基于倒谱特征的脉象信号识别算法研究
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摘要
中医将脉象视为生命的语言,它蕴含了丰富的人体健康状况信息。但由于中医脉象在教学中难度很大,临床脉诊时分歧较多,使脉诊经验无法交流。脉诊的定性化和主观性大大影响了其精度和可行性,成为中医脉诊应用、发展和交流的制约因素。随着传感器技术和计算机处理技术的发展,人们开始致力于脉诊的客观化,希望用现代科学技术的方法和仪器,推进脉诊的科学性。
高阶统计量是研究非线性和非高斯信号的有效工具,它在信号检测、特征提取以及谐波恢复等方面具有特有的优越性。本文主要应用倒双谱和三阶倒谱熵对脉象信号进行特征提取及分类识别研究。
我们通过对脉图的形态分析,在频域脉搏图中提取特征信息以及采用模式识别系统进行分类识别等,在国内外已有不少报道,但算法还不够完善。本课题在总结前人工作的基础上,对脉象数据的特征提取,脉象的识别进行了研究。主要工作包括以下几个方面:
首先,在对国内外人体脉象客观化研究方法进行比较的基础上,针对以往脉象特征提取算法单一地从频域提取特征值存在一些缺点,本文提出了多种基于倒双谱的特征参数提取方法:倒双谱的对角切片的零分量值、倒双谱对角切片中m = 1, n=1时的分量值、特定区间倒双谱峰态系数值、特定区间内三阶倒谱熵值。虽然这几种方法各自的识别效果还是可以,但是运算量较大,识别有些复杂。
其次,鉴于模式识别方法既简单又易于实现的特点,特别是在建立脉象信号识别模型时能减少数据的计算量,因而能够获得到较好的识别精度及较快的响应速度,本文在深入研究脉象数据特征的基础上,构建了基于倒双谱和马氏距离的辨识系统。并用它们对正常健康人和吸毒病人两类脉象信号进行了成功识别,平均识别率可以达到87.5%,为以后临床实验提供参考。
实验表明,采用本文提出的基于倒谱提取的特征参数和马氏距离组成的模型进行的中医脉象信号分析识别时,识别正确率取得了一定效果。
本文的研究是针对不同人体中医脉象信号而提出的一种脉象特征提取与识别方法,这一研究对于脉象客观化、脉象模型识别和现代信号处理技术在医疗辅助诊断中的应用,推动传统医学现代化具有积极意义。
Pulse is called the language of life in Traditional Chinese Medicine (TCM), it contains a wealth of information about the status of human health. However, due to pulse at the teaching of Chinese medicine is very difficult, when the doctors have lots of difference in clinic, so the experience of pulse diagnosis can not be communicated. The qualitative and subjective pulse diagnosis greatly affects its accuracy and feasibility, and restricts the application, developing and communication of Traditional Chinese Medicine pulse diagnosis. With the development of sensor and computer technology, people hope to apply modern technology to human pulse diagnosis to reveal the essence and feature of pulse phenomena scientifically, which is the main research aspect in this paper.
Higher-order Statistics (HOS) is the primary analysis tool in analyzing non-Gaussian and non-linear signal. It possesses plenty of advantages in signal detection, feature extraction and harmonic retrieval. Based on the Bicepstrum, the features of Human Pulse Signal are extracted and classified in the paper.
There have been a lot of reports about extracting features in time domain by analyzing the characteristics of human pulse. There are also a lot of works focused on how to use neural network to classify and recognize the pulse manifestations. But these techniques are far from perfect. In this paper, the following works are reported.
Firstly, based on comparing different objective algorithms of human pulse manifestations considering that most traditional methods are apt to obtain the information only from time-domain and can not preserve all the original information of pulse, varieties of approaches feature extraction of human pulse signal based on Bicepstrum are developed, which include the value of zero diagonal slice of Bicepstrum, the value of diagonal slice of Bicepstrum when m = 1, n=1, the kurtosis of Bicepstrum within a specific range and the value of third-order Cepstrum entropy within a specific range. Although these methods can achieve respective effects, larger operations and complex identification can exist.
Secondly, Pattern Recognition technique is simple, effective and reliable. Especially, it can greatly decrease the computational load and memory. This technique obtains ideal results with high precision of recognition and rapid response rate. By analyzing the pulse signal, the recognition systems based on Bicepstrum and Mahalanobis distance have been constructed. Using the system, the pulse signals of the normal and drug addicts have been successful recognized, and the average recognition rate is about 87.5%. By comparing the result, the system optimization is introduced for clinical experiment in future.
The experiment results show that the extracted parameter values based on the above methods as the feature vector using Mahalanobis distance is effective and correct.
As the algorithm implemented in this paper is in accordance with characteristics acquisition and recognition of Traditional Chinese Medicine syndrome types, it could be used as reference in objective research and real clinics. It also has much significance to develop the study of Traditional Chinese Medicine.
高阶统计量是研究非线性和非高斯信号的有效工具,它在信号检测、特征提取以及谐波恢复等方面具有特有的优越性。本文主要应用倒双谱和三阶倒谱熵对脉象信号进行特征提取及分类识别研究。
我们通过对脉图的形态分析,在频域脉搏图中提取特征信息以及采用模式识别系统进行分类识别等,在国内外已有不少报道,但算法还不够完善。本课题在总结前人工作的基础上,对脉象数据的特征提取,脉象的识别进行了研究。主要工作包括以下几个方面:
首先,在对国内外人体脉象客观化研究方法进行比较的基础上,针对以往脉象特征提取算法单一地从频域提取特征值存在一些缺点,本文提出了多种基于倒双谱的特征参数提取方法:倒双谱的对角切片的零分量值、倒双谱对角切片中m = 1, n=1时的分量值、特定区间倒双谱峰态系数值、特定区间内三阶倒谱熵值。虽然这几种方法各自的识别效果还是可以,但是运算量较大,识别有些复杂。
其次,鉴于模式识别方法既简单又易于实现的特点,特别是在建立脉象信号识别模型时能减少数据的计算量,因而能够获得到较好的识别精度及较快的响应速度,本文在深入研究脉象数据特征的基础上,构建了基于倒双谱和马氏距离的辨识系统。并用它们对正常健康人和吸毒病人两类脉象信号进行了成功识别,平均识别率可以达到87.5%,为以后临床实验提供参考。
实验表明,采用本文提出的基于倒谱提取的特征参数和马氏距离组成的模型进行的中医脉象信号分析识别时,识别正确率取得了一定效果。
本文的研究是针对不同人体中医脉象信号而提出的一种脉象特征提取与识别方法,这一研究对于脉象客观化、脉象模型识别和现代信号处理技术在医疗辅助诊断中的应用,推动传统医学现代化具有积极意义。
Pulse is called the language of life in Traditional Chinese Medicine (TCM), it contains a wealth of information about the status of human health. However, due to pulse at the teaching of Chinese medicine is very difficult, when the doctors have lots of difference in clinic, so the experience of pulse diagnosis can not be communicated. The qualitative and subjective pulse diagnosis greatly affects its accuracy and feasibility, and restricts the application, developing and communication of Traditional Chinese Medicine pulse diagnosis. With the development of sensor and computer technology, people hope to apply modern technology to human pulse diagnosis to reveal the essence and feature of pulse phenomena scientifically, which is the main research aspect in this paper.
Higher-order Statistics (HOS) is the primary analysis tool in analyzing non-Gaussian and non-linear signal. It possesses plenty of advantages in signal detection, feature extraction and harmonic retrieval. Based on the Bicepstrum, the features of Human Pulse Signal are extracted and classified in the paper.
There have been a lot of reports about extracting features in time domain by analyzing the characteristics of human pulse. There are also a lot of works focused on how to use neural network to classify and recognize the pulse manifestations. But these techniques are far from perfect. In this paper, the following works are reported.
Firstly, based on comparing different objective algorithms of human pulse manifestations considering that most traditional methods are apt to obtain the information only from time-domain and can not preserve all the original information of pulse, varieties of approaches feature extraction of human pulse signal based on Bicepstrum are developed, which include the value of zero diagonal slice of Bicepstrum, the value of diagonal slice of Bicepstrum when m = 1, n=1, the kurtosis of Bicepstrum within a specific range and the value of third-order Cepstrum entropy within a specific range. Although these methods can achieve respective effects, larger operations and complex identification can exist.
Secondly, Pattern Recognition technique is simple, effective and reliable. Especially, it can greatly decrease the computational load and memory. This technique obtains ideal results with high precision of recognition and rapid response rate. By analyzing the pulse signal, the recognition systems based on Bicepstrum and Mahalanobis distance have been constructed. Using the system, the pulse signals of the normal and drug addicts have been successful recognized, and the average recognition rate is about 87.5%. By comparing the result, the system optimization is introduced for clinical experiment in future.
The experiment results show that the extracted parameter values based on the above methods as the feature vector using Mahalanobis distance is effective and correct.
As the algorithm implemented in this paper is in accordance with characteristics acquisition and recognition of Traditional Chinese Medicine syndrome types, it could be used as reference in objective research and real clinics. It also has much significance to develop the study of Traditional Chinese Medicine.
引文
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