Multimedia Tools and Applications

, Volume 76, Issue 4, pp 4835–4847 | Cite as

Fusion of physiological measures for multimodal biometric systems

  • Silvio Barra
  • Andrea Casanova
  • Matteo Fraschini
  • Michele Nappi
Article

Abstract

Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances.

Keywords

EEG signal ECG signal Biometric Multimodal system Physiological measures 

References

  1. 1.
    Agrafioti F, Hatzinakos D (2008) Fusion of ecg sources for human identification. In: 3rd international symposium on communications, control and signal processing, 2008. ISCCSP, vol 2008, pp 1542–1547. doi:10.1109/ISCCSP.2008.4537472
  2. 2.
    Barra S, Casanova A, Fraschini M, Nappi M (2015) Eeg/ecg signal fusion aimed at biometric recognition. Springer International PublishingGoogle Scholar
  3. 3.
    Bermudez T, Lowe D, Arlaud-Lamborelle AM (2009) Eeg/ecg information fusion for epileptic event detection. In: 16Th international conference on digital signal processing, 2009, pp 1–8, doi:10.1109/ICDSP.2009.5201231, (to appear in print)
  4. 4.
    Biel L, Pettersson O, Philipson L, Wide P (2001) Ecg analysis: a new approach in human identification. IEEE Trans Instrum Meas 50(3):808–812. doi:10.1109/19.930458 CrossRefGoogle Scholar
  5. 5.
    Boulgouris N, Plataniotis K, Micheli-Tzanakou E (2010) Multimodal physiological biometrics authentication. Wiley-IEEE Press, pp 461–482. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5396661. doi:10.1002/9780470522356.ch18
  6. 6.
    Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet. Biomedizinische Technik/Biomedical Engineering 40(s1):317–318Google Scholar
  7. 7.
    Campisi P, La Rocca D (2014) Brain waves for automatic biometric-based user recognition. IEEE Trans Inf Forensics Secur 9(5):782–800CrossRefGoogle Scholar
  8. 8.
    Del Pozo-Banos M, Alonso JB, Ticay-Rivas JR, Travieso CM (2014) Electroencephalogram subject identification: a review. Expert Syst Appl 41 (15):6537–6554CrossRefGoogle Scholar
  9. 9.
    Delorme A, Makeig S (2004) Eeglab: an open source toolbox for analysis of single-trial {EEG} dynamics including independent component analysis. J Neurosci Methods 134(1):9–21. doi:10.1016/j.jneumeth.2003.10.009 CrossRefGoogle Scholar
  10. 10.
    DelPozo-Banos M, Travieso CM, Weidemann CT, Alonso JB (2015) Eeg biometric identification: a thorough exploration of the time-frequency domain. J Neural Eng 12(5):056,019CrossRefGoogle Scholar
  11. 11.
    Draper HW, Peffer CJ, Stallmann FW, Littmann D, Pipberger HV (1964) The corrected orthogonal electrocardiogram and vectorcardiogram in 510 normal men (frank lead system). Circulation 30(6):853–864CrossRefGoogle Scholar
  12. 12.
    Fraschini M, Hillebrand A, Demuru M, Didaci L, Marcialis G (2015) An eeg-based biometric system using eigenvector centrality in resting state brain networks. IEEE Signal Process Lett 22(6):666–670. doi:10.1109/LSP.2014.2367091 CrossRefGoogle Scholar
  13. 13.
    Fratini A, Sansone M, Bifulco P, Cesarelli M (2015) Individual identification via electrocardiogram analysis. BioMedical Engineering Online 14(1). doi:10.1186/s12938-015-0072-y
  14. 14.
    Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215– e220. doi:10.1161/01.CIR.101.23.e215. http://circ.ahajournals.org/cgi/content/full/101/23/e215PMID:1085218
  15. 15.
    Hoekema R, Uijen GJ, Van Oosterom A (2001) Geometrical aspects of the interindividual variability of multilead ecg recordings. IEEE Trans Biomed Eng 48 (5):551–559CrossRefGoogle Scholar
  16. 16.
    Koné C, Tayari IM, Le-Thanh N, Belleudy C (2015) Multimodal recognition of emotions using physiological signals with the method of decision-level fusion for healthcare applications. In: Inclusive smart cities and e-health. Springer, pp 301–306Google Scholar
  17. 17.
    Kozmann G, Lux RL, Green LS (1989) Sources of variability in normal body surface potential maps. Circulation 79(5):1077–1083CrossRefGoogle Scholar
  18. 18.
    Kyoso M, Uchiyama A (2001) Development of an ecg identification system. In: Engineering in medicine and biology society, 2001. Proceedings of the 23rd annual international conference of the IEEE, vol 4, pp 3721–3723. doi:10.1109/IEMBS.2001.1019645
  19. 19.
    La Rocca D, Campisi P, Vegso B, Cserti P, Kozmann G, Babiloni F, De Vico Fallani F (2014) Human brain distinctiveness based on eeg spectral coherence connectivity. IEEE Trans Biomed Eng 61(9):2406–2412. doi:10.1109/TBME.2014.2317881 CrossRefGoogle Scholar
  20. 20.
    Lopes Da Silva F (2013) {EEG} And meg: relevance to neuroscience. Neuron 80 (5):1112–1128. doi:10.1016/j.neuron.2013.10.017. http://www.sciencedirect.com/science/article/pii/S0896627313009203 CrossRefGoogle Scholar
  21. 21.
    Matos AC, Loureno A, Nascimento J (2014) Embedded system for individual recognition based on {ECG} biometrics. Procedia Technology 17:2013. Conference on Electronics, Telecommunications and Computers {CETC}Google Scholar
  22. 22.
    Noh YH, Hwang GH, Jeong DU (2011) Implementation of real-time abnormal ecg detection algorithm for wearable healthcare. In: 6Th international conference on computer sciences and convergence information technology (ICCIT), 2011, pp 111–114Google Scholar
  23. 23.
    Odinaka I, Lai P H, Kaplan A, O’Sullivan J, Sirevaag E, Rohrbaugh J (2012) Ecg biometric recognition: a comparative analysis. IEEE Trans Inf Forensics Secur 7(6):1812–1824. doi:10.1109/TIFS.2012.2215324 CrossRefGoogle Scholar
  24. 24.
    Pan Y, Ge S, Al Mamun A, Tang FR (2008) Detection of seizures in eeg signal using weighted locally linear embedding and svm classifier. In: IEEE conference on cybernetics and intelligent systems, vol 2008, pp 358–363. doi:10.1109/ICCIS.2008.4670889
  25. 25.
    Plataniotis K, Hatzinakos D, Lee J (2006) Ecg biometric recognition without fiducial detection. In: biometrics symposium: special session on research at the biometric consortium conference, 2006, pp 1–6. doi:10.1109/BCC.2006.4341628
  26. 26.
    Prittopaul P, Sathya S, Jayasree K (2015) Cyber physical system approach for heart attack detection and control using wireless monitoring and actuation system. In: IEEE 9th international conference on intelligent systems and control (ISCO), 2015, pp 1–6. doi:10.1109/ISCO.2015.7282352
  27. 27.
    Ravish D, Shenoy N, Shanthi K, Nisargh S (2014) Heart function monitoring, prediction and prevention of heart attacks: Using artificial neural networks. In: International conference on contemporary computing and informatics (IC3i) 2014, pp 1–6. doi:10.1109/IC3I.2014.7019580
  28. 28.
    Riera A, Dunne S, Cester I, Ruffini G (2008) Starfast: a wire-less wearable eeg/ecg biometric system based on the enobio sensor. In: Proceedings of the international workshop on wearable micro and nanosystems for personalised healthGoogle Scholar
  29. 29.
    Riera A, Soria-Frisch A, Caparrini M, Cester I, Ruffini G (2009) 1 Multimodal physiological biometrics authentication. Biometrics: Theory, Methods, and Applications:461–482Google Scholar
  30. 30.
    Rosli N, Rahman M, Mazlan S, Zamzuri H (2014) Electrocardiographic (ecg) and electromyographic (emg) signals fusion for physiological device in rehab application. In: IEEE student conference on research and development (SCOReD), 2014, pp 1–5. doi:10.1109/SCORED.2014.7072965
  31. 31.
    Ross A, Jain AK (2004) Multimodal biometrics: an overview. In: Proceedings of 12th european signal processing conference (EUSIPCO)Google Scholar
  32. 32.
    Sakai M, Wei D (2008) Wavelet shrinkage applications of eeg-ecg-based human-computer interface. In: 8Th IEEE international conference on computer and information technology, 2008. CIT 2008, pp 538–543. doi:10.1109/CIT.2008.4594732
  33. 33.
    Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR (2004) Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Trans Biomed Eng 51(6):2004CrossRefGoogle Scholar
  34. 34.
    Shahid S, Prasad G, Sinha R (2011) On fusion of heart and brain signals for hybrid bci. In: 5Th international IEEE/EMBS conference on neural engineering (NER), vol 2011, pp 48–52. doi:10.1109/NER.2011.5910486
  35. 35.
    Shantha Selva Kumari R, Prabin Jose J (2011) Seizure detection in eeg using time frequency analysis and svm. In: International conference on emerging trends in electrical and computer technology (ICETECT), 2011, pp 626–630. doi:10.1109/ICETECT.2011.5760193
  36. 36.
    Shen T, Tompkins W, Hu Y (2002) One-lead ecg for identity verification. In: Engineering in medicine and biology, 2002. 24th annual conference and the annual fall meeting of the biomedical engineering society EMBS/BMES conference, 2002. Proceedings of the second joint, vol 1, pp 62–63. doi:10.1109/IEMBS.2002.1134388
  37. 37.
    Soria-Frisch A, Riera A, Dunne S (2010) Fusion operators for multi-modal biometric authentication based on physiological signals. In: IEEE international conference on fuzzy systems (FUZZ), 2010, pp 1–7. doi:10.1109/FUZZY.2010.5584121
  38. 38.
    Verma G, Tiwary U (2014) Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage 102:162CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly
  2. 2.Department of Electrical and Electronic Engineering(DIEE)University of CagliariCagliariItaly
  3. 3.University of SalernoSalernoItaly

Personalised recommendations