Electrocardiogram Biometrics Recognition Menggunakan Artificial Neural Network

William Sim Jayapranata(1*), Rolly Intan(2), Liliana Liliana(3),


(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author

Abstract


Research on biometrics recognition has become popular in the last two decades. Electrocardiogram signal is one among many data that can be used in biometrics recognition purposes. It is unique for each individual, easy to obtain, and hard to forge made electrocardiogram well suited for biometrics recognition. In this research, an identifier will be made using the electrocardiogram signal of each individual.

In this research, non-fiducial approach on MIT-BIH Arrhythmia Database from physionet with Artificial Neural Network as classifier was used. Non-sequential classifier offers lower computational complexity compared to sequential classifiers. Non-fiducial approach does not require feature extraction but a method of truncating the signal to each heartbeat is still required. Artificial Neural Network method uses neuron on each layer to classify digitalized electrocardiogram signal data.

Experiment result using our method achieved 98.886% accuracy using MIT-BIH Arrhythmia Database. This research demonstrates Artificial Neural Network method capability as non-sequential classifier to identify electrocardiogram with non-fiducial approach.


Keywords


Machine Learning; Artificial Neural Network; Non-fiducial; Non-sequential; Electrocardiogram

Full Text:

PDF

References


Abraham, A. 2005. Handbook of Measuring System Design. John Wiley & Sons, Ltd.

Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A. and Tan, R.S. 2017. A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine. (2017). DOI:https://doi.org/10.1016/j.compbiomed.2017.08.022.

Alexander, R. 2017. Using the Analytical Hierarchy Process Model in the Prioritization of Information Assurance Defense In-Depth Measures?—A Quantitative Study. Journal of Information Security. (2017). DOI:https://doi.org/10.4236/jis.2017.83011.

Canham, J. 2018. Biometrics: leap of faith or fact of life? Biometric Technology Today. (2018). DOI:https://doi.org/10.1016/S0969-4765(18)30024-9.

Donida Labati, R., Muñoz, E., Piuri, V., Sassi, R. and Scotti, F. 2019. Deep-ECG: Convolutional Neural Networks for ECG biometric recognition. Pattern Recognition Letters. (2019). DOI:https://doi.org/10.1016/j.patrec.2018.03.028.

Drew, B.J., Califf, R.M., Funk, M., Kaufman, E.S., Krucoff, M.W., Laks, M.M., Macfarlane, P.W., Sommargren, C., Swiryn, S. and Van Hare, G.F. 2005. Aha scientific statement: Practice standards for electrocardiographic monitoring in hospital settings: An american heart association scientific statement from the councils on cardiovascular nursing, clinical cardiology, and cardiovascular disease in the y. Journal of Cardiovascular Nursing. (2005). DOI:https://doi.org/10.1097/00005082-200503000-00003.

Falah, F., Rahmati, O., Rostami, M., Ahmadisharaf, E., Daliakopoulos, I.N. and Pourghasemi, H.R. 2019. Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas. Spatial Modeling in GIS and R for Earth and Environmental Sciences.

Fratini, A., Sansone, M., Bifulco, P. and Cesarelli, M. 2015. Individual identification via electrocardiogram analysis. BioMedical Engineering Online. 14, 1 (2015), 1–23. DOI:https://doi.org/10.1186/s12938-015-0072-y.

Jambukia, S.H., Dabhi, V.K. and Prajapati, H.B. 2015. Classification of ECG signals using machine learning techniques: A survey. Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015. March (2015), 714–721. DOI:https://doi.org/10.1109/ICACEA.2015.7164783.

Jeon, T., Kim, B., Jeon, M. and Lee, B.G. 2014. Implementation of a portable device for real-time ECG signal analysis. BioMedical Engineering Online. (2014). DOI:https://doi.org/10.1186/1475-925X-13-160.

Karimian, N., Guo, Z., Tehranipoor, M. and Forte, D. 2017. Highly Reliable Key Generation From Electrocardiogram (ECG). IEEE Transactions on Biomedical Engineering. (2017). DOI:https://doi.org/10.1109/TBME.2016.2607020.

Kim, H., Kim, S., Van Helleputte, N., Artes, A., Konijnenburg, M., Huisken, J., Van Hoof, C. and Yazicioglu, R.F. 2014. A configurable and low-power mixed signal SoC for portable ECG monitoring applications. IEEE Transactions on Biomedical Circuits and Systems. (2014). DOI:https://doi.org/10.1109/TBCAS.2013.2260159.

Li, M. and Narayanan, S. 2010. Robust ECG biometrics by fusing temporal and cepstral information. Proceedings - International Conference on Pattern Recognition (2010).

Lin, C.H., Chen, J.L. and Tseng, C.Y. 2011. Optical sensor measurement and biometric-based fractal pattern classifier for fingerprint recognition. Expert Systems with Applications. (2011). DOI:https://doi.org/10.1016/j.eswa.2010.09.143.

Liu, B., Shi, G. and Zhao, W. 2017. The design of portable ECG health monitoring system. Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017. (2017), 2223–2226. DOI:https://doi.org/10.1109/CCDC.2017.7978884.

Lynn, H.M., Pan, S.B. and Kim, P. 2019. A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks. IEEE Access. 7, (2019), 145395–145405. DOI:https://doi.org/10.1109/ACCESS.2019.2939947.

Nedjah, N., Wyant, R.S., Mourelle, L.M. and Gupta, B.B. 2017. Efficient yet robust biometric iris matching on smart cards for data high security and privacy. Future Generation Computer Systems. (2017). DOI:https://doi.org/10.1016/j.future.2017.05.008.

Nelwan,S P, Meij,S H, van Dam,T B and Kors,J A 2001. Correction of ECG variations caused by body position changes and electrode placement during ST-T monitoring. Journal of Electrocardiology. (2001).

Paiva, J.S., Dias, D. and Cunha, J.P.S. 2017. Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology. PLoS ONE. (2017). DOI:https://doi.org/10.1371/journal.pone.0180942.

Park, Y.S. and Lek, S. 2016. Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling. Developments in Environmental Modelling.

Patro, K.K. and Kumar, P.R. 2017. Machine learning classification approaches for biometric recognition system using ECG signals. Journal of Engineering Science and Technology Review. 10, 6 (2017), 1–8. DOI:https://doi.org/10.25103/jestr.106.01.

Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y. and Ettaouil, M. 2016. Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence. (2016). DOI:https://doi.org/10.9781/ijimai.2016.415.

Rezgui, D. and Lachiri, Z. 2016. ECG biometric recognition using SVM-based approach. IEEJ Transactions on Electrical and Electronic Engineering. 11, (2016), S94–S100. DOI:https://doi.org/10.1002/tee.22241.

Ribeiro Pinto, J., Cardoso, J.S. and Lourenco, A. 2018. Evolution, current challenges, and future possibilities in ECG Biometrics. IEEE Access.

Samarin, N. and Sannella, D. 2019. A key to your heart: Biometric authentication based on ECG signals. arXiv.

Singh, Y.N. and Singh, S.K. 2012. Evaluation of Electrocardiogram for Biometric Authentication. Journal of Information Security. (2012). DOI:https://doi.org/10.4236/jis.2012.31005.

Tang, X. and Shu, L. 2014. Classification of electrocardiogram signals with RS and quantum networks neural. International Journal of Multimedia and Ubiquitous Engineering. 9, 2 (2014), 363–372. DOI:https://doi.org/10.14257/ijmue.2014.9.2.37.

Tuerxunwaili, Nor, R.M., Rahman, A.W.B.A., Sidek, K.A. and Ibrahim, A.A. 2016. Electrocardiogram identification: Use a simple set of features in QRS complex to identify individuals. Advances in Intelligent Systems and Computing (2016).

Wagner, G.S., Macfarlane, P., Wellens, H., Josephson, M., Gorgels, A., Mirvis, D.M., Pahlm, O., Surawicz, B., Kligfield, P., Childers, R. and Gettes, L.S. 2009. AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram. Journal of the American College of Cardiology. (2009). DOI:https://doi.org/10.1016/j.jacc.2008.12.016.

Wieclaw, L., Khoma, Y., Falat, P., Sabodashko, D. and Herasymenko, V. 2017. Biometrie identification from raw ECG signal using deep learning techniques. Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017 (2017).

Yang, Z., Zhou, Q., Lei, L., Zheng, K. and Xiang, W. 2016. An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare. Journal of Medical Systems. (2016). DOI:https://doi.org/10.1007/s10916-016-0644-9.

Yu, J., Sun, K., Gao, F. and Zhu, S. 2018. Face biometric quality assessment via light CNN. Pattern Recognition Letters. (2018). DOI:https://doi.org/10.1016/j.patrec.2017.07.015.

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. (2017). DOI:https://doi.org/10.1109/ACCESS.2017.2707460.

Zhang, X., Zhang, Y., Zhang, L., Wang, H. and Tang, J. 2019. Ballistocardiogram Based Person Identification and Authentication Using Recurrent Neural Networks. Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 (2019).

Zhao, Z., Zhang, Y., Deng, Y. and Zhang, X. 2018. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation. Computers in Biology and Medicine. (2018). DOI:https://doi.org/10.1016/j.compbiomed.2018.09.027.

Zihlmann, M., Perekrestenko, D. and Tschannen, M. 2017. Convolutional recurrent neural networks for electrocardiogram classification. Computing in Cardiology (2017).

2009. Encyclopedia of Biometrics.


Refbacks

  • There are currently no refbacks.


Jurnal telah terindeks oleh :