Application of System Analysis and Artificial Intelligence Methods to Diagnose the Dynamic Object State Taking a Retina as an Example. Part 1
DOI:
https://doi.org/10.24160/1993-6982-2023-6-135-143Keywords:
artificial intelligence, system analysis, dynamic object, decision making, diagnostics, vision pathologyAbstract
The article addresses the problem of designing advanced decision support systems to help specialists in diagnosing the states of dynamic objects taking a medical diagnostics problem as an example (early diagnosis of retinal pathologies). For diagnostics, it is proposed to use an integrated approach based on system analysis methods (plotting amplitude-frequency and phase-frequency responses, a wavelet transform for preprocessing and analysis of signals) and artificial intelligence methods (fuzzy sets, cognitive graphics). The research and development activities were carried out jointly by specialists from the National Research University Moscow Power Engineering Institute (the Department of Applied Mathematics and Artificial Intelligence and Management, and Intelligent Technologies) and physiologists of the Helmholtz National Medical Research Center of Eye Diseases (Department of Clinical Physiology of Vision named after S.V. Kravkov). Part 1 of the article presents the general problem of early diagnostics of retinal pathologies, preprocessing of information to highlight the most essential data for subsequent analysis using the system analysis and artificial intelligence methods, and the use of system analysis methods to expand the feature space for diagnostics. In part 2, it is planned to consider the use of fuzzy logic methods for early diagnostics of retinal pathologies, cognitive graphics methods using ontology and fuzzy rules for imaginative representation of retinal states.
References
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Для цитирования: Еремеев А.П., Колосов О.С., Зуева М.В., Цапенко И.В. Применение методов системного анализа и искусственного интеллекта для диагностики состояния динамического объекта на примере органа зрения. Ч. 1 // Вестник МЭИ. 2023. № 6. С. 135—143. DOI: 10.24160/1993-6982-2023-6-135-143
#
1. Kolosov O.S., Korolenkova V.A., Pronin A.D., Zueva M.V., Tsapenko I.V. Postroenie Amplitudno-chastotnykh Kharakteristik Setchatki Glaza i Formalizatsiya Ikh Parametrov dlya Ispol'zovaniya v Sistemakh Diagnostiki. Mekhatronika, Avtomatizatsiya, Upravlenie. 2018;19(7):451—457. (in Russian).
2. Eremeev A.P., Tcapenko I.V. The Use of Cognitive Graphics in the Diagnosis of Complex Vision Pathologies. Intern. J. Information Theories and Appl. 2019;26(1):83—99.
3. Eremeev A.P., Ivliev S.A. Metody i Programmnye Sredstva Prototipa Intellektual'noy Sistemy Podderzhki Prinyatiya Resheniy dlya Analiza i Diagnostiki Slozhnykh Patologiy Zreniya. Vestnik MEI. 2020;5:140—147. (in Russian).
4. Eremeev A.P., Kolosov O.S., Zueva M.V., Tsapenko I.V. Integratsiya Metodov Sistemnogo Analiza i Kognitivnoy Grafiki pri Ranney Diagnostike Patologiy Zreniya. Trudy XX Natsion. Konf. po Iskusstvennomu Intellektu s Mezhdunar. Uchastiem. T. 2. M.: Izd-vo MEI, 2022:313—329. (in Russian).
5. Robson A.G. e. a. ISCEV Standard for Full-field Clinical Electroretinography. Documenta Ophthalmologica. 2022;144;3:165—177.
6. Robson A.G. e. a. ISCEV Guide to Visual Electrodiagnostic Procedures. Documenta Ophthalmologica. 2018;136(1):1—26.
7. Özbay Y., Ceylan R., Karlik B. Integration of Type-2 Fuzzy Clustering and Wavelet Transform in a Neural Network Based ECG Classifier. Expert Systems with Appl. 2011;38;1:1004—1010.
8. Eremeev A.P., Ivliev S.A. Analiz i Diagnostika Slozhnykh Patologiy Zreniya na Osnove Veyvlet-preobrazovaniy i Neyrosetevogo Podkhoda. Integrirovannye Modeli i Myagkie Vychisleniya v iskusstvennom Intellekte: Sb. Trudov VIII Mezhdunar. Nauch.-tekhn. Konf. M.: Fizmatlit, 2015;2:589—595. (in Russian).
9. Brynolfsson J., Sandsten M. Classification of One-dimensional Non-stationary Signals Using the Wigner-ville Distribution in Convolutional Neural Networks. Proc. XXV European Signal Proc. Conf. Institute of Electrical and Electronics Engineers Inc., 2017:326—330.
10. Mallat S.G. A Wavelet Tour of Signal Processing: the SparseWay. Houston: Academic Press, 2009.
11. De Rouck A.F. History of the Electroretinography. In Principlesand Practice of Clinical Electrophysiology of Vision. London: Mit Press, 2006:139—185.
12. Zueva M.V. e. a. Assessment of the Amplitude-frequency Characteristics of the Retina with Its Stimulation by Flicker and Chess Pattern-reversed Incentives and their Use to Obtain New Formalized Signs of Retinal Pathologies. Biomedical J. Sci. &Techn. Research. 2019;19;5:14575—14583
---
For citation: Eremeev A.P., Kolosov O.S., Zueva M.V., Tsapenko I.V. Application of System Analysis and Artificial Intelligence Methods to Diagnose the Dynamic Object State Taking a Retina as an Example. Part 1. Bulletin of MPEI. 2023;6:135—143. (in Russian). DOI: 10.24160/1993-6982-2023-6-135-143
2. Eremeev A.P., Tcapenko I.V. The Use of Cognitive Graphics in the Diagnosis of Complex Vision Pathologies // Intern. J. Information Theories and Appl. 2019. V. 26(1). Рр. 83—99.
3. Еремеев А.П., Ивлиев С.А. Методы и программные средства прототипа интеллектуальной системы поддержки принятия решений для анализа и диагностики сложных патологий зрения // Вестник МЭИ. 2020. № 5. С. 140—147.
4. Еремеев А.П., Колосов О.С., Зуева М.В., Цапенко И.В. Интеграция методов системного анализа и когнитивной графики при ранней диагностике патологий зрения // Труды XX Национ. конф. по искусственному интеллекту с междунар. участием. Т. 2. М.: Изд-во МЭИ, 2022. С. 313—329.
5. Robson A.G. e. a. ISCEV Standard for Full-field Clinical Electroretinography // Documenta Ophthalmologica. 2022. V. 144. No. 3. Pp. 165—177.
6. Robson A.G. e. a. ISCEV Guide to Visual Electrodiagnostic Procedures // Documenta Ophthalmologica. 2018. V. 136(1). Pp. 1—26.
7. Özbay Y., Ceylan R., Karlik B. Integration of Type-2 Fuzzy Clustering and Wavelet Transform in a Neural Network Based ECG Classifier // Expert Systems with Appl. 2011. V. 38. No. 1. Pр. 1004—1010.
8. Еремеев А.П., Ивлиев С.А. Анализ и диагностика сложных патологий зрения на основе вейвлет-преобразований и нейросетевого подхода // Интегрированные модели и мягкие вычисления в искусственном интеллекте: Сб. трудов VIII Междунар. науч.-техн. конф. М.: Физматлит, 2015. T. 2. С. 589—595.
9. Brynolfsson J., Sandsten M. Classification of One-dimensional Non-stationary Signals Using the Wigner-ville Distribution in Convolutional Neural Networks // Proc. XXV European Signal Proc. Conf. Institute of Electrical and Electronics Engineers Inc., 2017. Pp. 326—330.
10. Mallat S.G. A Wavelet Tour of Signal Processing: the SparseWay. Houston: Academic Press, 2009.
11. De Rouck A.F. History of the Electroretinography // In Principlesand Practice of Clinical Electrophysiology of Vision. London: Mit Press, 2006. Pp. 139—185.
12. Zueva M.V. e. a. Assessment of the Amplitude-frequency Characteristics of the Retina with Its Stimulation by Flicker and Chess Pattern-reversed Incentives and their Use to Obtain New Formalized Signs of Retinal Pathologies // Biomedical J. Sci. & Techn. Research. 2019. V. 19. No. 5. Pp. 14575—14583.
---
Для цитирования: Еремеев А.П., Колосов О.С., Зуева М.В., Цапенко И.В. Применение методов системного анализа и искусственного интеллекта для диагностики состояния динамического объекта на примере органа зрения. Ч. 1 // Вестник МЭИ. 2023. № 6. С. 135—143. DOI: 10.24160/1993-6982-2023-6-135-143
#
1. Kolosov O.S., Korolenkova V.A., Pronin A.D., Zueva M.V., Tsapenko I.V. Postroenie Amplitudno-chastotnykh Kharakteristik Setchatki Glaza i Formalizatsiya Ikh Parametrov dlya Ispol'zovaniya v Sistemakh Diagnostiki. Mekhatronika, Avtomatizatsiya, Upravlenie. 2018;19(7):451—457. (in Russian).
2. Eremeev A.P., Tcapenko I.V. The Use of Cognitive Graphics in the Diagnosis of Complex Vision Pathologies. Intern. J. Information Theories and Appl. 2019;26(1):83—99.
3. Eremeev A.P., Ivliev S.A. Metody i Programmnye Sredstva Prototipa Intellektual'noy Sistemy Podderzhki Prinyatiya Resheniy dlya Analiza i Diagnostiki Slozhnykh Patologiy Zreniya. Vestnik MEI. 2020;5:140—147. (in Russian).
4. Eremeev A.P., Kolosov O.S., Zueva M.V., Tsapenko I.V. Integratsiya Metodov Sistemnogo Analiza i Kognitivnoy Grafiki pri Ranney Diagnostike Patologiy Zreniya. Trudy XX Natsion. Konf. po Iskusstvennomu Intellektu s Mezhdunar. Uchastiem. T. 2. M.: Izd-vo MEI, 2022:313—329. (in Russian).
5. Robson A.G. e. a. ISCEV Standard for Full-field Clinical Electroretinography. Documenta Ophthalmologica. 2022;144;3:165—177.
6. Robson A.G. e. a. ISCEV Guide to Visual Electrodiagnostic Procedures. Documenta Ophthalmologica. 2018;136(1):1—26.
7. Özbay Y., Ceylan R., Karlik B. Integration of Type-2 Fuzzy Clustering and Wavelet Transform in a Neural Network Based ECG Classifier. Expert Systems with Appl. 2011;38;1:1004—1010.
8. Eremeev A.P., Ivliev S.A. Analiz i Diagnostika Slozhnykh Patologiy Zreniya na Osnove Veyvlet-preobrazovaniy i Neyrosetevogo Podkhoda. Integrirovannye Modeli i Myagkie Vychisleniya v iskusstvennom Intellekte: Sb. Trudov VIII Mezhdunar. Nauch.-tekhn. Konf. M.: Fizmatlit, 2015;2:589—595. (in Russian).
9. Brynolfsson J., Sandsten M. Classification of One-dimensional Non-stationary Signals Using the Wigner-ville Distribution in Convolutional Neural Networks. Proc. XXV European Signal Proc. Conf. Institute of Electrical and Electronics Engineers Inc., 2017:326—330.
10. Mallat S.G. A Wavelet Tour of Signal Processing: the SparseWay. Houston: Academic Press, 2009.
11. De Rouck A.F. History of the Electroretinography. In Principlesand Practice of Clinical Electrophysiology of Vision. London: Mit Press, 2006:139—185.
12. Zueva M.V. e. a. Assessment of the Amplitude-frequency Characteristics of the Retina with Its Stimulation by Flicker and Chess Pattern-reversed Incentives and their Use to Obtain New Formalized Signs of Retinal Pathologies. Biomedical J. Sci. &Techn. Research. 2019;19;5:14575—14583
---
For citation: Eremeev A.P., Kolosov O.S., Zueva M.V., Tsapenko I.V. Application of System Analysis and Artificial Intelligence Methods to Diagnose the Dynamic Object State Taking a Retina as an Example. Part 1. Bulletin of MPEI. 2023;6:135—143. (in Russian). DOI: 10.24160/1993-6982-2023-6-135-143
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Published
2023-09-05
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Section
Informatics and information processes (technical sciences) (2.3.8.)
