摘要
中医脉象的准确识别有利于人体疾病的诊断,针对脉象特征模糊问题,提出一种基于自适应噪声完全集合经验模态分解(CEEMDAN)和基本脉象时域特征、能量熵与样本熵的混合特征提取方法。首先,采集人体脉象信号进行小波分解,以去除高频噪声和基线漂移。其次,对5种脉象信号进行CEEMDAN处理得到各阶固有模函数(IMF),计算IMF分量能量占比与脉象信号的相关性,选取5~7阶IMF分量计算能量熵和样本熵。最后,将时域特征、能量熵与样本熵融合的混合特征向量,输入到麻雀算法优化极限学习机(SSA-ELM)中进行脉象识别。实验结果表明,该文所提方法的脉象识别准确率达98.60%,平均精确率为98.64%,单种脉象识别的召回率及F1值都在97%以上。与传统方法相比,该方法具有较好的识别性能。
Accurate identification of traditional Chinese medicine pulse patterns is beneficial for diagnosing human diseases.In response to the problem of fuzzy pulse pattern characteristics,a hybrid feature extraction method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),fundamental pulse waveform time-domain features,energy entropy,and sample entropy is proposed.First,human pulse signal data are collected and subjected to wavelet decomposition to remove high-frequency noise and baseline drift.Second,the CEEMDAN method is applied to process pulse signals of five different types,resulting in intrinsic mode functions(IMF)at various orders.The selection of 5-7 order IMF components for computing energy entropy and sample entropy is based on their energy proportions and correlation with the pulse signal.Finally,a hybrid feature vector combining time-domain features,energy entropy,and sample entropy is input into a Sparrow algorithm-optimized Extreme Learning Machine(SSA-ELM)for pulse pattern recognition.The results demonstrate an accuracy rate of 98.60%,with an average precision of 98.64%.Moreover,recall rates and F1 scores for pulse pattern recognition are consistently above 97%.Compared to traditional methods,the proposed strategy exhibits superior recognition performance.
作者
张靖轩
李长龙
王晓青
江春花
吴晨曦
徐勤奇
ZHANG Jingxuan;LI Changlong;WANG Xiaoqing;JIANG Chunhua;WU Chenxi;XU Qinqi(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China;Maternal and Child Health Hospital of Tangshan,Tangshan 063000,China;Traditional Chinese Medical College,North China University of Science and Technology,Tangshan 063210,China)
出处
《中国测试》
CAS
北大核心
2024年第12期106-116,共11页
China Measurement & Test
基金
国家自然科学基金青年科学基金项目(31701244)
河北省自然科学基金资助项目(H2022209027)
河北省省属高等学校基本科研业务费(JQN2022001)
唐山市人才资助项目(A202203021)
唐山市科技计划项目(21130219C)。
