【作者】 陈雪峰；

【导师】 周宽久；

【作者基本信息】 大连理工大学 ， 系统工程， 2006， 硕士

【摘要】 快速的生活节奏,不合理的饮食结构和人口的老龄化均使心血管疾病的发病率和死亡率日趋增加。人们需要一种无创检测方法来了解自身的身体状况,及时发现心血管疾病,避免因为耽误治疗时机而导致意外。脉搏波信号是能反应身体生理状况的重要信号之一,基于中医理论的脉搏波分析提供了一种无创地获取人体健康状况的科学手段。目前国内外对利用脉搏波获取人体生理状况的方法研究尚处于起步阶段,脉搏波与典型中医脉象之间的关系还不是很明确,实际应用较少。因此,研究脉搏波的特征提取与识别方法并与传统中医脉象相对应无论在理论还是实际应用上都具有十分积极的意义。本文作者的主要工作包括以下几个方面: 首先,在对国内、外同类问题的解决方法比较研究的基础上,针对已往脉象识别中所有脉象均使用同一特征提取算法而导致所提取特征不能包含脉搏波所有信息的缺点,本文提出应对不同类型的脉搏波采用不同的特征提取方法:对于周期性的脉搏波采用脉搏波解析模型对脉搏波进行建模,并设计混合遗传算法提取脉象特征;对于脉律不齐的脉搏波信号,则采用小波分析法提取脉搏波信号在不同尺度上的能量作为此类脉象的特征。 其次,在脉象分类算法方面,鉴于中医脉象的分类依据是在背景比较复杂,推理不很明确的情况下得出的,而BP网络具有非线性映射、自适应并行计算的能力,采用BP网络对脉象进行分类。在具体算法实现上,采用附加动量法、学习率自适应调整等改进措施来加速BP网的收敛速度。 最后,将本文提出的脉象特征提取和识别算法应用于脉象识别系统中,通过实际的数据测试,证明了本文算法的可行性和有效性。 本文的算法是针对人体脉搏波与中医脉象特点而提出的一种脉象特征提取与识别方法,对于以后中医脉象客观化研究和类似识别系统的实现有一定的借鉴作用。更多还原

【Abstract】 The accelerated tempo of life, unreasonable structure of food and aging of population are making the morbidity and mortality of Cardiovascular Disease increase gradually. People need a noninvasive method to examine cardiovascular disease. And this method can help them know their health status, find Cardiovascular Disease timely and ward off the accident for missing treatment opportunity. Pulse wave is one of the important physiological signals of body. Pulse wave analysis based on Traditional Chinese Medical Science provides an effective method to know the status of people’s heart with non-ravage test. Now research on pulse wave analysis to get heart status is in primary phase. The relationship between pulse waves and typical pulse manifestations is not unambiguous and the applications of pulse wave analysis are limited. Thus, it is significant to study algorithms on characteristics acquisition of pulse waves and make pulse waves correlate with typical pulse manifestations both in theory and applications. The structure of this treatise is organized as below:Firstly, based on ideas at home and abroad, algorithms that are adopted to acquire characteristics of pulse waves can not include all useful characteristics of pulse waves. In this treatise different algorithms are adopted to acquire characteristics of different kinds of pulse waves: a mathematical model of pulse wave approximate by three-Gauss functions is issued and a hybrid GA is implemented to acquire characteristics of periodic pulse waves. Wavelet Transform (WT) is adopted to analyse aperiodic pulse waves and acquire its characteristics that are the energy of these kind of pulse waves wavelet transform in different scales.Secondly, since the background of pulse waves are complex and the classification basis of pulse waves are unambiguous, BP Neural Network (NN) is adopted to classify human pulse waves for its nonlinear mapping, and parallel computation ability. On idiographic algorithm design, appendix momentum method and auto learning rate adjust method are taken to mend the velocity of the algorithm convergent. In spite of limited training samples, the method in this thesis is superior to traditional pattern recognition methods if choose suitable features to be input-cell such as features extracted by algorithms designed above. The results also manifest that it is feasible to use Neural Network in classifying pulse waves.At last, the algorithms designed above are used in a system of pulse wave recognition. These algorithms are proved to be feasible and effective by data tests.As the algorithms implemented in this treatise are in accordance with characteristics of pulse waves and Chinese pulse manifestations. Hence, it could be used for reference in objective process of Traditional Chinese Medical Science and other pattern recognition systems.更多还原