Deep Neural Network-based Person Identification using ECG Signals

Main Article Content

Rudresh T. K.
Mallikarjun S. H.
Shameem Banu L

Abstract

In recent times, biometrics is mostly utilized for the authentication or identification of a user for a vast civilian application. Most of the electronic systems have been proposed that employed distinct behavioral or physiological human beings signature for identifying or verifying the user in an automatic manner. Nowadays, Electro Cardio Gram (ECG)-oriented biometric systems are in the exploration stage. The behavior of the ECG signal is distinctive to every person. As ECG is an exclusive physiological signal that is present only in the live people, it is utilized in the new biometric systems for recognizing the people and to counter the fraud as well as the forge attacks. Majority of the traditional techniques limits from the restriction in several points detection in the ECG signal. The contribution of this paper is the enhancement of the novel structure of person identification model by ECG signal. At first, the ECG signal collected from the three benchmark source is subjected for pre-processing, in which the noise is removed by Low Pass Filter (LPF) approach. Further, the Empirical Mode Decomposition (EMD) is adopted for the decomposition of signal. As feature selection is the significant part of classification enhancement, Principle Component Analysis (PCA) is used as the effective feature extraction that takes the most important features from the signal. Finally, the adoption of Deep Neural Network (DNN) is performed as the deep learning model that could identify the exact person from the given ECG signal. The effectiveness of the method is extensively validated on benchmark datasets and retrieves the outcome.

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How to Cite
[1]
Rudresh T. K., Mallikarjun S. H., and Shameem Banu L , Trans., “Deep Neural Network-based Person Identification using ECG Signals”, IJEAT, vol. 12, no. 6, pp. 14–21, Sep. 2023, doi: 10.35940/ijeat.F4262.0812623.
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Author Biographies

Rudresh T. K., Lecturer, Department of Electronics and Communication Engineering, Government Polytechnic, Chamarajanagar (Karnataka), India.

Rudresh T K received B.E degree in Electronics and communication engineering from Visvesvaraya Technological University Belagavi, Karnataka, India and the M.Tech degree in Electronics from Visvesvaraya Technological University Belagavi, Karnataka, India, in 2004 and 2008 respectively. He is working as a Lecturer in the Department of Electronics and Communication Engineering at the Government Polytechnic Kampli, Karnatka, India from 2011 to 2022. From 2023 he is working as lecturer in Department of Electronics and Communication at Government Polytechnic, Chamarajanagara. Before that, he worked as software engineer in L&T Integrated Engineering Services, Mysore, India from 2007 to 2011. His research interests include signal processing, image processing, VLSI and the Internet of Things.

Mallikarjun S. H., Lecturer, Department of Electronics and Communication Engineering, Government Polytechnic, Kampli (Karnataka), India.

Mallikarjun S H received B.E degree in Electronics and communication engineering from Visvesvaraya Technological University Belagavi, Karnataka, India and the M.Tech degree in VLSI Design and Embedded system from Visvesvaraya Technological University Belagavi, Karnataka, India, in 2008 and 2011 respectively. He is working as a Lecturer in the Department of Electronics and Communication Engineering at the Government Polytechnic Kampli, Karnataka, India since 2012. Before that, he worked as Assistant Professor in AIT chickamagaluru, India from 2011 to 2012. His research interests include medical electronics and image processing.

Shameem Banu L, Lecturer, Department of Electronics and Communication Engineering, Government Polytechnic, Bellari (Karnataka), India.

Shameem Banu L received B.E degree in Electronics and Communication Engineering from Visvesvaraya Technological University Belagavi, Karnataka, India and the M.Tech degree in Digital Communication & Networking from Visvesvaraya Technological University Belagavi, Karnataka, India, in 2003 and 2019 respectively. She has worked as a Lecturer in the Department of Electronics and Communication Engineering at the Government Polytechnic Ballari, from 2008 to 2012 and from 2012 to 2022 at the Government Polytechnic, Kampli, Karnataka, India. From 2023 she is working as lecturer in Department of Electronics and Communication at Government Polytechnic, Ballari. Before that, she has also worked as Assistant Professor in RYMEC Ballari, India from 2003 to 2008. Her research interests include Communication Systems and Image processing.

How to Cite

[1]
Rudresh T. K., Mallikarjun S. H., and Shameem Banu L , Trans., “Deep Neural Network-based Person Identification using ECG Signals”, IJEAT, vol. 12, no. 6, pp. 14–21, Sep. 2023, doi: 10.35940/ijeat.F4262.0812623.
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References

Jae-Neung Lee; Keun-Chang Kwak, "Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals", IEEE Access, vol. 7, pp. 48392 - 48404, March 2019. https://doi.org/10.1109/ACCESS.2019.2904095

R. Boostani, M. Sabeti, S. Omranian & S. Kouchaki, "ECG-Based Personal Identification Using Empirical Mode Decomposition and Hilbert Transform", Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 43, pp. 67-75, July 2018. https://doi.org/10.1007/s40998-018-0055-7

Leila Yousofvand, Abdolhossein Fathi & Fardin Abdali-Mohammadi, "Person identification using ECG signal’s symbolic representation and dynamic time warping adaptation", Signal, Image and Video Processing, vol. 13, pp. 245-251, August 2018. https://doi.org/10.1007/s11760-018-1351-4

Debasish Jyotishi; Samarendra Dandapat, "An LSTM-Based Model for Person Identification Using ECG Signal", IEEE Sensors Letters, vol. 4, no. 8, August 2020. https://doi.org/10.1109/LSENS.2020.3012653

Jeremias Sulam, Yaniv Romano, Ronen Talmon, "Dynamical system classification with diffusion embedding for ECG-based person identification", Signal Processing, vol. 130, pp. 403-411, January 2017. https://doi.org/10.1016/j.sigpro.2016.07.026

G. Adam and P. Witold, "ECG Signal Processing, Classication and Interpretation: A Comprehensive Framework of Computational Intelligence", London, U.K.: Springer-Verlag, 2012.

R. G. Afkhami, G. Azarnia, and M. A. Tinati, ``Cardiac arrhythmia classication using statistical and mixture modeling features of ECG signals,'' Pattern Recognit. Lett., vol. 70, pp. 45-51, Jan. 2016. https://doi.org/10.1016/j.patrec.2015.11.018

S. Dutta, A. Chatterjee, and S. Munshi, ``Identication of ECG beats from cross-spectrum information aided learning vector quantization,'' Measurement, vol. 44, no. 10, pp. 2020-2027, 2011. https://doi.org/10.1016/j.measurement.2011.08.014

M. M. Tantawi, K. Revett, A.-B. Salem, and M. F. Tolba, ``A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition,'' Signal, Image Video Process., vol. 9, no. 6, pp. 1271-1280, Sep. 2015. https://doi.org/10.1007/s11760-013-0568-5

S. Wahabi, S. Pouryayevali, S. Hari, and D. Hatzinakos, ``On evaluating ECG biometric systems: Sessaion-dependence and body posture,'' IEEE Trans. Inf. Forensics Security, vol. 9, no. 11, pp. 2002-2013, Nov. 2014. https://doi.org/10.1109/TIFS.2014.2360430

A. Ghaffari, M. R. Homaeinezhad, and M. M. Daevaeiha, ``High resolution ambulatory Holter ECG events detection-delineation via modied multilead wavelet-based features analysis: Detection and quantication of heart rate turbulence,'' Expert Syst. Appl., vol. 38, no. 5, pp. 5299-5310, May 2011. https://doi.org/10.1016/j.eswa.2010.10.028

K. T. Chui, K. F. Tsang, H. R. Chi, B.W. K. Ling, and C. K. Wu, ``An accurate ECG-based transportation safety drowsiness detection scheme,'' IEEE Trans. Ind. Informat., vol. 12, no. 4, pp. 1438-1452, Aug. 2016. https://doi.org/10.1109/TII.2016.2573259

S. Padhy and S. Dandapat, ``Third-order tensor based analysis of multilead ECG for classication of myocardial infarction,'' Biomed. Signal Process. Control, vol. 31, pp. 71-78, Jan. 2017. https://doi.org/10.1016/j.bspc.2016.07.007

Y. Kutlu and D. Kuntalp, ``Feature extraction for ECG heartbeats using higher order statistics of WPD coefcients,'' Comput. Methods Programs Biomed., vol. 105, no. 3, pp. 257-267, 2012. https://doi.org/10.1016/j.cmpb.2011.10.002

S.-M. Dima et al., ``On the detection of myocadial scar based on ECG/VCG analysis,'' IEEE Trans. Biomed. Eng., vol. 60, no. 12, pp. 3399-3409, Dec. 2013. https://doi.org/10.1109/TBME.2013.2279998

Papia Ray and Rajesh Kumar Lenka, "LOW FREQUENCY MODE ESTIMATION OF A DYNAMIC POWER SYSTEM BY NOISE ASSISTED EMPIRICAL MODE DECOMPOSITION", 2017 International Conference on Information Technology, IEEE Access, 2017.

H. Zhao and F. Kamareddine, "A Decision Tree Method on Fuzzy Name Identification from Chinese Phonemic Names to Chinese Names," 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018, pp. 227-232, 2018. https://doi.org/10.1109/CSCI46756.2018.00050

R. Luhadiya and A. Khedkar, "Iris detection for person identification using multiclass SVM," 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), 2016, pp. 387-392, 2016. https://doi.org/10.1109/ICAECCT.2016.7942619

M. Roukhami, M. T. Lazarescu, F. Gregoretti, Y. Lahbib and A. Mami, "Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors," in IEEE Access, vol. 7, pp. 102217-102231, 2019. https://doi.org/10.1109/ACCESS.2019.2931392

S. Ramesh, D. Vydeki, "Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm", INFORMATION PROCESSING IN AGRICULTURE, September 2019. https://doi.org/10.1016/j.inpa.2019.09.002

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