Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data

Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data

© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Aslan Tatarkanov, Abas Lampezhev, Dmitry Polezhaev, Ruslan Tekeev
DOI : 10.14445/22315381/IJETT-V70I11P213

How to Cite?

Aslan Tatarkanov, Abas Lampezhev, Dmitry Polezhaev, Ruslan Tekeev, "Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 129-137, 2022. Crossref,

The object of this study is medical automatic diagnostic systems, and the subject is automated techniques for diagnosing diseases designed for a countable amount of training and control samples. The necessity to introduce more and more used methods of state assessment and diagnostics of pathological change in research of the human central nervous and cardiovascular apparatus is obvious. The study results make it possible to obtain clinical, functional analysis methods. However, the problem is that they are very complicated in technical execution. This work conducted a set of studies to develop a formal description of methodological approaches to form the image of automated diagnostics of medical and biological systems subjected to random perturbation. This study reviewed current diagnostic methods of the main diagnostic system elements. Research on the development of statistical recognition systems, providing a link of the detection reliability with the necessary constraints to achieve this, is relevant. The study showed that the formation of features using a nonlinear transformation procedure in initial signal spaces and a stochastic coding method of classification of the features is based on calculating the correlation moment using the correlation functions of signs. Given this fact, we propose a methodical solution, which does not imply achieving a clear optimum under an arbitrary distribution of a priori data. This fact helps construct the suboptimal algorithm of the engineering system.

Biomedical signals, Functional medical diagnostic methods, Recognition methods, Statistical data processing, Stochastic coding.

[1] G. Tenaglia, F. Romanelli, S. La Rovere, G. M. Polli, L. Gabellieri, and M. Valis, “Functional Analysis for the Diagnostic Systems to Support the Exploitation of the Divertor Tokamak Test Facility,” Fusion Engineering and Design, vol. 170, pp. 112692, 2021. Crossref,
[2] S. Magasi and H. Marshall, “Accessible Medical Diagnostic Equipment: A Rapid Review,” Archives of Physical Medicine and Rehabilitation, vol. 102, no.10, pp. e113, 2021. Crossref,
[3] D. Dharmaprani, A. Lahiri, A. N. Ganesan, N. Kyriacou, and A. D. McGavigan, “Comparative Spatial Resolution of 12-Lead Electrocardiography and An Automated Algorithm,” Heart Rhythm, vol. 17, no.2 pp. 324-331, 2020. Crossref,
[4] F. J. C. Gascó, “Clinical Electrocardiography,” A Textbook, Revista Española de Cardiología, vol. 75, no. 5, pp. 453, 2022. Crossref, 10.1016/j.rec.2021.12.012
[5] Wu H.T, “Current State of Nonlinear-Type Time-Frequency Analysis and Applications to High-Frequency Biomedical Signals,” Current Opinion in Systems Biology, vol. 23, pp. 8-21, 2020. Crossref,
[6] R. Cassani and T. H. Falk, “Spectrotemporal Modeling of Biomedical Signals: Theoretical Foundation and Applications, Encyclopedia of Biomedical Engineering,” R. Narayan, Ed. New York, USA: Elsevier, pp. 144-163, 2019. Crossref,
[7] A. Karagiannis and P. Constantinou, “A Prediction Model for the Number of Intrinsic Mode Functions in Biomedical Signals: The Case of Electrocardiogram,” Biomedical Signal Processing and Control, vol. 6, no. 3, pp. 231-243, 2011. Crossref,
[8] M. R. Jennings, C. Turner, R. R. Bond, A. Kennedy, R. Thantilage, M. T. Kechadi, N.-A. Le-Khac, J. McLaughlin, and D. D. Finlay, “Code-Free Cloud Computing Service to Facilitate Rapid Biomedical Digital Signal Processing and Algorithm Development,” Computer Methods and Programs in Biomedicine, vol. 211, pp. 106398, 2021. Crossref,
[9] X. Wu, Z. Tao, B. Jiang, T. Wu, X. Wang, and H. Chen, “Domain Knowledge-Enhanced Variable Selection for Biomedical Data Analysis,” Information Sciences, vol. 606, pp. 469-488, 2022. Crossref,
[10] Y.-D. Lin, Y. K. Tan, and B. Tian, “A Novel Approach for Decomposition of Biomedical Signals in Different Applications Based on Data-Adaptive Gaussian Average Filtering,” Biomedical Signal Processing and Control, vol. 71, pp. 103104, 2022. Crossref,
[11] A. Yakimovich, A. Beaugnon, Y. Huang, and E. Ozkirimli, “Labels in a Haystack: Approaches Beyond Supervised Learning in Biomedical Applications,” Patterns, vol. 2, no. 12, pp. 100383, 2021. Crossref,
[12] D. Painuli, S. Bhardwaj, and U. Köse, “Recent Advancement in Cancer Diagnosis using Machine Learning and Deep Learning Techniques: A Comprehensive Review,” Computers in Biology and Medicine, vol. 146, pp. 105580, 2022. Crossref,
[13] S. M. H. Naqvi, S. C. Mehta, and A. D. Sharma, “Novel Methods and Regulation on Electronic Data Collection in Clinical Trials,” Journal of Clinical and Diagnostic Research, vol. 12, no.3, pp. FE01-FE03, 2018. Crossref,
[14] A. V. Prakash and S. Das, “Medical Practitioner's Adoption of Intelligent Clinical Diagnostic Decision Support Systems: A Mixed-Methods Study,” Information Management, vol. 58, no. 7, pp. 103524, 2021. Crossref,
[15] Z. Qiana, K. Huang Q.F. Wang, and X.Y. Zhang, “A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies,” Pattern Recognition, vol. 131, pp. 108889, 2022. Crossref,
[16] E. Butkevičiūtė, L. Bikulčienė, and T. Blažauskas, “The Unsupervised Pattern Recognition for the ECG Signal Features Detection,” Biomedical Signal Processing and Control, vol. 78, pp. 103947, 2022. Crossref,
[17] M. A. A. Gonzalez, M. J. Abe, D. L. L. Antonio, D. S. J. Santos, B. F. Amadeu, D. S. N. Amado, and S. L. Sayuri, “PANN Component for Use in Pattern Recognition in Medical Diagnostics Decision-Making," Procedia Compter Science, vol. 192, pp. 1750-1759, 2021. Crossref,
[18] J. Jurek, W. Wójtowicz, and A. Wójtowicz, “Syntactic Pattern Recognition-Based Diagnostics of Fetal Palates,” Pattern Recognition Letters, vol. 133, pp. 144-150, 2020. Crossref,
[19] M. R. Ogiela and R. Tadeusiewicz, “Syntactic Reasoning and Pattern Recognition for Analysis of Coronary Artery Images,” Intelligence-Based Medicine, vol. 26, pp. 145-159, 2002. Crossref,
[20] D. T. Holmes, “Chapter 2 - Statistical Methods in Laboratory Medicine, Contemporary Practice in Clinical Chemistry,” W. Clarke and M. A. Marzinke, Eds. New York, USA: Academic Press, pp. 15-35, 2020. Crossref,
[21] A. Bardossy, A. Blinowska, W. Kuzmicz, J. Ollitrault, M. Lewandowski, A. Przybylski, and Z. Jaworski, “Fuzzy Logic-Based Diagnostic Algorithm for Implantable Cardioverter Defibrillators,” Intelligence-Based Medicine, vol. 60, no.2, pp. 113-121, 2014. Crossref,
[22] M. I. Zabezhailo and Y. Y. Trunin, “On the Problem of Medical Diagnostic Evidence: Intelligent Analysis of Empirical Data on Patients in Samples of Limited Size,” Auto. Doc. Math. Ling, vol. 53, pp. 322-328, 2019. Crossref,
[23] F. Nielsen, “Generalized Bhattacharyya and Chernoff Upper Bounds on Bayes Error using Quasi-Arithmetic Means,” Pattern Recognition Letters, vol. 42, pp. 25-34, 2014. Crossref,
[24] V.-D. Nguyen, N. L. Trung, and K. Abed-Meraim, “Robust Subspace Tracking Algorithms using Fast Adaptive Mahalanobis Distance,” SiGN-Proc Manual, vol. 195, pp. 108402, 2022. Crossref,
[25] A. Subasi, “Chapter 5 - Biomedical Signal Classification Methods, in Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques,” A. Subasi, Ed. New York, USA: Academic Press, pp. 277-434, 2019. Crossref,
[26] M. Kazemzadeh, C. L. Hisey, K. Zargar-Shoshtari, W. Xu, and N. G. R. Broderick, “Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications,” Optics Communications, vol. 510, pp. 127977, 2022. Crossref,
[27] O. Nakonechnyi, V. Martsenyuk, A. Klos-Witkowska and D. Zhehestovska, “Minimax Combined with Machine Learning to Cope with Uncertainties in Medical Application,” in Proceedings of Sixth International Congress on Information and Communication Technology, vol. 217, 2021. Crossref,
[28] Jeon B, Y. Hong, D. Han, Y. Jang, S. Jung, Y. Hong, S. Ha, H. Shim, and H.J. Chang, “Maximum a Posteriori Estimation Method for Aorta Localization and Coronary Seed Identification,” Pattern Recognition, vol. 68, pp. 222-232, 2017. Crossref,
[29] Li, C., Q. Hu, D. Zhang, F. Hoffstaedter, A. Bauer, and D. Elmenhorst, “Neural Correlates of Affective Control Regions Induced by Common Therapeutic Strategies in Major Depressive Disorders: An Activation Likelihood Estimation Meta-Analysis Study,” Neuroscience & Biobehavioral Reviews, vol. 137, pp. 104643, 2022. Crossref,
[30] A. Almomany, W. R. Ayyad, and A. Jarrah, “Optimized Implementation of an Improved KNN Classification Algorithm using Intel FPGA Platform: Covid-19 Case Study,” Journal of King Saud University: Computer and Information Sciences, vol. 34, no.6, pp. 3815-3827, 2022. Crossref,
[31] A. A. Tatarkanov, I. A. Alexandrov, and I. V. Vorobieva, “Automated Diagnostics of Early Stages of Diabetic Retinopathy Using Neural Network Modeling Methods,” in Proceedings of T&QM&IS, pp. 554-559, 2021. Crossref,
[32] A. A. Tatarkanov, I. A. Alexandrov, A. A. Bestavashvili, and Y. Kopylov, “Using a One-Dimensional Convolution Neural Network to Detect Atrial Fibrillation,” in Proceedings of T&QM&IS, pp. 560-564, 2021. Crossref,
[33] A. Tatarkanov, I. Alexandrov, and R. Glashev, “Synthesis of Neural Network Structure for the Analysis of Complex Structured Ocular Fundus Images,” Journal of Applied Science and Engineering, vol. 19, no. 2, pp. 344-355, 2021. Crossref,
[34] Deris MSM, Mohamed MA, Mohamed RR, Derahman MN, Mamat AR, Kadir MFA and Ahmad F A Abidin., “Challenges in Providing Access to Health Facilities for Rural Citizens in Developing Countries,” International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 36-40, 2021. Crossref, Crossref,
[35] Baskoun Y, Qanadli SD, Arouch M and Taouzari M, “Design and Realization of an Innovative Adapter for the Visualization and Direct Exploitation of Endoscopy Images on Smartphones,” International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 200-203, 2021. Crossref, Crossref,