GENERATYWNY MODEL Z DEEP FAKE AUGUMENTATION DLA SYGNAŁÓW Z FONOKARDIOGRAMU ORAZ ELEKTROKARDIOGRAMU W STRUKTURACH LSGAN ORAZ CYCLE GAN

Swarajya Madhuri Rayavarapu

madhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering (Indie)
https://orcid.org/0009-0007-7559-2142

Tammineni Shanmukha Prasanthi


Andhra University, Department of Electronics and Communication Engineering (Indie)
https://orcid.org/0009-0000-5352-2265

Gottapu Santosh Kumar


Gayatri Vidya Parishad College of Engineering, Department of Civil Engineering (Indie)
https://orcid.org/0000-0002-1452-9752

Gottapu Sasibhushana Rao


Andhra University, Department of Electronics and Communication Engineering (Indie)
https://orcid.org/0000-0001-6346-8274

Gottapu Prashanti


Avanthi Institute of Pharmaceutical Sciences, Department of Pharmaceutical Technology (Indie)
https://orcid.org/0009-0002-7231-0377

Abstrakt

W celu zdiagnozowania szeregu chorób serca, istotne jest przeprowadzenie dokładnej oceny danych z fonokardiogramu (PCG) i elektrokardiogram (EKG). Sztuczna inteligencja i diagnostyka wspomagana komputerowo, oparta na uczeniu maszynowym stają się coraz bardziej powszechne we współczesnej medycynie, pomagając klinicystom w podejmowaniu krytycznych decyzji. Z kolei, Wymóg ogromnej ilości informacji do trenowania, w celu ustalenia platformy (ang. framework) techniki, opartej na głębokim uczeniu stanowi empiryczne wyzwanie w obszarze medycyny. Zwiększa to ryzyko niewłaściwego wykorzystania danych osobowych. Bezpośrednim skutkiem tego problemu był gwałtowny rozwój badań nad metodami tworzenia syntetycznych danych pacjentów. Badacze podjęli próbę wygenerowania syntetycznych odczytów diagramów EKG lub PCG. Stąd, w celu zrównoważenia zbioru danych, w pierwszej kolejności utworzono dane EKG w bazie danych arytmii MIT-BIH przy użyciu struktur sieci generatywnych LSGAN i Cycle GAN. Następnie, wykorzystując strukturę sieci VGGNet, przeprowadzono badania, mające na celu klasyfikację arytmii na potrzeby syntetyzowanych sygnałów EKG. Dla wygenerowanych sygnałów, przypominających sygnał oryginalny uzyskano dobre rezultaty. Należy podkreślić, że uzyskana dokładność wynosiła 91,20%, powtarzalność 89,52% i wynik F1 – odpowiednio 90,35%.


Słowa kluczowe:

arytmia, osłuchiwanie, elektrokardiogram, fonokardiogram, sieci generatywne

Ahmed N., Zhu Y.: Early Detection of Atrial Fibrillation Based on ECG Signals. Bioengineering 7(1), 2020, 16 [http://doi.org/10.3390/bioengineering7010016].
DOI: https://doi.org/10.3390/bioengineering7010016   Google Scholar

Akkaradamrongrat S. et al.: Text generation for imbalanced text classification. 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2019, 181–186.
DOI: https://doi.org/10.1109/JCSSE.2019.8864181   Google Scholar

Aziz S. et al.: Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features. Sensors 20(13), 2020, 3790 [http://doi.org/10.3390/s20133790].
DOI: https://doi.org/10.3390/s20133790   Google Scholar

Bentley P. et al.: Classifying Heart Sounds Challenge. 2011 [http://www.peterjbentley.com/heartchallenge/index.html]
  Google Scholar

Bouril D. et al.: Automated classification of normal and abnormal heart sounds using support vector machines. Computing in Cardiology Conference – CinC, Vancouver 2016, 549–552.
DOI: https://doi.org/10.22489/CinC.2016.158-329   Google Scholar

Cayce G. I. et al.: Improved Neural Network Arrhythmia Classification Through Integrated Data Augmentation. IEEE MetroCon, Hurst 2022, 1–3.
DOI: https://doi.org/10.1109/MetroCon56047.2022.9971141   Google Scholar

England J. R., Cheng P. M.: Artificial intelligence for medical image analysis: a guide for authors and reviewers. American journal of roentgenology 212(3), 2019, 513–519.
DOI: https://doi.org/10.2214/AJR.18.20490   Google Scholar

Garcea F. et al.: Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine 152, 2023, 106391 [http://doi.org/10.1016/j.compbiomed.2022.106391].
DOI: https://doi.org/10.1016/j.compbiomed.2022.106391   Google Scholar

Goldberger A. L. et al.: PhysioBank, PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, 2000, 215–220.
DOI: https://doi.org/10.1161/01.CIR.101.23.e215   Google Scholar

Goodfellow I. et al.: Generative adversarial networks. Communications of the ACM, 63(11), 2020, 139–144 [http://doi.org/10.1145/3422622].
DOI: https://doi.org/10.1145/3422622   Google Scholar

Guo G. et al.: Multimodal Emotion Recognition Using CNN-SVM with Data Augmentation. IEEE International Conference on Bioinformatics and Biomedicine, Las Vegas 2022, 3008–3014.
DOI: https://doi.org/10.1109/BIBM55620.2022.9994936   Google Scholar

Houssein E. H.: ECG signals classification: a review. International Journal of Intelligent Engineering Informatics 5(4), 2017, 376–396.
DOI: https://doi.org/10.1504/IJIEI.2017.087944   Google Scholar

Judge R., Mangrulkar R.: Heart Sound and Murmur Library. [http://open.umich.edu/education/med/resources/heart-sound-murmur-library/2015].
  Google Scholar

Khalifa Y et al.: A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals. Information Fusion 69, 2021, 52–72.
DOI: https://doi.org/10.1016/j.inffus.2020.11.008   Google Scholar

Li H. et al.: Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram. IEEE Access 7, 2019, 146457–146469.
DOI: https://doi.org/10.1109/ACCESS.2019.2943197   Google Scholar

Li J., Ke L., Du Q., Ding X., Chen X.: Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS. Applied Sciences 12(22), 2022, 11762.
DOI: https://doi.org/10.3390/app122211762   Google Scholar

Liu C. et al.: An open access database for the evaluation of heart sound algorithms. Physiological Measurement 37(12), 2016, 2181.
DOI: https://doi.org/10.1088/0967-3334/37/12/2181   Google Scholar

Mao X. et al.: Least Squares Generative Adversarial Networks. arXiv, 2017 [http://arxiv.org/abs/1611.04076].
DOI: https://doi.org/10.1109/ICCV.2017.304   Google Scholar

Nedoma J. et al.: Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body. 40th International Conference on Telecommunications and Signal Processing – TSP, 2017, 420–424.
DOI: https://doi.org/10.1109/TSP.2017.8076019   Google Scholar

Rahman, M. M. et al.: A Systematic Survey of Data Augmentation of ECG Signals for AI Applications. Sensors, 23(11), 2023, 5237 [http://doi.org/10.3390/s23115237].
DOI: https://doi.org/10.3390/s23115237   Google Scholar

Simonyan K., Zisserman A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv, 2015 [http://arxiv.org/abs/1409.1556].
  Google Scholar

Skandarani Y. et al.: GANs for medical image synthesis: An empirical study. Journal of Imaging 9(3), 2023 [http://doi.org/10.3390/jimaging9030069].
DOI: https://doi.org/10.3390/jimaging9030069   Google Scholar

Sreeniwas Kumar A., Nakul S.: Cardiovascular Disease in India: A 360 Degree Overview. Medical Journal Armed Forces India 76(1), 2020, 1–3 [http://doi.org/10.1016/j.mjafi.2019.12.005].
DOI: https://doi.org/10.1016/j.mjafi.2019.12.005   Google Scholar

Wang T. C. et al.: High-resolution image synthesis and semantic manipulation with conditional gans. IEEE Conference on computer vision and pattern recognition. Salt Lake City, 2018, 8798–8807.
DOI: https://doi.org/10.1109/CVPR.2018.00917   Google Scholar

Wu J. L. et al.: A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approaches. Soft Comput 27, 2023, 8209–8222
  Google Scholar

[http://doi.org/10.1007/s00500-022-07716-2].
DOI: https://doi.org/10.1007/s00500-022-07716-2   Google Scholar

Xiong P. et al.: Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Frontiers in Cardiovascular Medicine 9, 2022 [http://doi.org/10.3389/fcvm.2022.860032].
DOI: https://doi.org/10.3389/fcvm.2022.860032   Google Scholar

Zhu J. Y. et al.: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv [http://arxiv.org/abs/1703.10593].
  Google Scholar


Opublikowane
2023-12-20

Cited By / Share

Rayavarapu, S. M., Prasanthi, T. S., Kumar, G. S., Rao, G. S., & Prashanti, G. (2023). GENERATYWNY MODEL Z DEEP FAKE AUGUMENTATION DLA SYGNAŁÓW Z FONOKARDIOGRAMU ORAZ ELEKTROKARDIOGRAMU W STRUKTURACH LSGAN ORAZ CYCLE GAN. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(4), 34–38. https://doi.org/10.35784/iapgos.3783

Autorzy

Swarajya Madhuri Rayavarapu 
madhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0007-7559-2142

Autorzy

Tammineni Shanmukha Prasanthi 

Andhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0000-5352-2265

Autorzy

Gottapu Santosh Kumar 

Gayatri Vidya Parishad College of Engineering, Department of Civil Engineering Indie
https://orcid.org/0000-0002-1452-9752

Autorzy

Gottapu Sasibhushana Rao 

Andhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0000-0001-6346-8274

Autorzy

Gottapu Prashanti 

Avanthi Institute of Pharmaceutical Sciences, Department of Pharmaceutical Technology Indie
https://orcid.org/0009-0002-7231-0377

Statystyki

Abstract views: 137
PDF downloads: 144


Inne teksty tego samego autora