Evrişimsel sinir ağı ve QRS imgeleri kullanarak EKG tabanlı biyometrik tanıma yöntemi

Abstract

Medikal uygulamalarda yaygın olarak kullanılan elektrokardiyogram (EKG) işaretleri, aldatma saldırılarına karşı güçlü kılan yaşam işareti olma özelliği sayesinde, biyometrik uygulamalar için bir biyometrik büyüklük olarak kullanılmaya başlanmıştır. Bilgisayar sistemlerinin hesaplama güçlerinin artmasına bağlı olarak kişi tanıma ve sınıflandırma doğruluğunu arttırmak amacıyla son yıllarda EKG biyometrik tanıma için birkaç evrişimsel sinir ağı (ESA) tabanlı yöntem sunulmuştur. Bu çalışmada, QRS (QRS dalgası) imgeleri ve 2 boyutlu ESA yapısı kullanılarak EKG işaretleri tabanlı bir biyometrik tanıma yöntemi önerilmiştir. Önerilen yöntemde, ilk olarak EKG işaretleri gürültü temizleme ve QRS belirleme algoritmalarından geçirilerek QRS bölütlerine ayrılmıştır. Elde edilen bu bölütler R noktalarına göre hizalandıktan sonra 256x256 büyüklüğünde QRS imgesi olarak adlandırılan 2 boyutlu EKG işaretlerine dönüştürülmüştür. Son olarak elde edilen bu QRS imgelerinin giriş olarak uygulandığı 2 boyutlu bir ESA modeli geliştirilerek biyometrik tanıma gerçekleştirilmiştir. Önerilen yöntemin başarımı diğer ESA tabanlı EKG biyometrik tanıma yöntemleri ile karşılaştırmalı olarak incelenmiştir. Önerilen yöntem 46 kişiden oluşan bir EKG veri kümesi üzerinde %98.08 doğruluk oranı ve %99.275 kişi tanıma oranı sağlamıştır.

Keywords

• Elektrokardiyogram (EKG),Biyometrik,Evrişimsel sinir ağı (ESA),QRS imgeleri

ECG based biometric identification method using QRS images and convolutional neural network

Abstract

Electrocardiogram (ECG) signals, which are commonly used in medical applications, have been started to use as a biometric modality for biometric applications thanks to its liveness indicator that makes it stronger against spoofing attacks. Due to improving computational power of computer systems, several convolutional neural network (CNN) based methods have been recently proposed for ECG biometric identification in order to increase identification performance and classification accuracy. In this work, we proposed an ECG based biometric identification method using QRS (QRS wave) images and two-dimensional CNN. In the proposed method, ECG signals were segmented by applying noise removing and QRS detection algorithms. After these segments were aligned according to their R-points, they were transformed to two-dimensional ECG signals called QRS images of size 256x256. Finally, biometric identification task was achieved by developing a CNN based ECG biometric identification method which uses the QRS images as an input. The identification performance of the proposed method was compared to other CNN based ECG biometric identification methods proposed in the literature. The experimental results show that the proposed method provides an accuracy of 98.08% and an identification rate of 99.275% for a public ECG database of 46 persons.

Keywords

• Electrocardiogram (ECG),Biometrics,Convolutional neural network (CNN),QRS images

References

1. Jain AK, Ross A, Prabhakar S. “An introduction to biometric recognition”. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20, 2004.
2. Wang Y, Agrafioti F, Hatzinakos D, Plataniotis KN. “Analysis of human electrocardiogram for biometric recognition”. EURASIP Journal on Advances in Signal Processing, 2008, 1-11, 2008.
3. Fang C, Chan HL. “Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space”. Pattern Recognition, 42(9), 1824-1831, 2009.
4. Wübbeler G, Stavridi M, Kreiseler D, Bousseljot RD, Elster C. “Verification of humans using electrocardiogram”. Pattern Recognition Letters, 28(10), 1172-1175, 2007.
5. Biel L, Pettersson O, Philipson L, Wide P. “ECG analysis: A new approach in human identification”. IEEE Transactions on Instrumentation and Measurement, 50(3), 808-812, 2001.
6. Irvine JM, Wiederhold BK, Gavshon LW, et al. “Heart rate variability: A new biometric for human identification”. The International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, 25-28 June 2001.
7. Shen TW, Tompkins WJ, Hu YH. “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.
8. Israel SA, Scruggs WT, Worek WJ, Irvine JM. “Fusing face and ECG for personal identification”. The 32nd Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 15-17 October 2003.

9. Israel SA, Irvine JM, Cheng A, Wiederhold MD, Wiederhold BK. “ECG to identify individuals”. Pattern Recognition, 38(1) 1, 133-142, 2005.
10. Shen TW. Biometric Identity Verification Based on Electrocardiogram (ECG). Ph.D. Thesis, University of Wisconsin, Madison, USA, 2005.
11. Shen TW. “Quartile discriminant measurement (QDM) method for ECG biometric feature selection”. International Symposium of Biomedical Engineering, Taiwan, 14-16 December, 2006.
12. Chuang-Chien C, Chou-Min C, Chih-Yu H. “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, 2008.
13. Irvine JM, Israel SA, Scruggs WT, Worek WJ. “EigenPulse: robust human identification from cardiovascular function”. Pattern Recognition, 41(11), 3427-3435, 2008.
14. Fatemian SZ, Hatzinakos D. “A new ECG feature extractor for biometric recognition”. The 16th International Conference on Digital Signal Processing, Santorini, Greece, 5-7 July 2009.
15. Li M, Narayanan S. “Robust ECG biometrics by fusing temporal and cepstral information”. The 20th IAPR International Conference on Pattern Recognition, İstanbul, Turkey, 23-26 August 2010.
16. Sufi F, Khalil I, Habib I. “Polynomial distance measurement for ECG based biometric authentication”. Security and Communication Networks, 3(4), 303-319, 2010.
17. Loong JLC, Subari KS, Besar R, Abdullah MK. “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, 2010.
18. Ting CM, Salleh SH. “ECG based personal identification using extended kalman filter”. 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA), Kuala Lumpur, Malaysia, 10-13 May 2010.
19. Sufi F, Khalil I. “Faster person identification using compressed ECG in time critical wireless telecardiology applications”. Journal of Network and Computer Applications, 34(1), 282-293, 2011.
20. Safie SI, Soraghan JJ, Petropoulakis L. “Electrocardiogram (ECG) biometric authentication using pulse active ratio (PAR)”. IEEE Transactions on Information Forensics and Security, 6(4), 1315-1322, 2011.
21. Gurkan H, Guz U, Yarman BS. “A novel biometric authentication approach using electrocardiogram signals”. The 35th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3-7 July 2013.
22. Chamatidis I, Katsika A, Spathoulas G. “Using deep learning neural networks for ECG based authentication”. International Carnahan Conference on Security Technology, Madrid, Spain, 23-26 October 2017.
23. Chan ADC, Hamdy MM, Badre A, Badee V. “Wavelet distance measure for person identification using electrocardiograms”. IEEE Transactions on Instrumentation and Measurement, 57(2), 248-253, 2008.
24. Shen TW, Tompkins WJ, Hu YH. “Implementation of a one-lead ECG human identification system on a normal population”. Journal of Engineering and Computer Innovations, 2(1), 12-21, 2011.
25. Chen CK, Lin CL, Chiu YM. “Individual identification based on chaotic electrocardiogram signals”. The 6th IEEE Conference on Industrial Electronics and Applications, Beijing, China, 21-23 June 2011.
26. Lourenço A, Silva H, Fred A. “Unveiling the biometric potential of finger-based ECG signals”. Computational Intelligence and Neuroscience, 2011, 1-8, 2011.
27. Singh K, Singhvi A, Pathangay V. “Dry contact fingertip ECG based authentication system using time, frequency domain features and support vector machine”. The 37th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society, Milan, Italy, 25-29 August 2015.
28. Wieclaw L, Khoma Y, Falat P, Sabodashko D, Herasymenko V. “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.
29. Guven G, Gürkan H, Guz U. “Biometric identification using fingertip electrocardiogram signals”. Signal, Image and Video Processing, 12(5), 933-940, 2018.
30. Şeker A, Diri B, Balık HH. “Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme”. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64, 2017.
31. Pinto CR, Cardoso JS, Lourenço A. “Evolution, current challanges and future possibilities in ECG biometrics”. IEEE Access, 6, 34746-34776, 2018.
32. Lei X, Zhang Y, Lu Z. “Deep learning future representation for electrocardiogram identification”. IEEE International Conference on Digital Signal Processing, Beijing, China, 16-18 October 2016.
33. Zhang Q, Zhou D, Zeng X. “HeartID: A multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications”. IEEE Access, 5, 11805-11816, 2017.
34. Eduardo A, Aidos H, Fred A. “ECG-based biometrics using a deep autoencoder for feature learning: An empirical study on transferability”. The 6th International Conference on Pattern Recognition Applications and Methods, Porto, Portugal, 24-26 February 2017.
35. Salloum R, Kuo CCJ. “ECG-based biometrics using recurrent neural networks”. IEEE International Conference on Acoustics Speech and Signal Processing, New Orleans, LA, USA, 5-9 March 2017.
36. Zhang Q, Zhou D, Zeng X. “PulsePrint: Single-arm-ECG biometric human identification using deep learning”. The 8th IEEE 8th Annual Ubiquitous Computing Electronics and Mobile Communication Conference, New York, NY, USA, 19-21 October 2017.
37. Luz EJS, Moreira GJP, Oliveira LS, Schwartz WR, Menotti D. “Learning deep off-the-person heart biometrics representations”. IEEE Transactions on Information Forensics and Security, 13(5), 1258-1270, 2018.
38. Labati RD, Muñoz E, Piuri V, Sassi R, Scotti F. “Deep-ECG: Convolutional neural networks for ECG biometric recognition”. Pattern Recognition Letters, 126(1), 78-85, 2019.
39. Abdeldayem SS, Bourlai T. “ECG-based human authentication using high-level spectro-temporal signal features”. IEEE International Conference on Big Data, Seattle, WA, USA, 10-13 December 2018.
40. Hammad M, Wang K. “Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network”. Computer & Security, Elsevier, 81, 107-122, 2019.
41. Pan J, Tompkins WJ. “A real-time QRS detection algorithm”. IEEE Transactions on Biomedical Engineering, 32(3), 230-236, 1985.
42. Sze V, Chen Y, Yang T, Emer JS. “Efficient processing of deep neural networks: A tutorial and survey". Proceedings of the IEEE, 105(12), 2295-2329, 2017.
43. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. “Dropout: A simple way to prevent neural networks from overfitting”. Journal of Machine Learning Research, 15, 1929-1958, 2014.
44. Moody GB, Mark RG. “The impact of the MIT-BIH arrhythmia database”. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50, 2001.
45. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals”. Circulation, 101(23), 215-220, 2000.
46. Physionet. “MIT-BIH Arrhythmia Database”. https://physionet.org/physiobank/database/mitdb/ (01.10.2018).
47. Chollet F. Deep Learning with Python. New York, USA, Manning Publication, 2018.
48. Chollet F, et al. “Keras”. https://keras.io (01.09.2018).
49. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, et al. “TensorFlow:Large-scale machine learning on heterogeneous systems”. https://www.tensorflow.org/ (01.09.2018).