An Electrocardiogram Signal Preprocessing Strategy in LSTM Algorithm for Biometric Recognition
Fenny Winda Rahayu(1), Mohammad Reza Faisal(2*), Dodon Turianto Nugrahadi(3), Radityo Adi Nugroho(4), Muliadi Muliadi(5), Sri Redjeki(6)
(1) Lambung Mangkurat University
(2) Lambung Mangkurat University
(3) Lambung Mangkurat University
(4) Lambung Mangkurat University
(5) Lambung Mangkurat University
(6) Indonesia Digital Technology University
(*) Corresponding Author
Abstract
Keywords
Full Text:
PDFReferences
M. Gomez-Barrero et al., “Biometrics in the Era of COVID-19: Challenges and Opportunities,” IEEE Trans. Technol. Soc., vol. 3, no. 4, pp. 307–322, 2022, doi: 10.1109/tts.2022.3203571.
R. Ryu, S. Yeom, D. Herbert, and J. Dermoudy, “The design and evaluation of adaptive biometric authentication systems: Current status, challenges and future direction,” ICT Express, vol. 9, no. 6, pp. 1183–1197, 2023, doi: 10.1016/j.icte.2023.04.003.
A. Harasimiuk and A. Czyżewski, “Usability study of various biometric techniques in bank branches Usability study of various biometric techniques in bank branches,” Procedia Comput. Sci., vol. 225, pp. 2126–2135, 2023, doi: 10.1016/j.procs.2023.10.203.
R. Donida Labati, V. Piuri, F. Rundo, and F. Scotti, “MultiCardioNet: Interoperability between ECG and PPG biometrics,” Pattern Recognit. Lett., vol. 175, no. August, pp. 1–7, 2023, doi: 10.1016/j.patrec.2023.09.009.
J. Cheng, Q. Zou, and Y. Zhao, “ECG signal classification based on deep CNN and BiLSTM,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, pp. 1–12, 2021, doi: 10.1186/s12911-021-01736-y.
A. Ahmad, X. Xiao, H. Mo, and D. Dong, “Tuning data preprocessing techniques for improved wind speed prediction,” Energy Reports, vol. 11, no. May 2023, pp. 287–303, 2023, doi: 10.1016/j.egyr.2023.11.056.
B. H. Kim and J. Y. Pyun, “ECG identification for personal authentication using LSTM-based deep recurrent neural networks,” Sensors (Switzerland), vol. 20, no. 11, pp. 1–17, 2020, doi: 10.3390/s20113069.
S. Song et al., “Research on a working face gas concentration prediction model based on LASSO-RNN time series data,” Heliyon, vol. 9, no. 4, 2023, doi: 10.1016/j.heliyon.2023.e14864.
M. Muñoz-Organero, P. Callejo, and M. Á. Hombrados-Herrera, “A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid,” Heliyon, vol. 9, no. 6, p. e17625, 2023, doi: 10.1016/j.heliyon.2023.e17625.
Q. Kang, E. J. Chen, Z.-C. Li, H.-B. Luo, and Y. Liu, “Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling,” Undergr. Sp., vol. 13, pp. 335–350, 2023, doi: 10.1016/j.undsp.2023.05.006.
A. Malali, S. Hiriyannaiah, G. M. Siddesh, K. G. Srinivasa, and N. T. Sanjay, “Supervised ECG wave segmentation using convolutional LSTM,” ICT Express, vol. 6, no. 3, pp. 166–169, 2020, doi: 10.1016/j.icte.2020.04.004.
J. A. Lee and K. C. Kwak, “Personal Identification Using an Ensemble Approach of 1D-LSTM and 2D-CNN with Electrocardiogram Signals,” Appl. Sci., vol. 12, no. 5, 2022, doi: 10.3390/app12052692.
A. A. Aleidan et al., “Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals,” Appl. Sci., vol. 13, no. 16, 2023, doi: 10.3390/app13169454.
S. Chauhan, L. Vig, and S. Ahmad, “ECG anomaly class identification using LSTM and error profile modeling,” Comput. Biol. Med., vol. 109, pp. 14–21, 2019, doi: 10.1016/j.compbiomed.2019.04.009.
F. Li, Z. Kalinic, F. Mu, and E. Higueras-castillo, “Information & Management Biometric m-payment systems : A multi-analytical approach to determining use intention,” vol. 61, no. December 2023, 2024, doi: 10.1016/j.im.2023.103907.
P. Delgado-Santos, R. Tolosana, R. Guest, F. Deravi, and R. Vera-Rodriguez, “Exploring transformers for behavioural biometrics: A case study in gait recognition,” Pattern Recognit., vol. 143, p. 109798, 2023, doi: 10.1016/j.patcog.2023.109798.
Z. Qu, W. Shi, and P. Tiwari, “Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation,” Comput. Biol. Med., vol. 166, no. July, p. 107549, 2023, doi: 10.1016/j.compbiomed.2023.107549.
R. Nishikimi, M. Nakano, K. Kashino, and S. Tsukada, “Variational Autoencoder-Based Neural Electrocardiogram Synthesis Trained by FEM-Based Heart Simulator,” J. Pre-proof, 2023, doi: 10.1016/j.cvdhj.2023.12.002.
S. M. I. Niroshana, S. Kuroda, K. Tanaka, and W. Chen, “Beat-wise segmentation of electrocardiogram using adaptive windowing and deep neural network,” Sci. Rep., vol. 13, no. 1, pp. 1–19, 2023, doi: 10.1038/s41598-023-37773-y.
A. R. Pérez-Riera, L. C. De Abreu, R. Barbosa-Barros, K. C. Nikus, and A. Baranchuk, “R-Peak Time: An Electrocardiographic Parameter with Multiple Clinical Applications,” Natl. Libr. Med., vol. 21, no. 1, pp. 10–19, 2016, doi: 10.1111/anec.12323.
S. Hamza and Y. Ben Ayed, “SVM for human identification using the ECG signal,” Procedia Comput. Sci., vol. 176, pp. 430–439, 2020, doi: 10.1016/j.procs.2020.08.044.
V. Matoušek, “Application of LSTM Neural Networks in Language Modelling,” Univ. West Bohemia, Fac. Appl. Sci. Dep. Cybern. Univerzitn´ı 22, Plzen, Czech rep, no. June 2018, 2013, doi: 10.1007/978-3-642-40585-3.
Z. Sun, R. Machlev, Q. Wang, J. Belikov, Y. Levron, and D. Baimel, “A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers,” Energy AI, vol. 14, no. January, p. 100274, 2023, doi: 10.1016/j.egyai.2023.100274.
G. S. . Murthy, S. R. Allu, B. Andhavarapu, M. Bgadi, and M. Belusonti, “Text based Sentiment Analysis using Long Short Term Memory (LSTM),” Int. J. Eng. Res. Technol., vol. 9, no. 05, pp. 299–303, 2020.
DOI: https://doi.org/10.22146/ijccs.93895
Article Metrics
Abstract views : 2137 | views : 906Refbacks
- There are currently no refbacks.
Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1