  • Fig. 1 is the flow chart of the massage curative effect evaluation method provided in the embodiment of the present invention 1;
  • Fig. 2 (a) is the flow chart of removing the EMG artifact in the EEG signal in the embodiment 1 of the present invention
  • Figure 2(b) is a flowchart of removing eye movement artifacts in the EEG signal in Embodiment 1 of the present invention
  • Figure 3 (a) is a schematic diagram of the significant difference in the PDI characteristics of the delta rhythm of the EEG signal of the patient before and after massage provided in Example 1 of the present invention
  • Figure 3 (b) is a schematic diagram of the significant difference in the PDI characteristics of the ⁇ rhythm of the EEG signal of the patient before and after massage provided in Example 1 of the present invention
  • Figure 3 (c) is a schematic diagram of the significant difference in the PDI characteristics of the alpha rhythm of the EEG signal of the patient before and after massage according to Embodiment 1 of the present invention
  • Figure 3 (d) is a schematic diagram of the significant difference in the PDI characteristics of the ⁇ rhythm of the EEG signal of the patient before and after massage provided in Example 1 of the present invention
  • Embodiment 4 is a schematic structural diagram of an apparatus provided in Embodiment 2 of the present invention.
  • FIG. 5 is a schematic structural diagram of a massage curative effect evaluation system provided in Embodiment 3 of the present invention.
  • FIG. 6 is a schematic structural diagram of a storage medium provided in Embodiment 4 of the present invention.
  • FIG. 1 is a flowchart of the method for evaluating the therapeutic effect of massage provided in Embodiment 1 of the present invention.
  • the execution order of the steps in the flowchart shown in FIG. 1 can be changed, and some steps can be omitted.
  • Step S1 Collect the EEG signals (Electroencephalograph, EEG) of the patient before and after massage.
  • the patient may be a patient with chronic musculoskeletal pain (CMP) disease or other diseases
  • the EEG signals include, but are not limited to, Motion Imagery (MI) signals and Actual motion signal.
  • the imaginary motion signal includes a left-leaning imaginary motion signal (MI with left bending) and a right-leaning imaginary motion signal (MI with right bending)
  • the actual motion signal includes a left-leaning actual motion signal (Real left bending) and a right-leaning actual motion signal Signal (Real right bending).
  • the EEG signal is collected by a wearable device (such as an EEG cap).
  • EMG surface electromyography
  • FMRI functional magnetic resonance imaging
  • NIS near infrared spectroscopy
  • SMG Ultrasound dynamic analysis
  • the main feature of CMP disease is muscle and soft tissue damage. X-ray, CT, MRI and other imaging techniques cannot clearly show the muscle and soft tissue of CMP pain. EMG and SMG can provide dynamic and convenient muscle function assessment, therefore, wearable physiological parameter monitoring method is more suitable for the characteristics of CMP pain. Wearable physiological parameter monitoring does not irradiate the patient, and can dynamically measure the patient's physiological state for a long time and low load.
  • Step S2 Artifact removal is performed on the collected EEG signals.
  • the step S2 includes: first performing a mean value removal operation on the collected EEG signals, and then using a Multivariate Empirical Mode Decomposition (MEMD) method and a Canonical Correlation Analysis (CCA) method to remove the mean value.
  • MEMD Multivariate Empirical Mode Decomposition
  • CCA Canonical Correlation Analysis
  • EEG Electromyography
  • EEG Electromyography
  • EOG Eye movement artifact
  • the removing the EMG artifacts in the EEG signal includes: the first step, adopting a combined analysis method of the multivariate empirical pattern decomposition method and the canonical correlation analysis method to remove the artifacts with low CCA correlation; the second step, The artifact is removed for the second time by the multivariate empirical mode decomposition method.
  • the removal of EMG artifacts in the EEG signal includes the following steps:
  • IMFs Intrinsic Mode Functions
  • CCA Canonical Correlation Analysis
  • Identify the EMG artifact component If the EMG artifact component is identified, set the EMG artifact component to 0, and then perform the inverse calculation of the multivariate empirical mode decomposition. If the EMG artifact component is not identified, directly Perform the inverse calculation of the multivariate empirical mode decomposition, and obtain the EEG signal after performing the multivariate empirical mode decomposition method and the canonical correlation analysis method (MEMD-CCA);
  • the removal of eye movement artifacts in the EEG signal includes the following steps:
  • the eye movement artifact component is identified. If the eye movement artifact component is identified, the eye movement artifact peak detection and peak correction are performed, and then the inverse transformation (Inverse ICA) of the independent component analysis is performed. If no eye movement artifact is identified If the trace component is detected, the inverse transformation of the independent component analysis is directly performed to obtain the EEG signal after removing the artifact.
  • ICA inverse transformation
  • Step S3 extracting a PDI (Permutation Disalignment Index, permutation disalignment index) eigenvalue from the EEG signal after removing the artifacts.
  • the permutation disorder index (PDI) is a new measure of the coupling strength between time series based on permutation entropy (PE), and the coupling strength between different cortical regions can be calculated by using the PDI eigenvalues, not only a single cortical region activities, which can be used to dynamically evaluate the therapeutic effect of massage.
  • PDI eigenvalues can also be used as indirect EEG measures of brain connectivity in patients with Alzheimer's disease and mild cognitive impairment.
  • the calculation formula of the PDI characteristic value is as follows:
  • ⁇ i represents the symbol vector
  • n represents the number of occurrences of the time series x and y mapped to the specified sequence ⁇ i
  • the probability p x,y ( ⁇ i ) represents the proportion of the number of the same permutations in the total number of permutations when calculating the permutation entropy of the time series x and y.
  • the high value of parameter ⁇ represents the super-Gaussian distribution
  • the low value represents the sub-Gaussian distribution.
  • the sensitivity of the arrangement entropy to the super-Gaussian distribution and the sub-Gaussian distribution can be adjusted by the parameter ⁇ ;
  • PDI(X, Y) represents the PDI eigenvalues between vectors X t and Y t (representing the misalignment index between time series x and y), the higher the coupling strength of different cortical regions, the lower the PDI eigenvalues.
  • Step S4 according to the extracted PDI characteristic value, carry out a significant analysis of the therapeutic effect of massage.
  • the step S4 includes: dividing the PDI eigenvalues of different channels before and after the massage into two categories (one category before and after the massage), and then using one-way analysis of variance (one-way analysis) of variance, one-way ANOVA) method, analyze the two types of PDI eigenvalues to obtain the significant difference of the PDI eigenvalues of the quantitative index of massage efficacy and other massage efficacy results.
  • the one-way ANOVA method is a data statistical analysis method, such as the method provided by SPSS statistical analysis software.
  • the quantitative indicators that need to be analyzed include, but are not limited to, different populations, different channels, and PDI characteristic values before and after massage
  • the results of massage efficacy obtained by analysis include, but are not limited to, mean, variance, and 95% confidence interval , correlation coefficients, and significant differences.
  • step S4 if there is a significant difference between a quantitative index of therapeutic effect of massage before and after massage, and there is a correlation with the score of the clinical pain scale, it is determined that the index is sensitive to the therapeutic effect of massage and belongs to the preferred index. For example, if the eigenvalue of the index PDI decreases after massage, it may reflect that the connectivity of different channels of the patient's brain waves is enhanced, which means that the effect of massage is better. However, it should be added that, due to the differences in the scores of different groups of people on the clinical pain scale, judging the therapeutic effect of massage cannot simply be based on the decrease or increase of the score, but the correlation between the quantitative index and the clinical pain scale score should be used to determine the index. Whether there is sensitivity to the therapeutic effect of massage, as long as the sensitivity is sufficient, it can be used to judge the therapeutic effect of massage.
  • the PDI characteristics of the four rhythms (delta rhythm, theta rhythm, alpha rhythm, and beta rhythm) of the patient's EEG signals before and after massage provided in Example 1 of the present invention are respectively Schematic diagram of the significant difference of , in which the abscissa represents the test channel (such as AF3_F7, F7_F3, ..., F8_AF4, etc.), the ordinate represents the PDI characteristic value of the two adjacent channels, and each rhythm includes the patient's EEG signals before and after massage Significant differences in left-leaning imaginary movement, right-leaning imagined movement, left-leaning actual movement, and right-leaning actual movement. In the present invention, when p value ⁇ 0.05 is a significant difference, * in Figure 3(a)- Figure 3(d) represents a significant difference.
  • FIG. 4 is a schematic structural diagram of an apparatus according to Embodiment 2 of the present invention.
  • the device 2 may include, but is not limited to, a memory 21, and a processor 22 coupled to the memory 21, and the memory 21 and the processor 22 may be communicatively connected to each other through a system bus.
  • FIG. 4 only shows the device 2 with components 21 and 22, but it should be understood that the embodiment 2 does not show all the components of the device 2, and the device 2 has more or more alternative implementations. fewer components.
  • the device 2 may be a computing device such as a rack server, a blade server, a tower server, or a cabinet server, and the device 2 may be an independent server or a server cluster composed of multiple servers.
  • the memory 21 stores program instructions for implementing the above-mentioned method for evaluating the therapeutic effect of massage.
  • the memory 21 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (eg, SD or DX memory, etc.), random access memory (RAM), static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disk, Optical Disk, etc.
  • the memory 21 may be an internal storage unit of the device 2 , such as a hard disk or a memory of the device 2 .
  • the memory 21 may also be an external storage device of the device 2, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure) Digital, SD) card, flash card (Flash Card), etc.
  • the memory 21 may also include both the internal storage unit of the apparatus 2 and its external storage device.
  • the memory 21 is generally used to store the operating system, various application software, and system codes installed in the device 2 .
  • the memory 21 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 22 is configured to execute program instructions stored in the memory 21 to control the execution of the method for evaluating the therapeutic effect of massage.
  • the processor 22 may also be referred to as a CPU (Central Processing Unit, central processing unit).
  • the processor 22 may be an integrated circuit chip with signal processing capability.
  • Processor 22 may also be a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components .
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the processor 22 is generally used to control the overall operation of the device 2 , such as performing control and processing related to data interaction or communication with the device 2 . In this embodiment 2, the processor 22 is configured to run the program code or process data or the like stored in the memory 21 .
  • the device 2 further includes a network interface (such as a wireless network interface or a wired network interface), which is usually used to establish a communication connection between the device 2 and other electronic devices (such as a mobile phone).
  • a network interface such as a wireless network interface or a wired network interface
  • the network interface is used to connect the device 2 with an external data platform through a network, and establish a data transmission channel and a communication connection between the device 2 and the external data platform.
  • the network can be an intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, 5G Wireless or wired network such as network, Bluetooth (Bluetooth), Wi-Fi, etc.
  • FIG. 5 is a schematic structural diagram of a massage therapeutic effect evaluation system provided in Embodiment 3 of the present invention.
  • the massage therapeutic effect evaluation system 3 can be divided into one or more program modules, the one or more program modules are stored in the memory 21, and are processed by one or more processors ( Executed as processor 22) to accomplish the present invention.
  • the massage therapeutic effect evaluation system 3 can be divided into a signal acquisition module 31 , an artifact removal module 32 , a feature extraction module 33 , and an analysis module 34 .
  • the program module referred to in the present invention refers to a series of computer program instruction segments capable of accomplishing specific functions, and is more suitable for describing the execution process of the massage therapeutic effect evaluation system 3 in the device 2 than a program. The function of each program module 31-34 will be described in detail below.
  • the signal acquisition module 31 is used to collect the EEG signals of the patient before and after massage; the artifact removal module 32 is used to remove artifacts from the collected EEG signals; the feature extraction module 33 is used to remove the artifacts from the brain.
  • the PDI characteristic value is extracted from the electroencephalographic signal of the device; the analysis module 34 is used to perform a significant analysis of the therapeutic effect of massage according to the extracted PDI characteristic value.
  • FIG. 6 is a schematic structural diagram of a storage medium according to Embodiment 4 of the present invention.
  • the storage medium 4 stores a program file 41 capable of implementing all the above-mentioned methods, wherein the program file 41 may be stored in the above-mentioned storage medium 4 in the form of a software product, and includes several instructions to make A computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) executes all or part of the steps of the methods of the various embodiments of the present invention.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes , or terminal devices such as computers, servers, mobile phones, and tablets.
  • a method, device, system and storage medium for evaluating the therapeutic effect of massage according to the embodiments of the present invention, by collecting the EEG signals of a patient before and after massage, the collected EEG signals are artifact-removed, and the EEG signals after the artifact removal are removed from the EEG signals.
  • the PDI feature values were extracted, and according to the extracted PDI feature values, a significant analysis of the therapeutic effect of massage was performed to evaluate the therapeutic effect of massage.
  • the embodiment of the present invention has at least the following beneficial effects: the accuracy and reliability of the evaluation result of massage curative effect are improved.

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Abstract

一种推拿疗效评估方法，包括以下步骤：采集患者推拿前后的脑电信号（S1）；对采集的脑电信号进行伪迹去除（S2）；从去除伪迹后的脑电信号中提取排列失调指数(PDI)特征值（S3）；根据提取的PDI特征值，进行推拿疗效显著性分析（S4）。另外，还提供一种推拿疗效评估的装置、系统、以及存储介质。可以提高推拿疗效评估结果的准确性和可靠性。

Description

一种推拿疗效评估方法、装置、系统、以及存储介质 技术领域
本发明涉及数据统计分析技术领域，特别涉及一种推拿疗效评估方法、装置、系统以及存储介质。
背景技术
用于慢性骨骼肌疼痛(Chronic muscular pain，CMP)的药物疗法(例如阿片类止痛药物)副作用极大，容易损害肾、诱发胃溃疡、白细胞减少、肝损害，长期使用镇痛药物会出现药物耐受性问题有些甚至容易上瘾。根据美国疾控预防中心的统计，1999年至2017年之间，全美国一共有21.8万人因为过量服用阿片类药物而致死，其中主要的药物包括奥施康定和芬太尼。非药物疗法成本低、安全，是慢性疼痛的替代性疗法，例如2019年发表在国际著名期刊《英国运动医学杂志》(British Journal of Sports Medicine)系统综述的运动疗法(Exercise therapy)、针灸疗法(Acupunture)、推拿疗法(Massage therapy，MT)。针灸、推拿疗法等非药物疗法已经在很多病症中显示积极作用，比如产前抑郁，早产儿，足月婴儿，孤独症，皮肤病，疼痛综合征(包括关节炎、纤维肌痛、高血压)，自身免疫性疾病(包括哮喘、多发性硬化症)，人体免疫性疾病(例如艾滋病、乳腺癌)，以及帕金森和老年痴呆症等老年性疾病。
针灸和推拿疗法在CMP疗效评估的评价标准为临床量表(自我报告疼痛)和有效率。目前的推拿研究包括推拿疗法与标准治疗控制组的对比，例如不同形式的瑞士推拿和泰式推拿对比，不同结构的推拿(摇动和拉伸)、放松式推拿(安抚法)、以及推拿和运动疗法对比。但是，目前很多研究基于自述性的量表，可靠性不能保证。理想的非药物疗法研究方案应包括心理的、物理的、生理的、生物化学的测量并注明多参数的效果。
发明内容
鉴于此，本发明提供了一种推拿疗效评估方法、装置、系统以及存储介质。
第一方面，本发明提供了一种推拿疗效评估方法，所述方法包括以下步骤：
S1：采集患者推拿前后的脑电信号；
S2：对采集的脑电信号进行伪迹去除；
S3：从去除伪迹后的脑电信号中提取排列失调指数(PDI)特征值；及
S4：根据提取的PDI特征值，进行推拿疗效显著性分析。
优选地，所述步骤S2包括：
对采集的脑电信号执行去除均值操作；
采用多元经验模式分解(MEMD)方法和典型相关分析(CCA)方法，去除脑电信号中的肌电伪迹；
采用小波-独立分量分析方法，去除脑电信号中的眼动伪迹。
优选地，所述去除脑电信号中的肌电伪迹包括：
第一步，采用多元经验模式分解方法和典型相关分析方法的联合分析方式，去除CCA相关性低的伪迹；
第二步，经过多元经验模式分解方法第二次去除伪迹。
优选地，所述去除脑电信号中的肌电伪迹包括如下步骤：
当接收到被肌电伪迹污染的脑电信号时，执行多元经验模式分解计算；
调用本征模式函数，然后执行典型相关分析计算；
进行肌电伪迹分量的识别，如果识别到肌电伪迹分量，则设置肌电伪迹分量为0，然后执行多元经验模式分解的逆计算，如果没有识别到肌电伪迹分量，则直接执行多元经验模式分解的逆计算，得到执行多元经验模式分解方法和典型相关分析方法后的脑电信号；
针对执行多元经验模式分解方法和典型相关分析方法后的实际运动脑电信号，再次执行多元经验模式分解计算，得到去除肌电伪迹后的脑电信号。
优选地，所述去除脑电信号中的眼动伪迹包括如下步骤：
针对去除肌电伪迹后的脑电信号，执行带通滤波操作；
继续执行小波-独立分量分析操作；
进行眼动伪迹分量的识别，如果识别到眼动伪迹分量，则进行眼动伪迹峰值检测和峰值矫正，然后执行独立分量分析的逆变换，如果没有识别到眼动伪迹分量，则直接执行独立分量分析的逆变换，得到去除伪迹后的脑电信号。
优选地，所述PDI特征值的计算公式如下：
X t＝[x(t),x(t+L),...,x(t+(m-1)L)] T    (1)；
Y t＝[y(t),y(t+L),...,y(t+(m-1)L)] T    (2)；
其中，在计算公式(1)和(2)中，x和y代表推拿前或推拿后两个相邻通道的m维脑电信号时间序列，两个时间序列x和y映射到矢量X t和Y t，L代表排列熵中的时间间隔，m代表排列熵中的嵌入维度，t代表时间；
p X,Y(π i)＝n(π i)/(N-(m-1)L)    (3)；
Figure PCTCN2021074649-appb-000001
其中，在计算公式(3)中，π i代表符号矢量，n代表时间序列x和y映射到指定序列π i的出现个数，N代表时间序列x和y的采样点总数，概率p x,y(π i)代表时间序列x和y计算排列熵时出现相同排列的个数在总排列个数中所占的比例，符号矢量π i＝[τ 1,τ 2,...,τ m]，τ代表时间延迟；
在计算公式(4)中，参数α高值代表超高斯分布，低值代表亚高斯分布，PDI(X,Y)代表矢量X t和Y t之间的PDI特征值。
优选地，所述步骤S4包括：
将推拿前和推拿后不同通道的PDI特征值分成两类，然后采用单因素方差分析方法，分析该两类PDI特征值得到推拿疗效定量指标的PDI特征值的显著性差异；
如果某个推拿疗效定量指标在推拿前和推拿后有显著性差异，并且与临床 疼痛量表评分有相关性，则判定该指标对推拿疗效具有敏感性。
第二方面，本发明提供了一种装置，所述装置包括存储器、与所述存储器耦接的处理器，其中：
所述存储器存储有用于实现上述推拿疗效评估方法的程序指令；
所述处理器用于执行所述存储器存储的所述程序指令以控制所述推拿疗效评估方法的执行。
第三方面，本发明提供了一种推拿疗效评估系统，包括：
信号采集模块，用于采集患者推拿前后的脑电信号；
伪迹去除模块，用于对采集的脑电信号进行伪迹去除；
特征提取模块，用于从去除伪迹后的脑电信号中提取排列失调指数(PDI)特征值；及
分析模块，用于根据提取的PDI特征值，进行推拿疗效显著性分析。
第四方面，本发明提供了一种存储介质，存储有处理器可运行的程序指令，所述程序指令用于执行上述推拿疗效评估方法。
相较于现有技术，本发明实施例提供的一种推拿疗效评估方法、装置、系统及存储介质，通过采集患者推拿前后的脑电信号，对采集的脑电信号进行伪迹去除，从去除伪迹后的脑电信号中提取PDI特征值，根据提取的PDI特征值，进行推拿疗效显著性分析，评估患者推拿疗效。与现有技术相比，本发明实施例产生的有益效果在于：提升了推拿疗效评估结果的准确性和可靠性。
附图说明
图1为本发明实施例1提供的推拿疗效评估方法的流程图；
图2(a)为本发明实施例1中去除脑电信号中的肌电伪迹流程图；
图2(b)为本发明实施例1中去除脑电信号中的眼动伪迹流程图；
图3(a)为本发明实施例1提供的患者推拿前后脑电信号的δ节律的PDI特征的显著性差异示意图；
图3(b)为本发明实施例1提供的患者推拿前后脑电信号的θ节律的PDI特征的显著性差异示意图；
图3(c)为本发明实施例1提供的患者推拿前后脑电信号的α节律的PDI特征的显著性差异示意图；
图3(d)为本发明实施例1提供的患者推拿前后脑电信号的β节律的PDI特征的显著性差异示意图；
图4为本发明实施例2提供的装置结构示意图；
图5为本发明实施例3提供的推拿疗效评估系统的结构示意图；
图6为本发明实施例4提供的存储介质的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请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申请，并不用于限定本申请。
实施例1
请参阅图1，为本发明实施例1提供的推拿疗效评估方法的流程图。在本实施例中，根据不同的需求，图1所示的流程图中的步骤的执行顺序可以改变，某些步骤可以省略。
步骤S1：采集患者推拿前后的脑电信号(Electroencephalograph，EEG)。在其中一些实施例中，所述患者可以是慢性骨骼肌疼痛(Chronic muscular pain，CMP)疾病或其它疾病的患者，所述脑电信号包括但不限于，想象运动(Motion Imagery，MI)信号和实际运动信号。进一步地，所述想象运动信号包括左倾想象运动信号(MI with left bending)和右倾想象运动信号(MI with right bending)，所述实际运动信号包括左倾实际运动信号(Real left bending)和右倾实际运动信号(Real right bending)。所述脑电信号通过可穿戴设备(如脑电帽)进行采集。
目前，评估腰背肌肉功能方法包括表面肌电(Electromyography，EMG)、 功能核磁共振成像(Functional Magnetic Resonance Imaging，FMRI)、近红外光谱(Near infrared spectroscopy，NIS)、活组织检查(Muscle Biopsy)和超声动态分析(Sonomyography，SMG)。CMP疾病的主要特点为肌肉软组织损伤，X线、CT、MRI等影像技术无法清楚显示CMP疼痛的肌肉软组织。EMG和SMG可以提供动态便捷的肌肉功能评估，因此，用穿戴式生理参数监测手段更适合CMP疼痛的特点。穿戴式生理参数监测对患者没有辐射、可动态地长时间低负荷地测量患者的生理状态。
步骤S2：对采集的脑电信号进行伪迹去除。具体地，所述步骤S2包括：先对采集的脑电信号执行去除均值操作，然后采用多元经验模式分解(Multivariate Empirical Mode Decomposition，MEMD)方法和典型相关分析(Canonical Correlation Analysis，CCA)方法，去除脑电信号中的肌电伪迹(Electromyography，EMG)。最后，再采用小波-独立分量分析方法，去除脑电信号中的眼动伪迹(Electrooculogram，EOG)。
进一步地，所述去除脑电信号中的肌电伪迹包括：第一步，采用多元经验模式分解方法和典型相关分析方法的联合分析方式，去除CCA相关性低的伪迹；第二步，再经过多元经验模式分解方法第二次去除伪迹。
具体地，参阅图2(a)所示，所述去除脑电信号中的肌电伪迹包括如下步骤：
当接收到被肌电伪迹污染的脑电信号时，执行多元经验模式分解(MEMD)计算；
调用本征模式函数(Intrinsic Mode Functions，IMFs)，然后执行典型相关分析(CCA)计算；
进行肌电伪迹分量的识别，如果识别到肌电伪迹分量，则设置肌电伪迹分量为0，然后执行多元经验模式分解的逆计算，如果没有识别到肌电伪迹分量，则直接执行多元经验模式分解的逆计算，得到执行多元经验模式分解方法和典型相关分析方法(MEMD-CCA)后的脑电信号；
针对执行多元经验模式分解方法和典型相关分析方法后的实际运动脑电 信号，再次执行多元经验模式分解(MEMD)计算(第二次去除伪迹)，得到去除肌电伪迹后的脑电信号。
进一步地，参阅图2(b)所示，所述去除脑电信号中的眼动伪迹包括如下步骤：
针对去除肌电伪迹后的脑电信号，执行带通滤波(Band-pass Filtering)操作(0.5-40Hz)；
继续执行小波-独立分量分析(wavelet Independent Component Analysis，wICA)操作；
进行眼动伪迹分量的识别，如果识别到眼动伪迹分量，则进行眼动伪迹峰值检测和峰值矫正，然后执行独立分量分析的逆变换(Inverse ICA)，如果没有识别到眼动伪迹分量，则直接执行独立分量分析的逆变换，得到去除伪迹后的脑电信号。
步骤S3：从去除伪迹后的脑电信号中提取PDI(Permutation Disalignment Index，排列失调指数)特征值。其中，排列失调指数(PDI)是针对基于排列熵(Permutation Entropy，PE)的时间序列间耦合强度的新度量，利用PDI特征值能够计算不同皮层区域之间的耦合强度，而不仅是单个皮层区域的活动，从而用于动态评估推拿疗效。当然，PDI特征值也可以用作阿尔茨海默病和轻度认知障碍患者大脑连接性的间接脑电图测量。
在其中一些实施例中，所述PDI特征值的计算公式如下：
X t＝[x(t),x(t+L),...,x(t+(m-1)L)] T    (1)
Y t＝[y(t),y(t+L),...,y(t+(m-1)L)] T    (2)
其中，在计算公式(1)和(2)中，x和y代表推拿前或推拿后两个相邻通道的m维脑电信号时间序列，两个时间序列x和y映射到矢量X t和Y t，L代表排列熵中的时间间隔，m代表排列熵中的嵌入维度(如m＝5)，t代表时间；
p X,Y(π i)＝n(π i)/(N-(m-1)L)    (3)
Figure PCTCN2021074649-appb-000002
其中，在计算公式(3)中，π i代表符号矢量，n代表时间序列x和y映射到指定序列π i的出现个数，N代表时间序列x和y的采样点总数(如N＝1024)，概率p x,y(π i)代表时间序列x和y计算排列熵时出现相同排列的个数在总排列个数中所占的比例。在其中一些实施例中，符号矢量π i＝[τ 1,τ 2,...,τ m]，τ代表时间延迟(如τ＝1)。
在计算公式(4)中，参数α高值代表超高斯分布，低值代表亚高斯分布，通过参数α可以调整排列熵对超高斯分布和亚高斯分布的敏感性；PDI(X,Y)代表矢量X t和Y t之间的PDI特征值(代表时间序列x和y之间的排列失调指数)，不同皮层区域的耦合强度越高，PDI特征值越低。
步骤S4：根据提取的PDI特征值，进行推拿疗效显著性分析。
在其中一些实施例中，所述步骤S4包括：将推拿前和推拿后不同通道的PDI特征值分成两类(推拿前和推拿后各一类)，然后采用单因素方差分析(one-way analysis of variance，one-way ANOVA)方法，分析该两类PDI特征值得到推拿疗效定量指标的PDI特征值的显著性差异及其它推拿疗效结果。其中，所述单因素方差分析方法为一种数据统计分析方法，如SPSS统计分析软件提供的方法。
在其中一些实施例中，需要分析的定量指标包括但不限于，不同人群、不同通道、推拿前后的PDI特征值，分析得到的推拿疗效结果包括但不限于，均值、方差、95％的置信区间、相关性系数和显著性差异等。当计算出PDI特征值后，根据不同人群的不同通道的PDI特征值，计算出推拿前后的显著性差异。
在步骤S4中，如果某个推拿疗效定量指标在推拿前和推拿后有显著性差异，并且与临床疼痛量表评分有相关性，则判定该指标对推拿疗效具有敏感性，属于优选指标。举例而言，如果推拿后指标PDI特征值降低，则可能反映患 者脑电波不同通道的连接性增强了，代表推拿疗效较好。但是，需要补充说明的是，由于不同人群针对临床疼痛量表的评分有所差异，判断推拿疗效不能简单依据评分降低或上升，而需要根据定量指标与临床疼痛量表评分的相关性判定该指标对推拿疗效是否具有敏感性，只要灵敏度足够就能用于判断推拿疗效。
参阅图3(a)-图3(d)所示，分别为本发明实施例1提供的患者推拿前后脑电信号的4个节律(δ节律、θ节律、α节律、β节律)的PDI特征的显著性差异示意图，其中，横坐标代表测试通道(如AF3_F7、F7_F3、...、F8_AF4等)，纵坐标代表相邻两个通道的PDI特征值，每个节律包括患者推拿前后脑电信号的左倾想象运动、右倾想象运动、左倾实际运动、右倾实际运动的显著性差异。在本发明中，当p值<0.05为显著性差异，图3(a)-图3(d)中*表示显著性差异。
根据图3(a)-图3(d)的推拿疗效显著性分析结果可以得出，基于PDI特征值的脑电信号的α(alpha)节律和β(beta)节律在多个脑电信号测试通道间(如FC5_T7或FC6_F4的通道间)都有显著性差异，效果很显著。
实施例2
请参阅图4，为本发明实施例2提供的装置结构示意图。
在其中一些实施例中，所述装置2可包括但不限于，存储器21、与所述存储器21耦接的处理器22，所述存储器21和处理器22可通过系统总线相互通信连接。需要指出的是，图4仅示出了具有组件21和22的装置2，但是应理解的是，实施例2并没有示出装置2的所有组件，装置2具有可以替代实施的更多或者更少的组件。其中，所述装置2可以是机架式服务器、刀片式服务器、塔式服务器或机柜式服务器等计算设备，该装置2可以是独立的服务器，也可以是多个服务器所组成的服务器集群。
所述存储器21存储有用于实现上述推拿疗效评估方法的程序指令。所述存储器21至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、 硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器21可以是所述装置2的内部存储单元，例如该装置2的硬盘或内存。在另一些实施例中，所述存储器21也可以是所述装置2的外部存储设备，例如该装置2上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。当然，所述存储器21还可以既包括所述装置2的内部存储单元也包括其外部存储设备。本实施例中，所述存储器21通常用于存储安装于所述装置2的操作系统、各类应用软件、和系统代码等。此外，所述存储器21还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器22用于执行存储器21存储的程序指令以控制推拿疗效评估方法的执行。其中，处理器22还可以称为CPU(Central Processing Unit，中央处理单元)。处理器22可能是一种集成电路芯片，具有信号的处理能力。处理器22还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。该处理器22通常用于控制所述装置2的总体操作，例如执行与所述装置2进行数据交互或者通信相关的控制和处理等。本实施例2中，所述处理器22用于运行所述存储器21中存储的程序代码或者处理数据等。
在其中一些实施例中，所述装置2还包括网络接口(如无线网络接口或有线网络接口)，该网络接口通常用于在所述装置2与其他电子设备(如手机)之间建立通信连接。例如，所述网络接口用于通过网络将所述装置2与外部数据平台相连，在所述装置2与外部数据平台之间的建立数据传输通道和通信连接。所述网络可以是企业内部网(Intranet)、互联网(Internet)、全球移动通讯系统(Global System of Mobile communication，GSM)、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、4G网络、5G网络、蓝牙(Bluetooth)、Wi-Fi等无线或有线网络。
实施例3
请参阅图5，为本发明实施例3提供的推拿疗效评估系统的结构示意图。
本实施例中，所述的推拿疗效评估系统3可以被分割成一个或多个程序模块，所述一个或者多个程序模块被存储于所述存储器21中，并由一个或多个处理器(如处理器22)所执行，以完成本发明。例如，在图5中，所述的推拿疗效评估系统3可以被分割成信号采集模块31、伪迹去除模块32、特征提取模块33、以及分析模块34。本发明所称的程序模块是指能够完成特定功能的一系列计算机程序指令段，比程序更适合于描述所述推拿疗效评估系统3在所述装置2中的执行过程。以下将就各程序模块31-34的功能进行详细描述。
所述信号采集模块31用于采集患者推拿前后的脑电信号；所述伪迹去除模块32用于对采集的脑电信号进行伪迹去除；所述特征提取模块33用于从去除伪迹后的脑电信号中提取PDI特征值；所述分析模块34用于根据提取的PDI特征值，进行推拿疗效显著性分析。上述模块的详细技术方案在本发明提供的推拿疗效评估方法中已有详细描述，在此不再赘述。
实施例4
请参阅图6，为本发明实施例4提供的存储介质的结构示意图。
在其中一些实施例中，所述存储介质4存储有能够实现上述所有方法的程序文件41，其中，该程序文件41可以以软件产品的形式存储在上述存储介质4中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本发明各个实施方式方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质，或者是计算机、服务器、手机、平板等终端设备。
本发明实施例的一种推拿疗效评估方法、装置、系统及存储介质，通过采集患者推拿前后的脑电信号，对采集的脑电信号进行伪迹去除，从去除伪迹后的脑电信号中提取PDI特征值，根据提取的PDI特征值，进行推拿疗效显著性分析，评估患者推拿疗效。与现有技术相比，本发明实施例至少具有以下有益效果：提升了推拿疗效评估结果的准确性和可靠性。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本发明中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本发明所示的这些实施例，而是要符合与本发明所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种推拿疗效评估方法，其特征在于，所述方法包括以下步骤：
    S1：采集患者推拿前后的脑电信号；
    S2：对采集的脑电信号进行伪迹去除；
    S3：从去除伪迹后的脑电信号中提取排列失调指数(PDI)特征值；及
    S4：根据提取的PDI特征值，进行推拿疗效显著性分析。
  2. 如权利要求1所述的推拿疗效评估方法，其特征在于，所述步骤S2包括：
    对采集的脑电信号执行去除均值操作；
    采用多元经验模式分解(MEMD)方法和典型相关分析(CCA)方法，去除脑电信号中的肌电伪迹；
    采用小波-独立分量分析方法，去除脑电信号中的眼动伪迹。
  3. 如权利要求2所述的推拿疗效评估方法，其特征在于，所述去除脑电信号中的肌电伪迹包括：
    第一步，采用多元经验模式分解方法和典型相关分析方法的联合分析方式，去除CCA相关性低的伪迹；
    第二步，经过多元经验模式分解方法第二次去除伪迹。
  4. 如权利要求2所述的推拿疗效评估方法，其特征在于，所述去除脑电信号中的肌电伪迹包括如下步骤：
    当接收到被肌电伪迹污染的脑电信号时，执行多元经验模式分解计算；
    调用本征模式函数，然后执行典型相关分析计算；
    进行肌电伪迹分量的识别，如果识别到肌电伪迹分量，则设置肌电伪迹分量为0，然后执行多元经验模式分解的逆计算，如果没有识别到肌电伪迹分量，则直接执行多元经验模式分解的逆计算，得到执行多元经验模式分解方法和典 型相关分析方法后的脑电信号；
    针对执行多元经验模式分解方法和典型相关分析方法后的实际运动脑电信号，再次执行多元经验模式分解计算，得到去除肌电伪迹后的脑电信号。
  5. 如权利要求2所述的推拿疗效评估方法，其特征在于，所述去除脑电信号中的眼动伪迹包括如下步骤：
    针对去除肌电伪迹后的脑电信号，执行带通滤波操作；
    继续执行小波-独立分量分析操作；
    进行眼动伪迹分量的识别，如果识别到眼动伪迹分量，则进行眼动伪迹峰值检测和峰值矫正，然后执行独立分量分析的逆变换，如果没有识别到眼动伪迹分量，则直接执行独立分量分析的逆变换，得到去除伪迹后的脑电信号。
  6. 如权利要求1所述的推拿疗效评估方法，其特征在于，所述PDI特征值的计算公式如下：
    X t＝[x(t),x(t+L),...,x(t+(m-1)L)] T    (1)；
    Y t＝[y(t),y(t+L),...,y(t+(m-1)L)] T    (2)；
    其中，在计算公式(1)和(2)中，x和y代表推拿前或推拿后两个相邻通道的m维脑电信号时间序列，两个时间序列x和y映射到矢量X t和Y t，L代表排列熵中的时间间隔，m代表排列熵中的嵌入维度，t代表时间；
    p X,Y(π i)＝n(π i)/(N-(m-1)L)    (3)；
    Figure PCTCN2021074649-appb-100001
    其中，在计算公式(3)中，π i代表符号矢量，n代表时间序列x和y映射到指定序列π i的出现个数，N代表时间序列x和y的采样点总数，概率p x,y(π i)代表时间序列x和y计算排列熵时出现相同排列的个数在总排列个数中所占的比例，符号矢量π i＝[τ 1,τ 2,...,τ m]，τ代表时间延迟；
    在计算公式(4)中，参数α高值代表超高斯分布，低值代表亚高斯分布，PDI(X,Y)代表矢量X t和Y t之间的PDI特征值。
  7. 如权利要求1所述的推拿疗效评估方法，其特征在于，所述步骤S4包括：
    将推拿前和推拿后不同通道的PDI特征值分成两类，然后采用单因素方差分析方法，分析该两类PDI特征值得到推拿疗效定量指标的PDI特征值的显著性差异；
    如果某个推拿疗效定量指标在推拿前和推拿后有显著性差异，并且与临床疼痛量表评分有相关性，则判定该指标对推拿疗效具有敏感性。
  8. 一种装置，其特征在于，所述装置包括存储器、与所述存储器耦接的处理器，其中：
    所述存储器存储有用于实现权利要求1-7任一项所述的推拿疗效评估方法的程序指令；
    所述处理器用于执行所述存储器存储的所述程序指令以控制推拿疗效评估方法的执行。
  9. 一种推拿疗效评估系统，其特征在于，该系统包括：
    信号采集模块，用于采集患者推拿前后的脑电信号；
    伪迹去除模块，用于对采集的脑电信号进行伪迹去除；
    特征提取模块，用于从去除伪迹后的脑电信号中提取排列失调指数(PDI)特征值；及
    分析模块，用于根据提取的PDI特征值，进行推拿疗效显著性分析。
  10. 一种存储介质，其特征在于，存储有处理器可运行的程序指令，所述程序指令用于执行权利要求1至7任一项所述推拿疗效评估方法。
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