International Scholarly Research Notices / 2012 / Article / Tab 1

Review Article

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Table 1

Performance estimates of various methods using ECG as a biometric under different validation and test conditions. SIMCA: soft independent modeling of class analogy, NN: neural network, PCA: principle component analysis, LDA: linear discriminate analysis, EER: equal error rate, and N/R: not reported.

Methods Data sizeData representation techniquesClassification techniquesClassification accuracy Test conditions

Israel et al. [2] 29 subjects Analysis of waveform curvature Linear discriminant analysis 97-98% Normal and different anxiety states
Irvine et al. [3] 43 subjects Eigen pulse analysis, PCA Correlation based analysis~100% enrollment rate Varying stress levels
Biel et al. [5] 20 subjects Siemens ECG using Siemens Megacart Multivariate analysis SIMCA N/R Validated ECG data recorded in 6 weeks
Shen et al. [6] 20 subjects Feature extracted from QRST fiducials Template matching, decision-based NN 80–100% MIT-BIH database [29]
Wang et al. [7] 31 subjects Autocorrelation and discrete cosine transform Nearest neighbor classifier (NN) 94.47%–97.8% PTB and MIT-BIH database [29]
Singh and Gupta [9, 10]50 subjects 250 ECG recordingsTime derivative and waveform curvature analysisCorrelation based classification 98.09%
EER: 1.01%
Physionet database [29]
Multibiometric system using ECG signal Singh et al. [27]78 subjects Time derivative and waveform curvature analysis Transformed based score fusion and classification 99.08%
EER: 0.2%
Physionet database [29], face and fingerprint NIST [33]

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