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

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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

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