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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (3): 566-576    DOI: 10.3785/j.issn.1008-973X.2025.03.014
    
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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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     
Received: 26 February 2024      Published: 10 March 2025
CLC:  TP 393  
Fund:  浙江省基础公益研究计划资助项目（LGF21F030003）.
Corresponding Authors: Hang ZHANG     E-mail: lele_girl@163.com;22210156@zju.edu.cn
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Youping FU
Hang ZHANG
Menghan LI
Jun MENG
Cite this article:

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.

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


基于脉搏波信号多维度特征的身份识别

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 
Fig.1 Identity recognition system flowchart
Fig.2 Filtering effect of 10-second PPG signal
Fig.3 Baseline drift removal effect of 10-second PPG signal
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}}}}} $
Tab.1 Names, definitions, and calculation formulas of time-domain characteristic parameters for PPG signals
Fig.4 Time domain characteristic parameter map
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
Tab.2 Names of frequency domain characteristic parameters of PPG signals and their meanings
Fig.5 Standardized power spectrum based on 10 second PPG signal
Fig.6 Impact of parameter values on feature selection results
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
Tab.3 Effective feature set constructed based on feature selection results
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
Tab.4 Different models of identity recognition
Fig.7 Accuracy of KNN classification models under different neighbor numbers
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
Tab.5 Optimization results of SVM model parameters
Fig.8 Accuracy of SVM classification model before and after parameter optimization
Fig.9 Classification accuracy of feature sets with different dimensions
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
Tab.6 Comparison of proposed PPG identity recognition with relevant research
[8]   SUN Bin, XUE Yunan, CHEN Xiaohui A method for PPG signal identification based on improved neighborhood rough set[J]. Foreign Electronic Measurement Technology, 2022, 41 (6): 8- 13
[9]   GU Y Y, ZHANG Y T. Photoplethysmographic authentication through fuzzy logic [C]// IEEE EMBS Asian-Pacific Conference on Biomedical Engineering . Kyoto: IEEE, 2003: 136–137.
[10]   SALANKE N S G R, MAHESWARI N, SAMRAJ A, et al. Enhancement in the design of biometric identification system based on photoplethysmography data [C]// International Conference on Green High Performance Computing . Nagercoil: IEEE, 2013: 1–6.
[11]   JEON Y J, KANG S J Multi-slice nested recurrence plot (msnrp): a robust approach for person identification using daily ECG or PPG signals[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 106799
doi: 10.1016/j.engappai.2023.106799
[12]   LEE J A, KWAK K C Personal identification using an ensemble approach of 1D-LSTM and 2D-CNN with electrocardiogram signals[J]. Applied Sciences, 2022, 12 (5): 2692
doi: 10.3390/app12052692
[13]   ELGENDI M On the analysis of fingertip photoplethysmogram signals[J]. Current cardiology reviews, 2012, 8 (1): 14- 25
doi: 10.2174/157340312801215782
[14]   HAYES M J, SMITH P R A new method for pulse oximetry possessing inherent insensitivity toartifact[J]. IEEE Transactions on Biomedical Engineering, 2001, 48 (4): 452- 461
doi: 10.1109/10.915711
[15]   KARIMIAN N, GUO Z, TEHRANIPOOR M, et al. Human recognition from photoplethysmography (PPG) based on non-fiducial features [C]// IEEE International Conference on Acoustics, Speech and Signal Processing . New Orleans: IEEE, 2017: 4636–4640.
[16]   ALAM R-U, ZHAO H, GOODWIN A, et al Differences in power spectral densities and phase quantities due to processing of EEG signals[J]. Sensors, 2020, 20: 6285
doi: 10.3390/s20216285
[17]   HU J F. Biometric system based on EEG signals: a nonlinear model approach [C]// International Conference on Machine Vision and Human-machine Interface . Kaifeng: IEEE, 2010: 48–51.
[18]   HEJAZI M, AL-HADDAD S A R, HASHIM S J, et al. Non-fiducial based ECG biometric authentication using one-class Support Vector Machine [C]// Signal Processing: Algorithms, Architectures, Arrangements, and Applications . Poznan: IEEE, 2017: 190–194.
[19]   YE Z, TURNER R. Intelligent linear and nonlinear analysis for biometric fingerprint recognition [C]// 39th Southeastern Symposium on System Theory . Macon: IEEE, 2007: 315–319.
[1]   RAJARAM S, VOLLALA S, RAMASUBRAMANIAN N, et al Enhanced and secured random number generation for eUASBP[J]. International Journal of System Assurance Engineering and Management, 2022, 13 (3): 1135- 1150
[2]   SPOOREN J, PREUVENEERS D, JOOSEN W. PPG2Live: using dual PPG for active authentication and liveness detection [C]// International Conference on Biometrics . Crete: IEEE, 2019: 1–6.
[20]   LEE T K M, SANEI S, CLARKE B. Fusion of nonlinear measures in fronto-normal gait recognition [C]// 5th International Multi-conference on Computing in the Global Information Technology . Valencia: IEEE, 2010: 104–109.
[21]   PINCUS S M Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences, 1991, 88 (6): 2297- 2301
doi: 10.1073/pnas.88.6.2297
[3]   NOWARA E M, SABHARWAL A, VEERARAGHAVAN A, et al. PPG secure: biometric presentation attack detection using photopletysmograms [C]// IEEE International Conference on Automatic Face and Gesture Recognition . Washington, DC: IEEE, 2017: 56–62.
[4]   GU Y Y, ZHANG Y, ZHANG Y T. A novel biometric approach in human verification by photoplethysmographic signals [C]// 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine . Birmingham: IEEE, 2003: 13–14.
[22]   章思佳, 林栋, 齐诗仪, 等 基于眼电信号近似熵探讨针灸治神的临床应用[J]. 中国针灸, 2023, 43 (1): 79- 82
ZHANG Sijia, LIN Dong, QI Shiyi, et al Clinical application of acupuncture-moxibustion for the treatment of spirit based on approximate entropy of electrooculogram signal[J]. Chinese Acupuncture and Moxibustion, 2023, 43 (1): 79- 82
[23]   樊凤杰, 白洋, 纪会芳 基于脑电非线性动力学特征探究TEAS内关穴对焦虑的影响[J]. 计量学报, 2022, 43 (1): 133- 139
FAN Fengjie, BAI Yang, JI Huifang Explore the lnfluence of transcutaneous electrical acupoint stimulation neiguan point on anxiety based on electroencephalogram nonlinear dynamic analysis method[J]. Acta Metrologica Sinica, 2022, 43 (1): 133- 139
doi: 10.3969/j.issn.1000-1158.2022.01.21
[5]   KAVSAOGLU A R, POLAT K, BOZKURT M R A novel feature ranking algorithm for biometric recognition with PPG signals[J]. Computers in Biology and Medicine, 2014, 49: 1- 14
doi: 10.1016/j.compbiomed.2014.03.005
[6]   JAAFAR N A L, SIDEK K A, AZAM S N A M. Acceleration plethysmogram based biometric identification [C]// International Conference on BioSignal Analysis, Processing and Systems . Kuala Lumpur: IEEE, 2015: 16–21.
[7]   CHAKRABORTY S, PAL S. Photoplethysmogram signal based biometric recognition using linear discriminant classifier [C]// International Conference on Control, Instrumentation, Energy and Communication . Kolkata: IEEE, 2016: 183–187.
[8]   孙斌, 薛毓楠, 陈小惠 基于改进邻域粗糙集的 PPG 信号身份识别方法[J]. 国外电子测量技术, 2022, 41 (6): 8- 13
[24]   RICHMAN J S, MOORMAN J R Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278 (6): H2039- H2049
doi: 10.1152/ajpheart.2000.278.6.H2039
[25]   CHEN W, WANG Z, XIE H, et al Characterization of surface EMG signal based on fuzzy entropy[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15 (2): 266- 272
doi: 10.1109/TNSRE.2007.897025
[26]   AZAMI H, ROSTAGHI M, ABÁSOLO D, et al Refined composite multiscale dispersion entropy and its application to biomedical signals[J]. IEEE Transactions on Biomedical Engineering, 2017, 64 (12): 2872- 2879
doi: 10.1109/TBME.2017.2679136
[27]   郭秀花. 医学统计学与SPSS软件实现方法(第2版)[M]. 北京: 科学出版社, 2017: 33–47.
[28]   徐慧, 付迎春, 付朝川, 等 改进WOA算法优化SVM的网络入侵检测[J]. 实验室研究与探索, 2019, 38 (8): 128- 133
XU Hui, FU Yingchun, FU Chaochuan, et al Improved whale optimization algorithm to optimize support vector machine for network intrusion detection[J]. Research and Exploration in Laboratory, 2019, 38 (8): 128- 133
doi: 10.3969/j.issn.1006-7167.2019.08.031
[29]   MOODY G B, MARK R G. A database to support development and evaluation of intelligent intensive care monitoring [C]// Computers in Cardiology . Indianapolis: IEEE, 1996: 657–660.
[30]   GU Y Y, ZHANG Y, ZHANG Y T. A novel biometric approach in human verification by photoplethysmo-signals [C]// 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine . Birmingham: IEEE, 2003: 13–14.
[31]   LEE A, KIM Y. Photoplethysmography as a form of biometric authentication [C]// 2015 IEEE Sensors . Busan: IEEE, 2015: 1–2.
[32]   KARIMIAN N, TEHRANIPOOR M, FORTE D. Non-fiducial PPG-based authentication for healthcare application [C]// IEEE EMBS International Conference on Biomedical and Health Informatics . Orlando: IEEE, 2017: 429–432.
[33]   KHAN M U, AZIZ S, NAQVI S Z H, et al. Pattern analysis towards human verification using photoplethysmograph signals [C]// International Conference on Emerging Trends in Smart Technologies . Karachi: IEEE, 2020: 1–6.
[34]   HWANG D Y, TAHA B, LEE D S, et al Evaluation of the time stability and uniqueness in PPG-based biometric system[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 116- 130
doi: 10.1109/TIFS.2020.3006313
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