经穴电信号特性分析与分类算法研究
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摘要
经络理论是中医基础理论的核心之一,在指导中医临床实践中起着决定性的作用。经络是体表与脏腑,机体环境和外环境联系的主要通道和途径,经络的生理作用是通过其对信息与物质的动态传输作用表现出来的,因此展开现代经络特性研究,特别是从电信息的角度开展经络信息传递、生理调控及其与人体生理病理状态的联系等方面的研究,将对中医经络临床诊断、预防和治疗具有重要意义,同时也为经络实质的研究提供重要的参考。本文首先对所采集的经络穴位电信号滤波预处理,然后从信号的特性分析入手对经穴电信号进行时频分析、高阶谱分析、非线性动力学特性分析等研究工作,最后对穴位信号与相对应的非穴位参照信号进行了分类研究。所取得的主要研究成果为：
对所采集的经络穴位信号进行了滤波处理研究。针对穴位电信号的产生机理及其采集所遇到的影响因素和难点进行了分析。利用卡尔曼滤波对采集的经穴电信号进行滤波处理。由于卡尔曼滤波算法存在一定的不鲁棒特点,在存在概率模型方面的不确定情况下对其进行了鲁棒化处理,实验的结果表明基于鲁棒卡尔曼滤波方法对经穴电信号具有较好的滤波作用。
对经穴电信号进行了时频特性分析。首先研究了短时傅里叶变换的理论与实现,然后对小波变换和Wigner分布进行了分析与研究。在研究了静态时频特性之后,利用Gabor变换和小波熵等概念对经穴电信号进行了动态时频分析。
对经穴电信号进行了高阶谱分析。高阶谱不仅保留了信号的幅值信息而且还保留了信号的相位信息。利用双谱的切片谱很容易可以检测出信号之间的相位耦合现象。为了兼顾计算速度与信息的全面性,本文提出一种基于复合切片谱的经穴电信号特性分析方法。经时间复杂度分析与实验数据验证,表明该方法在保留了水平与垂直方向上的信息基础上,与双谱的计算量相比减少了计算量,节约了运行时间,提高了运行效率。在研究高阶谱的基础上,还分析研究了经穴电信号的Wigner时变高阶谱与Wigner时变双谱切片。实验的结果表明,健康人体的穴位信号与非穴位信号之间、饭前与饭后不同状态下的穴位信号之间、健康人体穴位信号与心脏心血管疾病患者的穴位信号之间,其高阶谱特性存在明显的差异,并且其差异表现出一定的规律性。对于小波分解后的低频子带信号,饭后穴位信号的高阶谱特征明显高于饭前；而小波分解后的高频子带信号,饭后穴位信号的高阶谱特征低于饭前。心脏心血管疾病患者的穴位信号高阶谱特征明显低于正常人的相应高阶谱特征。
对经穴电信号进行了混沌特性分析。首先利用替代数据法对经穴电信号进行非线性检测。检测的结果表明人体经络穴位信号是一种非线性的信号。然后分别对健康人体的经穴信号与对应的非穴位信号以及心脏心血管疾病患者的穴位信号进行相空间重构,在此基础上对一些常用的非线性动力学性能指标进行了对比分析。实验的结果表明,穴位电信号表现出非线性动力学特性,并且健康人体的经穴信号与对应的非穴位信号以及心脏心血管疾病患者的穴位信号其非线性动力学参数表现出不同程度的差异。
对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号之间进行了基于优化神经网络的分类研究。传统的BP神经网络算法具体收敛速度慢,容易收敛到局部最小,对于多值分类泛化能力较弱。针对以上神经网络算法中的不足,本文提出一种基于量子进化神经网络的经穴电信号分类模型。实验结果表明,该模型能够较好地对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号进行分类。
对基于经穴电信号的支持向量机分类模型进行了研究。针对目前支持向量机分类算法还存在着惩罚函数与核函数难以确定的问题,本文提出了一种基于粒子群优化算法的经穴电信号支持向量机分类模型。实验结果表明该模型对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号具有较好的分类准确率。与此同时,相比神经网络分类模型而言,基于粒子群优化算法的SVM分类算法比神经网络分类模型的分类准确率略有提高。
Meridians theory is one of the cores in the basic theory of Traditional Chinese Medicine (TCM). It plays a decisive role in the guidance of the TCM clinical practice. Meridians are the main channels and pathways between the body and organs, the physical organisms and the external environment. The physiological role of the meridian is manifested through its dynamic transmission of information and material. Therefore, the research on the meridian characteristics, especially within the information flow, physiological regulation mechanism, and the relation between the meridians and physiological and pathological states, has important implications to meridians clinical diagnosis, prevention and treatment, and of great reference to further research on the meridian substantiality. In this dissertation, the electrical signal of acupuncture point is preprocessed by a well designed Kalman Filter firstly. Then with the introduction of the time-frequency domain analysis, high-order spectrum analysis and the nonlinear dynamic systems analysis, the characteristic parameters of the acupoint signals are extracted and analyzed. Finally, the classification algorithms between the acupoint signals and the non-acupoint signals had been discussed. The main contributions of the dissertation are summarized as follows.
A filter used for denoising the acupoint electrical signals is designed. On the basis of the analysis of the factors and difficulties encountered in the acupoint electrical signal generation mechanism and the acquisition, the Kalman filter was chosen to process the signal. Also, considering the weak robustness of the Kalman filter, the stabilization treatment is carried out in uncertainty case of the existence probability model. The experiment results show that the proposed robust Kalman filtering method has better filtering effect both on acupoint signals and non-acupoint signals.
The characteristic analysis on acupoint signals was carried out from both the time-domain and frequency-domain. The theory and implementation of the short-time Fourier transform are firstly discussed. Then, the wavelet transform and Wigner distribution analysis are analyzed. Based on the study on static time-frequency analysis, dynamic time-frequency analysis of Gabor transform and wavelet entropy method is introduced to analyze the acupoint signals.
Higher order spectral analysis is applied to acupoint signals. A new composite slice spectrum computing method is proposed for the analysis of the characteristics of acupoint signals, where the computing speed and comprehensiveness of the information is taken into account. Time complexity analysis and experiments results show that the proposed method retains information on the horizontal and vertical directions. Furthermore, it reduces the amount of computation, consumes less time, and improves the operating speed. On the basis of high-order spectrum analysis, the Wigner time varying higher order spectrum and its slices are applied for the analysis on acupoint signals. Experiment results show that there exist obvious differences between the healthy human body acupoint signals and the corresponding non-acupuncture point's signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease. For the signal from low-frequency sub-band of the wavelet decomposition, its high order spectral characteristics of after meals was significantly higher than that of before meals; while the high-frequency sub-band signals show the opposite trend. The high-order spectral features of acupoint signal from patients with cardiovascular disease significantly lower than that of healthy humam bodys.
Chaotic Characteristic Analysis of the acupoint signals has been carried out. Firstly, the surrogate-data technique is chosen to detect the nonlinear property of the acupoint electrical signals. The results have shown the nonlinearities of the acupoint signals. Then, the phase spaces are reconstructed for the acupoint signals, the corresponding non-acupoint signals and signals from patients of cardiovascular disease respectively, and then the commonly-used nonlinear dynamics characteristics are analyzed. The experimental results reveal that the acupoint and non-acupoint signals are both chaotic, while the corresponding nonlinear dynamics parameters are different to a certain extent.
The classification methods based on optimized neural networks are studied. To deal with the problems with the traditional BP algorithms such as the slow convergence speed, local minimum, less robustness on multivalent classification, a quantum evolution neural networks model is developed in this dissertation for the classification of the acupoint electrical signals. The experimental results have shown that the model gives satisfactory classification ability between the acupoint signals and the non-acupoint signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease.
The support vector machine algorithm has also been implemented for the classification of the meridians signals. A particle swarm optimization based support vector machine classification model is proposed to meet the difficulty in the choice of the proper parameter in the support vector machine.The experiment results have shown that the model has exhibited an effective classification performance on acupoint signals and non-acupoint electrical signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease. Meanwhile, it gives a better performance than the neural network classification model.
对所采集的经络穴位信号进行了滤波处理研究。针对穴位电信号的产生机理及其采集所遇到的影响因素和难点进行了分析。利用卡尔曼滤波对采集的经穴电信号进行滤波处理。由于卡尔曼滤波算法存在一定的不鲁棒特点,在存在概率模型方面的不确定情况下对其进行了鲁棒化处理,实验的结果表明基于鲁棒卡尔曼滤波方法对经穴电信号具有较好的滤波作用。
对经穴电信号进行了时频特性分析。首先研究了短时傅里叶变换的理论与实现,然后对小波变换和Wigner分布进行了分析与研究。在研究了静态时频特性之后,利用Gabor变换和小波熵等概念对经穴电信号进行了动态时频分析。
对经穴电信号进行了高阶谱分析。高阶谱不仅保留了信号的幅值信息而且还保留了信号的相位信息。利用双谱的切片谱很容易可以检测出信号之间的相位耦合现象。为了兼顾计算速度与信息的全面性,本文提出一种基于复合切片谱的经穴电信号特性分析方法。经时间复杂度分析与实验数据验证,表明该方法在保留了水平与垂直方向上的信息基础上,与双谱的计算量相比减少了计算量,节约了运行时间,提高了运行效率。在研究高阶谱的基础上,还分析研究了经穴电信号的Wigner时变高阶谱与Wigner时变双谱切片。实验的结果表明,健康人体的穴位信号与非穴位信号之间、饭前与饭后不同状态下的穴位信号之间、健康人体穴位信号与心脏心血管疾病患者的穴位信号之间,其高阶谱特性存在明显的差异,并且其差异表现出一定的规律性。对于小波分解后的低频子带信号,饭后穴位信号的高阶谱特征明显高于饭前；而小波分解后的高频子带信号,饭后穴位信号的高阶谱特征低于饭前。心脏心血管疾病患者的穴位信号高阶谱特征明显低于正常人的相应高阶谱特征。
对经穴电信号进行了混沌特性分析。首先利用替代数据法对经穴电信号进行非线性检测。检测的结果表明人体经络穴位信号是一种非线性的信号。然后分别对健康人体的经穴信号与对应的非穴位信号以及心脏心血管疾病患者的穴位信号进行相空间重构,在此基础上对一些常用的非线性动力学性能指标进行了对比分析。实验的结果表明,穴位电信号表现出非线性动力学特性,并且健康人体的经穴信号与对应的非穴位信号以及心脏心血管疾病患者的穴位信号其非线性动力学参数表现出不同程度的差异。
对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号之间进行了基于优化神经网络的分类研究。传统的BP神经网络算法具体收敛速度慢,容易收敛到局部最小,对于多值分类泛化能力较弱。针对以上神经网络算法中的不足,本文提出一种基于量子进化神经网络的经穴电信号分类模型。实验结果表明,该模型能够较好地对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号进行分类。
对基于经穴电信号的支持向量机分类模型进行了研究。针对目前支持向量机分类算法还存在着惩罚函数与核函数难以确定的问题,本文提出了一种基于粒子群优化算法的经穴电信号支持向量机分类模型。实验结果表明该模型对健康人体的穴位信号与非穴位信号、饭前与饭后不同状态下的穴位信号以及健康人体穴位信号与心脏心血管疾病患者的穴位信号具有较好的分类准确率。与此同时,相比神经网络分类模型而言,基于粒子群优化算法的SVM分类算法比神经网络分类模型的分类准确率略有提高。
Meridians theory is one of the cores in the basic theory of Traditional Chinese Medicine (TCM). It plays a decisive role in the guidance of the TCM clinical practice. Meridians are the main channels and pathways between the body and organs, the physical organisms and the external environment. The physiological role of the meridian is manifested through its dynamic transmission of information and material. Therefore, the research on the meridian characteristics, especially within the information flow, physiological regulation mechanism, and the relation between the meridians and physiological and pathological states, has important implications to meridians clinical diagnosis, prevention and treatment, and of great reference to further research on the meridian substantiality. In this dissertation, the electrical signal of acupuncture point is preprocessed by a well designed Kalman Filter firstly. Then with the introduction of the time-frequency domain analysis, high-order spectrum analysis and the nonlinear dynamic systems analysis, the characteristic parameters of the acupoint signals are extracted and analyzed. Finally, the classification algorithms between the acupoint signals and the non-acupoint signals had been discussed. The main contributions of the dissertation are summarized as follows.
A filter used for denoising the acupoint electrical signals is designed. On the basis of the analysis of the factors and difficulties encountered in the acupoint electrical signal generation mechanism and the acquisition, the Kalman filter was chosen to process the signal. Also, considering the weak robustness of the Kalman filter, the stabilization treatment is carried out in uncertainty case of the existence probability model. The experiment results show that the proposed robust Kalman filtering method has better filtering effect both on acupoint signals and non-acupoint signals.
The characteristic analysis on acupoint signals was carried out from both the time-domain and frequency-domain. The theory and implementation of the short-time Fourier transform are firstly discussed. Then, the wavelet transform and Wigner distribution analysis are analyzed. Based on the study on static time-frequency analysis, dynamic time-frequency analysis of Gabor transform and wavelet entropy method is introduced to analyze the acupoint signals.
Higher order spectral analysis is applied to acupoint signals. A new composite slice spectrum computing method is proposed for the analysis of the characteristics of acupoint signals, where the computing speed and comprehensiveness of the information is taken into account. Time complexity analysis and experiments results show that the proposed method retains information on the horizontal and vertical directions. Furthermore, it reduces the amount of computation, consumes less time, and improves the operating speed. On the basis of high-order spectrum analysis, the Wigner time varying higher order spectrum and its slices are applied for the analysis on acupoint signals. Experiment results show that there exist obvious differences between the healthy human body acupoint signals and the corresponding non-acupuncture point's signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease. For the signal from low-frequency sub-band of the wavelet decomposition, its high order spectral characteristics of after meals was significantly higher than that of before meals; while the high-frequency sub-band signals show the opposite trend. The high-order spectral features of acupoint signal from patients with cardiovascular disease significantly lower than that of healthy humam bodys.
Chaotic Characteristic Analysis of the acupoint signals has been carried out. Firstly, the surrogate-data technique is chosen to detect the nonlinear property of the acupoint electrical signals. The results have shown the nonlinearities of the acupoint signals. Then, the phase spaces are reconstructed for the acupoint signals, the corresponding non-acupoint signals and signals from patients of cardiovascular disease respectively, and then the commonly-used nonlinear dynamics characteristics are analyzed. The experimental results reveal that the acupoint and non-acupoint signals are both chaotic, while the corresponding nonlinear dynamics parameters are different to a certain extent.
The classification methods based on optimized neural networks are studied. To deal with the problems with the traditional BP algorithms such as the slow convergence speed, local minimum, less robustness on multivalent classification, a quantum evolution neural networks model is developed in this dissertation for the classification of the acupoint electrical signals. The experimental results have shown that the model gives satisfactory classification ability between the acupoint signals and the non-acupoint signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease.
The support vector machine algorithm has also been implemented for the classification of the meridians signals. A particle swarm optimization based support vector machine classification model is proposed to meet the difficulty in the choice of the proper parameter in the support vector machine.The experiment results have shown that the model has exhibited an effective classification performance on acupoint signals and non-acupoint electrical signals, acupoint signals between different states of before meals and after meals, acupoint signals between healthy human body and patients of cardiovascular disease. Meanwhile, it gives a better performance than the neural network classification model.
引文
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