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Methods | Data size | Data representation techniques | Classification techniques | Classification accuracy | Test conditions |
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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 recordings | Time derivative and waveform curvature analysis | Correlation 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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