Journal of Innovative Science and Engineering
Araştırma Makalesi
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Yıl 2019, Cilt 3, Sayı 1, 11 - 22, 24.06.2019

Ayca HANILCI Hakan GÜRKAN

https://doi.org/10.38088/jise.559236

Öz

Kaynakça

  • [1] Jain, A.K., Ross, A. and Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 4–20.
  • [2] Wang, Y., Agrafioti, F., Hatzinakos, D. and Plataniotis, K.N. (2008). Analysis of Human Electrocardiogram for Biometric Recognition. EURASIP Journal on Advances in Signal Processing, 2008: 1-11.
  • [3] Fang, C. and Chan, H.L. (2009). Human Identification by Quantifying Similarity and Dissimilarity in Electrocardiogram Phase Space. Pattern Recognition, 42(9): 1824-1831.
  • [4] Wübbeler, G., Stavridi, M., Kreiseler, D., Bousseljot, R.D. and Elster, C. (2007). Verification of Humans Using Electrocardiogram. Pattern Recognition Letters, 28(10): 1172-1175.
  • [5] Biel, L., Pettersson, O., Philipson, L. and Wide, P. (2001). ECG Analysis: A New Approach in Human Identification. IEEE Transactions on Instrumentation and Measurement, 50(3): 808–812.
  • [6] Irvine, J.M., Wiederhold, B.K., Gavshon, L.W., et al. (2001). Heart Rate Variability: A New Biometric for Human Identification. The International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, 25-28 June 2001. pp. 1106-1111.
  • [7] Shen, T.W., Tompkins, W.J. and Hu, Y.H. (2002). One-lead ECG for Identity Verification. The 2nd Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Houston, Texas, USA, 23-26 October 2002. pp. 62-63.
  • [8] Israel, S.A., Scruggs, W.T., Worek, W.J. and Irvine, J.M. (2003). Fusing Face and ECG for Personal Identification. The 32nd Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 15-17 October 2003. pp. 226-231.
  • [9] Israel, S.A., Irvine, J.M., Cheng, A., Wiederhold, M.D. and Wiederhold, B.K. (2005). ECG to Identify Individuals. Pattern Recognition, 38(1): 133–142.
  • [10] Shen, T.W. (2005). Biometric Identity Verification Based on Electrocardiogram (ECG). Ph.D. Thesis. University of Wisconsin, Madison, USA.
  • [11] Shen, T.W. (2006). Quartile Discriminant Measurement (QDM) Method for ECG Biometric Feature Selection. International Symposium of Biomedical Engineering, Taiwan, 2006.
  • [12] Chuang-Chien, C., Chou-Min, C. and Chih-Yu, H. (2008). A Novel Personal Identity Verification Approach using a Discrete Wavelet Transform of the ECG Signal. International Conference on Multimedia and Ubiquitous Engineering, Busan, Korea, 24-26 April. pp. 201-206.
  • [13] Irvine, J.M., Israel, S.A., Scruggs, W.T. and Worek, W.J. (2008). EigenPulse: Robust Human Identification from Cardiovascular Function. Pattern Recognition, 41(11): 3427-3435.
  • [14] Fatemian, S.Z. and Hatzinakos, D. (2009). A New ECG Feature Extractor for Biometric Recognition. The 16th International Conference on Digital Signal Processing, Santorini, Greece, 5-7 July 2009. pp.1-6.
  • [15] Li, M. and Narayanan, S. (2010). Robust ECG Biometrics by Fusing Temporal and Cepstral Information. 20th IAPR International Conference on Pattern Recognition, İstanbul, Turkey, 23-26 August 2010. pp. 1326-1329.
  • [16] Sufi, F., Khalil, I. and Habib, I. (2010). Polynomial Distance Measurement for ECG based Biometric Authentication. Security and Communication Networks, 3(4): 303-319.
  • [17] Loong, J.L.C., Subari, K.S., Besar, R. and Abdullah, M.K. (2010). A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems. World Academy of Science Engineering and Technology, 68: 759-764.
  • [18] Ting, C.M. and Salleh, S.H. (2010). ECG based Personal Identification using Extended Kalman Filter. 10th Int. Conf. on Information Sciences Signal Processing and Their Applications, Kuala Lumpur, Malaysia, 10-13 May 2010. pp. 774-777.
  • [19] Sufi, F. and Khalil, I. (2011). Faster Person Identification using Compressed ECG in Time Critical Wireless Telecardiology Applications. Journal of Network and Computer Applications, 34(1): 282-293.
  • [20] Safie, S.I., Soraghan, J.J. and Petropoulakis, L. (2011). Electrocardiogram (ECG) Biometric Authentication using Pulse Active Ratio (PAR). IEEE Transactions on Information Forensics and Security, 6(4): 1315-1322.
  • [21] Gurkan, H., Guz, U. and Yarman, B.S. (2013). A Novel Biometric Authentication Approach using Electrocardiogram Signals. 35th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3-7 July 2013. pp. 4259-4262.
  • [22] Chamatidis, I., Katsika, A. and Spathoulas, G. (2017). Using Deep Learning Neural Networks for ECG based Authentication. International Carnahan Conference on Security Technology, Madrid, Spain, 23-26 October 2017. pp. 1-6.
  • [23] Chan, A.D.C., Hamdy, M.M., Badre, A. and Badee, V. (2008). Wavelet Distance Measure for Person Identification using Electrocardiograms. IEEE Transactions on Instrumentation and Measurement, 57(2): 248-253.
  • [24] Shen, T.W., Tompkins, W.J. and Hu, Y.H. (2011). Implementation of a One-lead ECG Human Identification System on a Normal Population. Journal of Engineering and Computer Innovations, 2(1): 12-21.
  • [25] Chen, C.K., Lin, C.L. and Chiu, Y.M. (2011). Individual Identification Based on Chaotic Electrocardiogram Signals. The 6th IEEE Conference on Industrial Electronics and Applications, Beijing, China, 21-23 June 2011. pp. 1765-1770.
  • [26] Lourenço, A., Silva, H. and Fred, A. (2011). Unveiling the Biometric Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience, 2011: 1-8.
  • [27] Singh, K., Singhvi, A. and Pathangay, V. (2015). Dry Contact Fingertip ECG based Authentication System using Time, Frequency Domain Features and Support Vector Machine. 37th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society, Milan, Italy, 25-29 August 2015. pp. 526-529.
  • [28] Wieclaw, L., Khoma, Y., Falat, P., Sabodashko, D. and Herasymenko, V. (2017). Biometric Identification from Raw ECG Signals using Deep Learning Techniques. The 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application, Bucharest, Romania, 21-23 September 2017. pp. 129-133.
  • [29] Guven. G., Gürkan, H. and Guz, U. (2018). Biometric Identification using Fingertip Electrocardiogram Signals. Signal, Image and Video Processing, 12(5): 933-940.
  • [30] Güven, G. (2016). Fingertip ECG signal based biometric recognition system, Master’s Thesis, FMV ISIK University, Istanbul, Turkey.
  • [31] Şeker, A., Diri, B. And Balık, H.H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3): 47-64.
  • [32] Lei, X., Zhang, Y. and Lu, Z. (2016). Deep Learning Future Representation for Electrocardiogram Identification. IEEE International Conference on Digital Signal Processing, Beijing, China, 16-18 October 2016. pp. 11-14. [26] Lourenço, A., Silva, H. and Fred, A. (2011). Unveiling the Biometric Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience, 2011: 1-8.
  • [33] Zhang, Q., Zhou, D. and Zeng, X. (2017). HeartID: A Multiresolution Convolutional Neural Network for ECG-based Biometric Human Identification in Smart Health Applications. IEEE Access, 5: 11805-11816.
  • [34] Eduardo, A., Aidos, H. and Fred, A. (2017). ECG-based Biometrics using a Deep Autoencoder for Feature Learning: An Empirical Study on Transferability. 6th International Conference on Pattern Recognition Applications and Methods, Porto, Portugal, 24-26 February 2017. pp. 463-470.
  • [35] Salloum, R. and Kuo, C.C.J. (2017). ECG-based Biometrics using Recurrent Neural Networks. IEEE International Conference on Acoustics Speech and Signal Processing, New Orleans, LA, USA, 5-9 March 2017. pp. 2062-2066
  • [36] Zhang, Q., Zhou, D. and Zeng, X. (2017). PulsePrint: Single-arm-ECG Biometric Human Identification using Deep Learning. 8th IEEE 8th Annual Ubiquitous Computing Electronics and Mobile Communication Conference, New York, NY, USA, 19-21 October 2017. pp. 452-456.
  • [37] Luz, E.J.S., Moreira, G.J.P., Oliveira, L.S., Schwartz, W.R. and Menotti, D. (2018). Learning Deep off-the-person Heart Biometrics Representations. IEEE Transactions on Information Forensics and Security, 13(5): 1258-1270.
  • [38] Labati, R.D., Muñoz, E., Piuri, V., Sassi, R. and Scotti, F. (2018). Deep-ECG:Convolutional Neural Networks for ECG Biometric Recognition. Pattern Recognition Letters, doi: 10.1016/j.patrec.2018.03.028 (Article in Press).
  • [39] Abdeldayem, S.S. and Bourlai, T. (2018). ECG-based Human Authentication using High-level Spectro-temporal Signal Features. IEEE International Conference on Big Data, Seattle, WA, USA, 10-13 December 2018. pp. 4984-4993.
  • [40] Goldberger, A.L., Amaral, L.A.N., Glass, L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23): e215–e220.
  • [41] Bousseljot, R., Kreiseler, D. and Schnabel, A. (1995). Nutzung der EKGSignaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik, 40(1): 317.
  • [42] Li, M., Rozgic, V., Thatte, G., Lee, S., Emken, A., Annavaram, M., Mitra, U., Sprujit-Metz, D. and Narayanan, S. (2010). Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18 (4): 369-380.
  • [43] Huang, X., Acero, A. and Hon, H.W. (2001). Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice-Hall, Upper Saddle River, New Jersey, 1st Edition, 980, ISBN: 0-13-022616-5.
  • [44] Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., et al. (2006). The HTK Book (for HTK Version 3.4.1), Cambridge University Engineering Department.
  • [45] Pinto, C.R., Cardoso, J.S. and Lourenço, A. (2018). Evolution, Current Challanges and Future Possibilities in ECG Biometrics. IEEE Access, 6: 34746-34776.

ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Yıl 2019, Cilt 3, Sayı 1, 11 - 22, 24.06.2019

Ayca HANILCI Hakan GÜRKAN

https://doi.org/10.38088/jise.559236

Öz

In this paper, an ECG biometric identification method, based on a two-dimensional convolutional neural network, is introduced for biometric applications. The proposed model includes two-dimensional convolutional neural networks that work parallel and receive two different sets of 2-dimensional features as input. First, ACDCT features and cepstral properties are extracted from overlapping ECG signals. Then, these features are transformed from one-dimensional representation to two-dimensional representation by matrix manipulations. For feature learning purposes, these two-dimensional features are given to the inputs of the proposed model, separately. Finally, score level fusion is applied to identify the user. Our experimental results show that the proposed biometric identification method achieves an accuracy of %88.57 and an identification rate of 90.48% for 42 persons.

Anahtar Kelimeler

Biometric identification; Electrocardiogram Signal; CNN

Kaynakça

  • [1] Jain, A.K., Ross, A. and Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 4–20.
  • [2] Wang, Y., Agrafioti, F., Hatzinakos, D. and Plataniotis, K.N. (2008). Analysis of Human Electrocardiogram for Biometric Recognition. EURASIP Journal on Advances in Signal Processing, 2008: 1-11.
  • [3] Fang, C. and Chan, H.L. (2009). Human Identification by Quantifying Similarity and Dissimilarity in Electrocardiogram Phase Space. Pattern Recognition, 42(9): 1824-1831.
  • [4] Wübbeler, G., Stavridi, M., Kreiseler, D., Bousseljot, R.D. and Elster, C. (2007). Verification of Humans Using Electrocardiogram. Pattern Recognition Letters, 28(10): 1172-1175.
  • [5] Biel, L., Pettersson, O., Philipson, L. and Wide, P. (2001). ECG Analysis: A New Approach in Human Identification. IEEE Transactions on Instrumentation and Measurement, 50(3): 808–812.
  • [6] Irvine, J.M., Wiederhold, B.K., Gavshon, L.W., et al. (2001). Heart Rate Variability: A New Biometric for Human Identification. The International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, 25-28 June 2001. pp. 1106-1111.
  • [7] Shen, T.W., Tompkins, W.J. and Hu, Y.H. (2002). One-lead ECG for Identity Verification. The 2nd Joint Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Houston, Texas, USA, 23-26 October 2002. pp. 62-63.
  • [8] Israel, S.A., Scruggs, W.T., Worek, W.J. and Irvine, J.M. (2003). Fusing Face and ECG for Personal Identification. The 32nd Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 15-17 October 2003. pp. 226-231.
  • [9] Israel, S.A., Irvine, J.M., Cheng, A., Wiederhold, M.D. and Wiederhold, B.K. (2005). ECG to Identify Individuals. Pattern Recognition, 38(1): 133–142.
  • [10] Shen, T.W. (2005). Biometric Identity Verification Based on Electrocardiogram (ECG). Ph.D. Thesis. University of Wisconsin, Madison, USA.
  • [11] Shen, T.W. (2006). Quartile Discriminant Measurement (QDM) Method for ECG Biometric Feature Selection. International Symposium of Biomedical Engineering, Taiwan, 2006.
  • [12] Chuang-Chien, C., Chou-Min, C. and Chih-Yu, H. (2008). A Novel Personal Identity Verification Approach using a Discrete Wavelet Transform of the ECG Signal. International Conference on Multimedia and Ubiquitous Engineering, Busan, Korea, 24-26 April. pp. 201-206.
  • [13] Irvine, J.M., Israel, S.A., Scruggs, W.T. and Worek, W.J. (2008). EigenPulse: Robust Human Identification from Cardiovascular Function. Pattern Recognition, 41(11): 3427-3435.
  • [14] Fatemian, S.Z. and Hatzinakos, D. (2009). A New ECG Feature Extractor for Biometric Recognition. The 16th International Conference on Digital Signal Processing, Santorini, Greece, 5-7 July 2009. pp.1-6.
  • [15] Li, M. and Narayanan, S. (2010). Robust ECG Biometrics by Fusing Temporal and Cepstral Information. 20th IAPR International Conference on Pattern Recognition, İstanbul, Turkey, 23-26 August 2010. pp. 1326-1329.
  • [16] Sufi, F., Khalil, I. and Habib, I. (2010). Polynomial Distance Measurement for ECG based Biometric Authentication. Security and Communication Networks, 3(4): 303-319.
  • [17] Loong, J.L.C., Subari, K.S., Besar, R. and Abdullah, M.K. (2010). A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems. World Academy of Science Engineering and Technology, 68: 759-764.
  • [18] Ting, C.M. and Salleh, S.H. (2010). ECG based Personal Identification using Extended Kalman Filter. 10th Int. Conf. on Information Sciences Signal Processing and Their Applications, Kuala Lumpur, Malaysia, 10-13 May 2010. pp. 774-777.
  • [19] Sufi, F. and Khalil, I. (2011). Faster Person Identification using Compressed ECG in Time Critical Wireless Telecardiology Applications. Journal of Network and Computer Applications, 34(1): 282-293.
  • [20] Safie, S.I., Soraghan, J.J. and Petropoulakis, L. (2011). Electrocardiogram (ECG) Biometric Authentication using Pulse Active Ratio (PAR). IEEE Transactions on Information Forensics and Security, 6(4): 1315-1322.
  • [21] Gurkan, H., Guz, U. and Yarman, B.S. (2013). A Novel Biometric Authentication Approach using Electrocardiogram Signals. 35th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3-7 July 2013. pp. 4259-4262.
  • [22] Chamatidis, I., Katsika, A. and Spathoulas, G. (2017). Using Deep Learning Neural Networks for ECG based Authentication. International Carnahan Conference on Security Technology, Madrid, Spain, 23-26 October 2017. pp. 1-6.
  • [23] Chan, A.D.C., Hamdy, M.M., Badre, A. and Badee, V. (2008). Wavelet Distance Measure for Person Identification using Electrocardiograms. IEEE Transactions on Instrumentation and Measurement, 57(2): 248-253.
  • [24] Shen, T.W., Tompkins, W.J. and Hu, Y.H. (2011). Implementation of a One-lead ECG Human Identification System on a Normal Population. Journal of Engineering and Computer Innovations, 2(1): 12-21.
  • [25] Chen, C.K., Lin, C.L. and Chiu, Y.M. (2011). Individual Identification Based on Chaotic Electrocardiogram Signals. The 6th IEEE Conference on Industrial Electronics and Applications, Beijing, China, 21-23 June 2011. pp. 1765-1770.
  • [26] Lourenço, A., Silva, H. and Fred, A. (2011). Unveiling the Biometric Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience, 2011: 1-8.
  • [27] Singh, K., Singhvi, A. and Pathangay, V. (2015). Dry Contact Fingertip ECG based Authentication System using Time, Frequency Domain Features and Support Vector Machine. 37th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society, Milan, Italy, 25-29 August 2015. pp. 526-529.
  • [28] Wieclaw, L., Khoma, Y., Falat, P., Sabodashko, D. and Herasymenko, V. (2017). Biometric Identification from Raw ECG Signals using Deep Learning Techniques. The 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application, Bucharest, Romania, 21-23 September 2017. pp. 129-133.
  • [29] Guven. G., Gürkan, H. and Guz, U. (2018). Biometric Identification using Fingertip Electrocardiogram Signals. Signal, Image and Video Processing, 12(5): 933-940.
  • [30] Güven, G. (2016). Fingertip ECG signal based biometric recognition system, Master’s Thesis, FMV ISIK University, Istanbul, Turkey.
  • [31] Şeker, A., Diri, B. And Balık, H.H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3): 47-64.
  • [32] Lei, X., Zhang, Y. and Lu, Z. (2016). Deep Learning Future Representation for Electrocardiogram Identification. IEEE International Conference on Digital Signal Processing, Beijing, China, 16-18 October 2016. pp. 11-14. [26] Lourenço, A., Silva, H. and Fred, A. (2011). Unveiling the Biometric Potential of Finger-Based ECG Signals. Computational Intelligence and Neuroscience, 2011: 1-8.
  • [33] Zhang, Q., Zhou, D. and Zeng, X. (2017). HeartID: A Multiresolution Convolutional Neural Network for ECG-based Biometric Human Identification in Smart Health Applications. IEEE Access, 5: 11805-11816.
  • [34] Eduardo, A., Aidos, H. and Fred, A. (2017). ECG-based Biometrics using a Deep Autoencoder for Feature Learning: An Empirical Study on Transferability. 6th International Conference on Pattern Recognition Applications and Methods, Porto, Portugal, 24-26 February 2017. pp. 463-470.
  • [35] Salloum, R. and Kuo, C.C.J. (2017). ECG-based Biometrics using Recurrent Neural Networks. IEEE International Conference on Acoustics Speech and Signal Processing, New Orleans, LA, USA, 5-9 March 2017. pp. 2062-2066
  • [36] Zhang, Q., Zhou, D. and Zeng, X. (2017). PulsePrint: Single-arm-ECG Biometric Human Identification using Deep Learning. 8th IEEE 8th Annual Ubiquitous Computing Electronics and Mobile Communication Conference, New York, NY, USA, 19-21 October 2017. pp. 452-456.
  • [37] Luz, E.J.S., Moreira, G.J.P., Oliveira, L.S., Schwartz, W.R. and Menotti, D. (2018). Learning Deep off-the-person Heart Biometrics Representations. IEEE Transactions on Information Forensics and Security, 13(5): 1258-1270.
  • [38] Labati, R.D., Muñoz, E., Piuri, V., Sassi, R. and Scotti, F. (2018). Deep-ECG:Convolutional Neural Networks for ECG Biometric Recognition. Pattern Recognition Letters, doi: 10.1016/j.patrec.2018.03.028 (Article in Press).
  • [39] Abdeldayem, S.S. and Bourlai, T. (2018). ECG-based Human Authentication using High-level Spectro-temporal Signal Features. IEEE International Conference on Big Data, Seattle, WA, USA, 10-13 December 2018. pp. 4984-4993.
  • [40] Goldberger, A.L., Amaral, L.A.N., Glass, L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23): e215–e220.
  • [41] Bousseljot, R., Kreiseler, D. and Schnabel, A. (1995). Nutzung der EKGSignaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik, 40(1): 317.
  • [42] Li, M., Rozgic, V., Thatte, G., Lee, S., Emken, A., Annavaram, M., Mitra, U., Sprujit-Metz, D. and Narayanan, S. (2010). Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18 (4): 369-380.
  • [43] Huang, X., Acero, A. and Hon, H.W. (2001). Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice-Hall, Upper Saddle River, New Jersey, 1st Edition, 980, ISBN: 0-13-022616-5.
  • [44] Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., et al. (2006). The HTK Book (for HTK Version 3.4.1), Cambridge University Engineering Department.
  • [45] Pinto, C.R., Cardoso, J.S. and Lourenço, A. (2018). Evolution, Current Challanges and Future Possibilities in ECG Biometrics. IEEE Access, 6: 34746-34776.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Ayca HANILCI Bu kişi benim
Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Electrical Electronics Engineering
0000-0002-1314-3743
Türkiye

Hakan GÜRKAN (Sorumlu Yazar)
Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Electrical Electronics Engineering
0000-0002-7008-4778
Türkiye

Destekleyen Kurum Bursa Technical University
Proje Numarası 181N14
Yayımlanma Tarihi 24 Haziran 2019
Yayınlandığı Sayı Yıl 2019, Cilt 3, Sayı 1

Kaynak Göster

Bibtex @araştırma makalesi { jise559236, journal = {Journal of Innovative Science and Engineering}, issn = {}, eissn = {2602-4217}, address = {ursa Technical University, Mimar Sinan Campus, Mimar Sinan Mah. Mimar Sinan Blv. Eflak Cad. No:177 16310 Yıldırım, Bursa / Turkey}, publisher = {Bursa Teknik Üniversitesi}, year = {2019}, volume = {3}, pages = {11 - 22}, doi = {10.38088/jise.559236}, title = {ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks}, key = {cite}, author = {Hanılcı, Ayca and Gürkan, Hakan} }
APA Hanılcı, A. & Gürkan, H. (2019). ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks . Journal of Innovative Science and Engineering , 3 (1) , 11-22 . DOI: 10.38088/jise.559236
MLA Hanılcı, A. , Gürkan, H. "ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks" . Journal of Innovative Science and Engineering 3 (2019 ): 11-22 <http://jise.btu.edu.tr/tr/pub/issue/44666/559236>
Chicago Hanılcı, A. , Gürkan, H. "ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks". Journal of Innovative Science and Engineering 3 (2019 ): 11-22
RIS TY - JOUR T1 - ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks AU - Ayca Hanılcı , Hakan Gürkan Y1 - 2019 PY - 2019 N1 - doi: 10.38088/jise.559236 DO - 10.38088/jise.559236 T2 - Journal of Innovative Science and Engineering JF - Journal JO - JOR SP - 11 EP - 22 VL - 3 IS - 1 SN - -2602-4217 M3 - doi: 10.38088/jise.559236 UR - https://doi.org/10.38088/jise.559236 Y2 - 2019 ER -
EndNote %0 Journal of Innovative Science and Engineering ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks %A Ayca Hanılcı , Hakan Gürkan %T ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks %D 2019 %J Journal of Innovative Science and Engineering %P -2602-4217 %V 3 %N 1 %R doi: 10.38088/jise.559236 %U 10.38088/jise.559236
ISNAD Hanılcı, Ayca , Gürkan, Hakan . "ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks". Journal of Innovative Science and Engineering 3 / 1 (Haziran 2019): 11-22 . https://doi.org/10.38088/jise.559236
AMA Hanılcı A. , Gürkan H. ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. JISE. 2019; 3(1): 11-22.
Vancouver Hanılcı A. , Gürkan H. ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. Journal of Innovative Science and Engineering. 2019; 3(1): 11-22.
IEEE A. Hanılcı ve H. Gürkan , "ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks", Journal of Innovative Science and Engineering, c. 3, sayı. 1, ss. 11-22, Haz. 2019, doi:10.38088/jise.559236
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