An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion
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摘要: 针对单模态的心电信号(ECG)或光电容积脉搏波信号(PPG)识别技术中存在的精度不高，未考虑类内相关性等问题，该文提出基于判别相关分析法(DCA)对ECG与PPG组合特征矩阵进行特征层融合以及对K-最近邻(KNN)和支持向量机(SVM)分类器在决策层融合的识别方法。实验结果表明，使用融合特征(ECG-PPG)与融合分类器(KNN-SVM)的方法对23名受试者进行分类识别的准确率可以达到98.2%，识别精度在常规环境下优于单模态识别。为多模生物特征身份识别提供了一种有效模型。Abstract: Because single mode ElectroCardioGraph (ECG) and PhotoPlethysmoGraph(PPG) existed problem with the low recognition accuracy, not considering intra-class correlation, this paper proposes a recognition method based on the Discriminant Correlation Analysis (DCA) for the feature layer fusion of the ECG and PPG combined feature matrix and the fusion of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers at the decision layer. The experimental results show that the use of fusion features (ECG-PPG) and fusion the classifier (KNN-SVM) method can classify and recognize 23 subjects with an accuracy of 98.2%, and the recognition accuracy is better than single-modal recognition in the conventional environment. It provides an effective model for multimodal biometric identification.
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表 1 小波变换提取到的信号特征
编号 特征 具体描述 1 PQ_T PQ波持续时间(ms) 2 ST_T ST波持续时间(ms) 3 QR_T QR波持续时间(ms) 4 RS_T RS波持续时间(ms) 5 R_A R波幅值(μV) 6 PT_A PT波振幅差 7 SQ_A SQ波振幅差 8 PR_A PR波振幅差 
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表 2 PPG信号的特征与描述
编号 特征 具体描述 1 P_A 收缩波峰幅值(μV) 2 V_A 舒张波幅值(μV) 3 L_A 降中峡幅值(μV) 4 T_A 波谷幅值(μV) 5 PT_A 波谷到收缩波幅值差(μV) 6 VT_A 波谷到舒张波距离幅值差(μV) 7 PV_A 收缩波峰到舒张波幅值差(μV) 8 TT_T 波谷到波谷持续时间(ms) 9 TP_T 波谷到收缩波峰持续时间(ms) 10 PV_T 收缩波峰到舒张波时间(ms) 11 TV_T 波谷到舒张波时间(ms)
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