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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (3): 566-576    DOI: 10.3785/j.issn.1008-973X.2025.03.014
计算机技术     
基于脉搏波信号多维度特征的身份识别
傅幼萍1(),张航2,*(),厉梦菡2,孟濬2
1. 浙江长征职业技术学院 计算机与信息技术学院，浙江 杭州 310023
2. 浙江大学 电气工程学院，浙江 杭州 310058
Identity recognition based on multi-dimensional features of pulse wave signals
Youping FU1(),Hang ZHANG2,*(),Menghan LI2,Jun MENG2
1. School of Computer and Information Technology, Zhejiang Changzheng Vocational and Technical College, Hangzhou 310023, China
2. College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China
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摘要：

现有光电容积脉搏波(PPG)身份识别方法特征表征不全面、鲁棒性不强，为此提出基于PPG信号多维度特征的身份识别方法. 该方法将PPG信号的非线性维度作为重要特征引入身份识别. 对PPG信号进行预处理；分别从时域、频域和非线性3个维度提取PPG信号的特征参数；通过优化和选择，构建有效的特征集；将该特征集用于身份识别，并对身份识别系统的性能进行分析和评估. 通过对多维度的全面分析，该方法实现了较全面的特征提取，并且时域、频域和非线性维度分析提供的互补信息增强了识别系统的鲁棒性. 在包含200个主体和1000条数据的身份识别任务中，该方法取得了98.4%的准确率. 与KNN之类其他现有研究的对比分析表明，本研究方法取得了较高的准确率. 结果表明，构建多维度特征对于提高PPG身份识别任务准确率至关重要.
关键词： 脉搏波;  身份识别;  特征提取;  数据挖掘;  信号分析    
Abstract:

A novel identity recognition method based on multi-dimensional features extracted from photoplethysmography (PPG) signals was proposed, addressing the limitations of existing methods in terms of incomplete feature representation and weak robustness. The non-linear dimension of PPG signals was incorporated as a crucial feature for identity recognition. After preprocessing the PPG signals were processed, and features were extracted from three distinct dimensions, i.e., time domain, frequency domain, and non-linearity. An effective feature set was then constructed through optimization and selection. Finally, this feature set was utilized for identity recognition, and the performance of the recognition system was analyzed and evaluated. By comprehensively analyzing multiple dimensions, the proposed method achieved comprehensive feature extraction. Furthermore, the complementary information provided by time domain, frequency domain, and non-linearity analysis enhanced the robustness of the recognition system. On an identity recognition task involving 200 subjects and 1000 samples, the proposed approach achieved an accuracy of 98.4%. Comparative analysis with other state-of-the-art methods, such as KNN, demonstrated the superior accuracy of the proposed approach. Results indicated the significance of constructing multi-dimensional features for enhancing the accuracy of PPG identity recognition tasks.
Key words: pulse wave    identity recognition    feature extraction    data mining    signal analysis
收稿日期: 2024-02-26 出版日期: 2025-03-10
CLC:  TP 393  
基金资助: 浙江省基础公益研究计划资助项目（LGF21F030003）.
通讯作者: 张航     E-mail: lele_girl@163.com;22210156@zju.edu.cn
作者简介: 傅幼萍（1980—），女，讲师，硕士，从事智能视觉、数据挖掘研究. orcid.org/0009-0007-1405-5490. E-mail：lele_girl@163.com
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引用本文:

傅幼萍,张航,厉梦菡,孟濬. 基于脉搏波信号多维度特征的身份识别[J]. 浙江大学学报(工学版), 2025, 59(3): 566-576.

Youping FU,Hang ZHANG,Menghan LI,Jun MENG. Identity recognition based on multi-dimensional features of pulse wave signals. Journal of ZheJiang University (Engineering Science), 2025, 59(3): 566-576.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.03.014    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I3/566

图 1  身份识别系统流程图
图 2  10 s的PPG信号的滤波效果
图 3  10 s的PPG信号的基线漂移去除效果
参数名称定义表达式
波峰幅值$ h $A点的幅值$ h $
上升支时间$ {t_1} $点O到点A经历的时间$ {t_1} $
下降支时间$ {t_2} $点A到点$ {O'} $经历的时间$ {t_2} $
全周期$ t $点O到点$ {O'} $经历的时间$ {t_1}+{t_2} $
上升支斜率$ {k_{OA}} $$ OA $的斜率$ {h}/{{{t_1}}} $
下降支斜率$ {k_{{O'}A}} $$ {O'}A $的斜率$ {h}/{{{t_2}}} $
上升面积$ {S_{{{OAM}}}} $上升曲线$ OAM $包围面积$ \displaystyle\sum\nolimits_{i = 1}^{{n_0}} {{x_i}} $
下降面积$ {S_{{O'AM}}} $下降曲线$ {O'}AM $包围面积$ \displaystyle\sum\nolimits_{i = {n_0}}^n {{x_i}} $
全周期面积$ {S_{OAO'}} $PPG曲线$ OA{O'} $包围面积$ \displaystyle\sum\nolimits_{i = 1}^n {{x_i}} $
比例$ K $脉搏波压力脉动分量的均值在
脉动分量最大值中所占比例		$ \dfrac{{{P_{\text{m}}} - {P_{\text{d}}}}}{{{P_{\text{s}}} - {P_{\text{d}}}}} $
表 1  PPG信号的时域特征参数名称、定义及表达式
图 4  时域特征参数图
参数名称FD/Hz参数名称FD/Hz
power_10~0.02power_50.13~0.16
power_20.02~0.05power_60.16~0.30
power_30.05~0.09power_70.30~1.00
power_40.09~0.13power_all0~1.00
表 2  PPG信号的频域特征参数名称及其含义
图 5  基于10 s的PPG信号的标准化功率谱
图 6  参数取值对特征选择结果的影响
序号特征参数序号特征参数
1FuzzyEn10$ {S_{OAO'}} $
2RCMDE11power_all
3$ {k_{O'A}} $12power_7
4PermEn13$ {S_{OAM}} $
5power_114SampleEn
6power_515K值
7power_416$ {k_{OA}} $
8power_617t1
9h18power_2
表 3  根据特征选择结果构建的有效特征集
模型A/%SS/(obs·s?1)Tinfer/sS/kB
DT40.0~140005.16573
KNN96.0~130000.63517
Linear SVM94.0~151084.5089547
QuadraticSVM94.8~121114.60137104
Cubic SVM94.8~121120.50131862
Gaussian SVM97.6~111101.50159960
LDA98.4~120000.861048
表 4  身份识别的不同模型
图 7  不同邻居个数下KNN分类模型的准确率
核函数k1k2A/%SS(obs·s?1)
Linear2.5—96.8~14
Quadratic2.0—95.6~11
Cubic2.0—95.2~11
Gaussian2.06.598.0~11
表 5  SVM模型参数优化结果
图 8  参数优化前后SVM分类模型的准确率
图 9  不同维度特征集的分类准确率
文献年份M（PPG数据）特征提取分类器A/%
注：1) FAR为假接受率；2) FRR为假拒绝率.
文献[30]200317时域KNN94.0
文献[9]200317时域模糊逻辑94.0
文献[5]201440时域KNN94.4
文献[6]201510时域贝叶斯网络97.5
文献[31]201510（708组）时域FNNFAR:4.21)
FRR:3.72)
文献[7]201615时域LDA100.0
文献[10]201623高斯参数LDA95.7
文献[32]201742小波变换KNN99.8
文献[15]201742小波变换SVM100.0
文献[33]202020时域+频域SVM93.1
文献[34]2021100—CNN+LSTM98.0
文献[35]202135频域CNN99.4
文献[12]2022100时域+频域+小波变换CNNLSTM98.3
文献[11]202350—MsNRPNet92.0
本研究25（125组）时域+频域+非线性特征LDA100.0
—50（250组）99.9
200（1000组）98.4
表 6  本研究PPG身份识别方法与相关研究的比较
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