Abstract
Currently, security plays a crucial role in military, forensic and other industry applications. Traditional biometric authentication methods such as fingerprint, voice, face, iris and signature may not meet the demand for higher security. At present, the utility of biological signals in the area of security became popular. ECG signal is getting wide attention to use it as a tool for biometric recognition in authentication applications. ECG signals can provide more accurate biometrics for personal identity recognition. In machine learning, over-fitting is one of the major problems when working with a large data set of features so that an effective statistical technique is needed to control it. In this research, ECG signals are acquired from 20 individuals over 6 months in the MIT-BIH ECG-ID database. Altogether, a high-dimensional (N = 72) set of ECG features are extracted. These features are further fed to an algorithm, which reduces the feature space by classifying vital features and avoiding random, correlated and over-fitted features to increase the prediction accuracy. In this paper, a new intelligent statistical learning method, namely least absolute shrinkage and selection operator (LASSO), is proposed to select appropriate features for identification. The refined features thus obtained are trained with popular machine learning algorithms such as artificial neural networks, multi-class one-against-all support vector machine and K-nearest neighbour (K-NN). Finally, the performance of the proposed method with and without LASSO is compared using performance metrics. From the experimental results, it is observed that the proposed method of LASSO with K-NN classifier is effective with a recognition accuracy of 99.1379%.
Similar content being viewed by others
References
Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20
Wu SS, Chen P, Swindlehurst AL, Hung P (2019) Cancelable biometric recognition with ECGs: subspace-based approaches. IEEE Trans Inf Forensics Secur 14(5):1323–1336
Israel SA, Scruggs WT, Worek WJ, Irvine JM (2003) Fusing face and ECG for personal Identification. In: Proceedings of 32nd Applied Imagery Pattern Recognition Workshop (AIPR-03), USA. pp 226–231
Odinaka I, Lai P, Kaplan AD, O’Sullivan JA, Sirevaag EJ, Rohrbaugh JW (2012) ECG biometric recognition: a comparative analysis. IEEE Trans Inf Forensics Secur 7(6):1812–1824
Biel L, Petterson O, Philipson L, Wide P (2001) ECG analysis a new approach in human identification. IEEE Trans Instrum Meas 50(3):812
Irvine JM, Widerhold BK, Gavshon LW (2001) Heart rate variability: a new biometric for human identification. In: Proceedings of International Conference on Artificial Intelligence, Las Vegas, USA. pp 1106–1111
Hatzinakos D, Agrafioti F, Gao J (2011) Heart biometrics: theory, methods and applications. Biom B 3:199–216
Belgcem N, Ali A, Fouenier R (2012) ECG based human authentication using wavelets and random forests. Int J Cryptogr Inf Secur 2(1):01–11
Wang Y, Agrafioti F, Hatzinakos D (2008) Analysis of human electrocardiogram for biometric recognition. EURASIP J Adv Signal Process 19:148–158
Venkatesh N, Jayaraman S (2010) Human electrocardiogram for biometrics using DTW and FLDA. In: International Conference on Pattern recognition (ICPR). pp 3838–3841
Mar T, Zaunseder S, Martínez JP, Llamedo M, Poll R (2011) Optimization of ECG classification by means of feature selection. IEEE Trans Biomed Eng 58(8):2168–2177
Bassiouni M, Khaleefa W, EI-Dahsan E, Salem ABM (2016) A machine learning technique for person identification using ECG signals. Int J Appl Phys 1:37–41
Camara C, Lopez PP, Tapiador JE (2015) Human identification using compressed ECG signals. J Med Syst 39(11):148–158
Lin SL, Chen CK, Lin CL, Yang WC, Chiang CT (2014) Individual identification based on chaotic electrocardiogram signals during muscular exercise. IET Biometrics 3:257–266
Israel SA, Irvine JM, Cheng A, Widerhold BK (2005) ECG to identify Individuals. J Pattern Recognit 38(1):133–142
Srivastva R, Singh YN (2017) Human recognition using discrete cosine transform and discriminant analysis of ECG. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla. pp 1–5
Komeili M, Armanfard N, Hatzinakos D, Venetsanopoulos A (2015). Feature selection from multisession electrocardiogram signals for identity verification. In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, NS, pp 603–608
Hejazi M, Al-Haddad SAR, Singh YP, Hashim SJ, Aziz AFA (2016) ECG Biometric authentication based on non-fiducial approach using kernel methods. Digit Signal Proc 52:72–86
Kraska T, Talwalkar A, Duchi J, Griffith R, Franklin MJ, Jordan M (2013) ML base: a distributed machine learning system. In: 6th Biennial Conference on Innovative Data systems Research, USA, pp 1–7
Taşkın G, Kaya H, Bruzzone L (2017) Feature selection based on high dimensional model representation for hyperspectral images. IEEE Trans Image Process 26(6):2918–2928
Patro KK, Kumar PR (2015) De-noising of ECG raw signal by cascaded window based digital filters configuration. In: 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Bhubaneswar, pp 120–124
Patro KK, Rajesh Kumar P (2015) A novel frequency-time based approach for the detection of characteristic waves in electrocardiogram signal. Lecture notes in Electrical engineering, Springer India vol 372, no 1, pp 57–67
Patro KK, Kumar PR (2017) Effective feature extraction of ECG for Biometric application. Procedia Comput Sci 115:296–306
Patro KK, Kumar PR (2017) Machine learning classification approaches for biometric recognition system using ECG signals. J Eng Sci Technol Rev 10(6):01–08
Devi A, Misal A (2013) A survey on classifiers used in heart valve disease detection. Int J Adv Res Electr Electron Instrum Eng 2(1):609–614
Adegoke, Omatayo O (2014) Review of feature selection methods in medical image processing. IOSR J Eng 4(1):01–05
Hawkins DM (2004) The problem of over fitting. J Chem Inf Comput Sci 44:1–12
Rosasco L, Poggio T (2015) A regularization tour of machine learning. MIT-9.250 Lecture notes
Tibshirani Robert (1996) Regression shrinkage and Selection via the lasso. J R Stat Soc Soc B Wiley 58(1):267–288
Zou Hui, Hastie Trevor (2005) Regularization and variable selection via Elastic net. J R Stat Soc Soc B Wiley 67(2):301–320
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220
Gupta A, Thomas B (2014) Neural network based indicative ECG Classification. In: 5th IEEE International Conference (Confluence), pp 277–279
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425
Ghofrani N, Bostani R (2010) Reliable features for an ECG-based Biometric System. In: Proceedings of the 17th Iranian Conference of Biomedical Engineering (ICBME2010), pp 01–05
Shen WD, Tompkins WJ, Hu YH (2011) Implementation of a one-lead ECG human identification system on a normal population. J Eng Comput Innov 2(1):12–21
Lin SL, Chen CK, Lin CL, Yang WC, Chiang CT (2014) Individual identification based on chaotic electrocardiogram signals during muscular exercise. IET Biometrics 3(4):257–266
Gutta S, Cheng Qi (2016) Joint feature extraction and classifier design for ECG based biometric recognition. IEEE J Biomed Health Inf 20(2):460–468
Salloum R, Jay Kuo C (2017) ECG based biometrics using recurrent neural networks. In: IEEE ICASSP-2017. pp 2062–2066
Komeili M, Louis W, Armanfard N, Hatzinakos D (2018) Feature selection for non-stationary data: application to human recognition using medical biometrics. IEEE Trans Cybern 48(5):01–14
Silva Teodoro FG, Peres SM, Lima CAM (2017) Feature selection for biometric recognition based on electrocardiogram signals. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK. pp 2911–2920
Abdeldayem SS, Bourlai T (2018) ECG-based human authentication using high-level spectro-temporal signal features. In: 2018 IEEE International Conference on Big Data (Big Data), pp 4984–4993
Chen PT, Wu SC, Hsieh JH (2017) A cancelable biometric scheme based on multi-lead ECGs. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo. pp 3497–3500
Zemzemi M et al. (2018) Toward a low cost, high performance ecg based biometrics: a preliminary work. In: 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), Hammamet, pp 55–59
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patro, K.K., Reddi, S.P.R., Khalelulla, S.K.E. et al. ECG data optimization for biometric human recognition using statistical distributed machine learning algorithm. J Supercomput 76, 858–875 (2020). https://doi.org/10.1007/s11227-019-03022-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-03022-1