An Analytical Study on the Most Important Methods and Data Sets Used to Identify People Through ECG: Review

Authors

  • Abdullah Najm Abed Alzaki Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq
  • Mohammed Al-Tamimi Computer Science Department - College of Science - University of Baghdad

DOI:

https://doi.org/10.37385/jaets.v5i2.3992

Keywords:

Electrocardiogram, Biometric, Medical, Methods, Dataset, Intelligence

Abstract

The electrocardiogram is a topic of great importance from a medical and biometric perspective, especially recently, as researchers have begun to search for new biometric methods other than the palm print, fingerprint, or iris as alternative systems. Researchers discovered that ECG has unique features that are not common among humans, making it a good topic for researchers in biometric systems for identifying people. In this research paper, the goal is to shed light on the most important basic concepts that are related to ECG in terms of the methods used by researchers and in terms of the most critical data sets used by researchers, and also to shed light on some previous studies that achieved a high rate of citations, and also to shed light on the most important basic concepts that make Its features are unique and intelligence methods can be used effectively.

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References

Abd-Alzhra, A. S., & Al-Tamimi, M. S. H. (2022). Image Compression Using Deep Learning: Methods and Techniques. Iraqi Journal of Science, 1299–1312. doi.org/10.24996/ijs.2022.63.3.34.

Abed, R. M., Abdulmalek, H. W., Yaaqoob, L. A., Altaee, M. F., & Kamona, Z. K. (2023). Genetic Polymorphism of TLR5 and TLP6 in Iraqi Patients with Heart Failure Disease. Iraqi Journal of Science, 1662–1674. doi.org/10.24996/ijs.2023.64.4.9

Alduwaile, D. A., & Islam, M. S. (2021). Using convolutional neural network and a single heartbeat for ecg biometric recognition. Entropy, 23(6). https://doi.org/10.3390/e23060733

AL-Jibory, Farah K, M. S. H. A.-T. (2021). Hybrid System for Plagiarism Detection on A Scientific Paper. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 5707–5719. https://doi.org/10.17762/turcomat.v12i13.9822.

Al-Juboori, R. A. L. (2017). Contrast enhancement of the mammographic image using retinex with CLAHE methods. Iraqi Journal of Science, 327–336.

Al-Khafaji, R. S. S., & Al-Tamimi, M. S. H. (2022). Vein Biometric Recognition Methods and Systems: A Review. Sudebno-Meditsinskaya Ekspertiza, 16(1), 36–46. https://doi.org/10.12913/22998624/144495

AlMusallam, M., & Soudani, A. (2021). Low energy ECG features extraction for atrial fibrillation detection in wearable sensors. SENSORNETS 2021 - Proceedings of the 10th International Conference on Sensor Networks, Sensornets, 69–77. https://doi.org/10.5220/0010245200690077

Al-Tamimi, M. S. (2019). H. A survey on the vein biometric recognition systems: Trends and challenges. Journal of Theoretical and Applied Information Technology, 97(2), 551–568.

Al-Tamimi, M. S. H. (2019). Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme. International Journal of Electrical and Computer Engineering (IJECE), 9(5), 4382–4395. https://doi.org/10.11591/ijece.v9i5.pp4382-4395

Aminorroaya, A., Dhingra, L. S., Pedroso Camargos, A., Vasisht Shankar, S., Khunte, A., Sangha, V., ... & Khera, R. (2024). Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD). medRxiv, 2024-03. https://doi.org/10.1101/2024.03.18.24304477v1

Ansari, S., Farzaneh, N., Duda, M., Horan, K., Andersson, H. B., Goldberger, Z. D., Nallamothu, B. K., & Najarian, K. (2017). A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Reviews in Biomedical Engineering, 10, 264–298. https://doi.org/10.1109/RBME.2017.2757953

Attia, Z. I., Lerman, G., & Friedman, P. A. (2021). Deep neural networks learn by using human-selected electrocardiogram features and novel features. European Heart Journal - Digital Health, 2(3), 446–455. https://doi.org/10.1093/ehjdh/ztab060

Bassiouni, M. M., El-Dahshan, E. S. A., Khalefa, W., & Salem, A. M. (2018). Intelligent hybrid approaches for human ECG signals identification. Signal, Image and Video Processing, 12(5), 941–949. https://doi.org/10.1007/s11760-018-1237-5

Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222–2239. https://doi.org/10.1093/aje/kwz189

Boostani, R., Sabeti, M., Omranian, S., & Kouchaki, S. (2019). ECG-Based Personal Identification Using Empirical Mode Decomposition and Hilbert Transform. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 43(1), 67–75. https://doi.org/10.1007/s40998-018-0055-7

Charlton, P. H., Birrenkott, D. A., Bonnici, T., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Tarassenko, L., Watkinson, P. J., Beale, R., & Clifton, D. A. (2018). Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review. IEEE Reviews in Biomedical Engineering, 11, 2–20. https://doi.org/10.1109/RBME.2017.2763681

Cluitmans, M., Brooks, D. H., MacLeod, R., Dössel, O., Guillem, M. S., Van Dam, P. M., Svehlikova, J., He, B., Sapp, J., Wang, L., & Bear, L. (2018). Validation and opportunities of electrocardiographic imaging: From technical achievements to clinical applications. Frontiers in Physiology, 9(SEP), 1–19. https://doi.org/10.3389/fphys.2018.01305

Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7(3), 1174–1179. www.ijcsit.com

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

Gu, F., Chung, M. H., Chignell, M., Valaee, S., Zhou, B., & Liu, X. (2022). A Survey on Deep Learning for Human Activity Recognition. ACM Computing Surveys, 54(8). https://doi.org/10.1145/3472290

Hameed, N. M., & Al-tuwaijari, J. M. (2022). Electrocardiogram ECG For Human Identification Based on Machine Learning Techniques and deep learning. Neuroquantology, 20(3). https://doi.org/10.14704/nq.2022.20.3.NQ22347

Hammad, M., P?awiak, P., Wang, K., & Acharya, U. R. (2021). ResNet-Attention model for human authentication using ECG signals. Expert Systems, 38(6). https://doi.org/10.1111/exsy.12547

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770–778. https://doi.org/10.1109/CVPR.2016.90

Hwang, H., Rehman, H. Z. U., & Lee, S. (2019). 3D U-Net for skull stripping in brain MRI. Applied Sciences, 9(3), 569. https://doi.org//10.3390/app9030569.

Ibrahim, A. E., Abdel-Mageid, S., Nada, N., & Elshahed, M. A. (2022). Human Identification Using Electrocardiogram Signal as a Biometric Trait. International Journal of System Dynamics Applications, 11(3), 1–17. https://doi.org/10.4018/ijsda.287113

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2

khiled AL-Jibory, F., Younis, M. A., & Al-Tamimi, M. S. H. (2022). Preparing of ECG Dataset for Biometric ID Identification with Creative Techniques. https://doi.org/10.18421/TEM114-10.

Kumar, N. A., & Sathish Kumar, S. (2022). Deep Learning-Based Image Preprocessing Techniques for Crop Disease Identification. Futuristic Communication and Network Technologies: Select Proceedings of VICFCNT 2020, 1–10.

Lee, J. A., & Kwak, K. C. (2022). Personal Identification Using an Ensemble Approach of 1D-LSTM and 2D-CNN with Electrocardiogram Signals. Applied Sciences (Switzerland), 12(5). https://doi.org/10.3390/app12052692

Man, S., Maan, A. C., Schalij, M. J., & Swenne, C. A. (2015). Vectorcardiographic diagnostic & prognostic information derived from the 12-lead electrocardiogram: Historical review and clinical perspective. Journal of Electrocardiology, 48(4), 463–475. https://doi.org/10.1016/j.jelectrocard.2015.05.002

Mappangara, I., Qanitha, A., Uiterwaal, C. S. P. M., Henriques, J. P. S., & de Mol, B. A. J. M. (2020). Tele-ECG consulting and outcomes on primary care patients in a low-to-middle income population: the first experience from Makassar telemedicine program, Indonesia. BMC Family Practice, 21(1), 1–11. https://doi.org/10.1186/s12875-020-01325-4

Marinho, L. B., Nascimento, N. de M. M., Souza, J. W. M., Gurgel, M. V., Rebouças Filho, P. P., & de Albuquerque, V. H. C. (2019). A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Generation Computer Systems, 97, 564–577. https://doi.org/10.1016/j.future.2019.03.025

Merone, M., Soda, P., Sansone, M., & Sansone, C. (2017). ECG databases for biometric systems: A systematic review. Expert Systems with Applications, 67, 189–202. https://doi.org/10.1016/j.eswa.2016.09.030.

Nakano, Y., Rashed, E. A., Nakane, T., Laakso, I., & Hirata, A. (2021). ECG localization method based on volume conductor model and Kalman filtering. Sensors, 21(13), 4275. https://doi.org/10.3390/s21134275

Odinaka, I., Lai, P. H., Kaplan, A. D., O’Sullivan, J. A., Sirevaag, E. J., & Rohrbaugh, J. W. (2012). ECG biometric recognition: A comparative analysis. IEEE Transactions on Information Forensics and Security, 7(6), 1812–1824. https://doi.org/10.1109/TIFS.2012.2215324

Pal, A., Gautam, A. K., & Singh, Y. N. (2015). Evaluation of bioelectric signals for human recognition. Procedia Computer Science, 48(C), 746–752. https://doi.org/10.1016/j.procs.2015.04.211

Rahma, M. M., & Salman, A. D. (2022). Heart Disease Classification–Based on the Best Machine Learning Model. Iraqi Journal of Science, 3966–3976. https://doi.org/10.24996/ijs.2022.63.9.28

Sabeeh, M., & Khaled, F. (2021). Plagiarism Detection Methods and Tools: An Overview. Iraqi Journal of Science, 62(8), 2771–2783. https://doi.org/10.24996/ijs.2021.62.8.30

Sadiq, A. T., & Mahmood, N. T. (2014). A hybrid estimation system for medical diagnosis using modified full Bayesian classifier and artificial bee colony. Iraqi Journal of Science, 55(3A), 1095–1107 https://doi.org/10.24996/ijs.2022.63.9.28

Salerno, S. M., Alguire, P. C., & Waxman, H. S. (2003). Competency in Interpretation of 12-Lead Electrocardiograms: A Summary and Appraisal of Published Evidence. Annals of Internal Medicine, 138(9), 751–760. https://doi.org/10.7326/0003-4819-138-9-200305060-00013

Salloum, R., & Kuo, C.-C. J. (2017). ECG-BASED BIOMETRICS USING RECURRENT NEURAL NETWORKS Ronald Salloum and C . -C . Jay Kuo Ming Hsieh Department of Electrical Engineering University of Southern California , Los Angeles , CA. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2017, 2062–2066.

Sandau, K. E., Funk, M., Auerbach, A., Barsness, G. W., Blum, K., Cvach, M., ... & Wang, P. J. (2017). Update to practice standards for electrocardiographic monitoring in hospital settings: a scientific statement from the American Heart Association. Circulation, 136(19), e273-e344. https://doi.org/10.1161/CIR.0000000000000527

Sansone, M., Fusco, R., Pepino, A., & Sansone, C. (2013). Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: A review. Journal of Healthcare Engineering, 4(4), 465–504. https://doi.org/10.1260/2040-2295.4.4.465

Sarkar, P., & Etemad, A. (2020). Self-supervised ECG Representation Learning for Emotion Recognition. IEEE Transactions on Affective Computing, 1–13. https://doi.org/10.1109/TAFFC.2020.3014842

Shkara, A. A., & Hussain, Y. (2018). Heartbeat Amplification and ECG Drawing from Video (Black and White or Colored Videos). Iraqi Journal of Science, 408–419. https://doi:10.24996/ijs.2018.59.1B.21

Sullivan, E. (2020). Understanding from machine learning models. The British Journal for the Philosophy of Science.

Sun, J.-Y., Shen, H., Qu, Q., Sun, W., & Kong, X.-Q. (2021). The application of deep learning in electrocardiogram: Where we came from and where we should go?. International Journal of Cardiology, 337, 71–78. https://doi.org/10.1016/j.ijcard.2021.05.017

Sun, Y., Chan, K. L., & Krishnan, S. M. (2005). Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovascular Disorders, 5(1), 1–7. https://doi.org/ 10.1186/1471-2261-5-28

Teferra, M. N., Kourbelis, C., Newman, P., Ramos, J. S., Hobbs, D., Clark, R. A., & Reynolds, K. J. (2019). Electronic textile electrocardiogram monitoring in cardiac patients: A scoping review protocol. JBI Database of Systematic Reviews and Implementation Reports, 17(2), 147–156. https://doi.org/10.11124/JBISRIR-2017-003630

Yang, X. L., Liu, G. Z., Tong, Y. H., Yan, H., Xu, Z., Chen, Q., Liu, X., Zhang, H. H., Wang, H. B., & Tan, S. H. (2015). The history, hotspots, and trends of electrocardiogram. Journal of Geriatric Cardiology, 12(4), 448–456. https://doi.org/10.11909/j.issn.1671-5411.2015.04.018

Yen, S. B., & Francisco, S. (1990). Initial Study Using Electrocardiogram for Authentication and Identification Teresa. Microbiology, 28(9), 1877–1880.

Yousiaf, T. H., & Al-Tamimi, M. S. H. (2023). The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review. Journal of Applied Engineering and Technological Science (JAETS), 5(1), 321–338.doi : https://doi.org/10.37385/jaets.v5i1.3413

Zhang, Y., Xiao, Z., Guo, Z., & Wang, Z. (2019). ECG-based personal recognition using a convolutional neural network. Pattern Recognition Letters, 125, 668–676.

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Published

2024-06-06

How to Cite

Alzaki, A. N. A., & Al-Tamimi, M. (2024). An Analytical Study on the Most Important Methods and Data Sets Used to Identify People Through ECG: Review . Journal of Applied Engineering and Technological Science (JAETS), 5(2), 842–859. https://doi.org/10.37385/jaets.v5i2.3992