ECG-based identity recognition via deterministic learning

Figures & data

Table 1. Distribution of the number of recordings per subject.

Diagnosis result	Healthy				Patients
Number of recordings	2	3	5	7	2	3	4	5
Number of subjects	7	5	1	1	20	29	48	2

Figure 1. Modelling results of TVCG signal of recording s0302lrem taken from the PTB database: $f_1(V(t))$(a); $f_2(V(t))$(b); $f_3(V(t))$ (c); $F(V(t))$(d).

Figure 2. $\parallel {\tilde{V}}^k{\parallel }_{L_1},k=1,\cdots ,6$ for recognition patient251/s0503_rem.

Figure 3. Dynamics of two recordings of taken from the PTB database: patient001 (a); patient117 (b).

Figure 4. Dynamics of two recordings from two different subjects: patient004 and patient005 (a); patient165 and patient169 (b).

Table 2. Results from the first experiment.

Sub-experiment	Training	Test	Correct	Accuracy (%)
(a)	27	14	14	100.0
(b)	14	14	14	100.0
(c)	14	27	26	96.3
Average result				98.3

Table 3. Results from the second experiment.

Sub-experiment	Training	Test	Correct	Accuracy (%)
(a)	232	99	95	96.0
(b)	99	99	93	93.9
(c)	99	232	211	91.0
Average result				93.3

Table 4. Results from the third experiment.

Sub-experiment	Training	Test	Correct	Accuracy (%)
(a)	259	113	108	95.6
(b)	113	259	107	94.7
(c)	113	259	235	90.7
Average result				92.8

Table 5. Results of the 4-fold cross-validation.

Fold	Training set	Test set	Correct	Accuracy (%)
1	259	113	107	94.7
2	259	113	105	92.9
3	232	86	84	97.7
4	164	52	52	100
Average result				96.3

Table 6. Comprehensive summary of ECG-based identity recognition using the PTB database or other public databases.

Reference	Sample number	Feature type	Technique	Accuracy
Wang et al. [3]	13 HS, ptbdb+13 HS, nsrdb	MF, AF	RDHB, WLFS, LDAC, kNN	100.0%
Agrafioti and Hatzinakos [26]	14 HS, ptbdb	ABF	AC, LDA, NNC, ED	100.0%
Plataniotis et al. [29]	14 HS, ptbdb	ABF	AC, DCT, NNC, ED GLLC,	92.9%
Ghofrani et al. [30]	12 HS, ptbdb	ARC, MPSD	kNN, MLP, PN	100.0%
Agrafioti and Hatzinakos [13]	30 mitdb+13ptbdb+13 nsrdb	ABF	LDA, ED, PCA, CD, kNN	96.4%
Singh and Gupta [31]	32 mitbd+18 nsrdb	MF	CTM, PPCC	99.0%
Proposed	14 HS, ptbdb	dynamics	DL	100.0%
	99 PS, ptbdb	dynamics	DL	96.0%
	14 HS+99 PS, ptbdb	dynamics	DL	95.6%

Note: ABF, autocorrelation based features; AC, autocorrelation; AF, analytic feature; ARC, autoregressive coefficients; CD, cosine distance; CTM, correlation-based template matching; DCT, discrete cosine transform; DL, deterministic learning; ED, Euclidean distance; GLLC, Gaussian log-likelihood classifier; HS, healthy subject; kNN, k nearest neighbours; LDA, linear discriminant analysis; LDAC, LDA classifier; MF, morphological feature; mitdb, MIT-BIH arrhythmia database; MLP, multi-layer perceptron; MPSD, mean power spectral density; NNC, nearest neighbor classifier; NN, neural network; nsrdb, MIT-BIH normal sinus rhythm; PCA, principal component analysis; PD, Privacy database; PNN, probabilistic neural network; PPCC, Pearson product-moment correlation coefficient; PS, patient subject; ptbdb, PTB database; RDHB, reduced-dimension heart beat (via PCA or LDA); WLFS = Wilks’ Lambda feature selection.

Wang Y, Agrafioti F, Hatzinakos D, et al. Analysis of human electrocardiogram for biometric recognition. EURASIP J Adv Signal Process. 2007 [cited 2017 Jun 10];2008(1):148658. DOI:10.1155/2008/148658

 Google Scholar

Agrafioti F, Hatzinakos D. Fusion of ECG sources for human identification. In: 3rd International Symposium on Communications, Control and Signal Processing, 2008 (ISCCSP 2008); Proceedings. 2008 Mar 12–14; St. Julians, Malta. Piscataway (NJ): IEEE; 2008. p. 1542–1547.

 Google Scholar

Plataniotis KN, Hatzinakos D, Lee JK. ECG biometric recognition without fiducial detection. In: 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. 2006 Sep 19–21; Baltimore (MD). IEEE; 2006. p. 1–6. DOI:10.1109/BCC.2006.4341628

 Google Scholar

Ghofrani N, Bostani R. Reliable features for an ECG-based biometric system. In: 17th Iranian Conference of Biomedical Engineering (ICBME); Proceedings. 2010 Nov 3–4; Isfahan (Iran). IEEE; 2010, p. 1–5. DOI:10.1109/ICBME.2010.5704918

 Google Scholar

Agrafioti F, Hatzinakos D. ECG biometric analysis in cardiac irregularity conditions. SIViP. 2009;3(4):329–343.

 Google Scholar

Singh YN, Gupta P. Correlation-based classification of heartbeats for individual identification. Soft Comput. 2011;15(3):449–460.

 Web of Science ®Google Scholar