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.inAndhra 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 generatywneBibliografia
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Autorzy
Swarajya Madhuri Rayavarapumadhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0007-7559-2142
Autorzy
Tammineni Shanmukha PrasanthiAndhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0000-5352-2265
Autorzy
Gottapu Santosh KumarGayatri Vidya Parishad College of Engineering, Department of Civil Engineering Indie
https://orcid.org/0000-0002-1452-9752
Autorzy
Gottapu Sasibhushana RaoAndhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0000-0001-6346-8274
Autorzy
Gottapu PrashantiAvanthi Institute of Pharmaceutical Sciences, Department of Pharmaceutical Technology Indie
https://orcid.org/0009-0002-7231-0377
Statystyki
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Inne teksty tego samego autora
- Swarajya Madhuri Rayavarapu, Shanmukha Prasanthi Tammineni, Sasibhushana Rao Gottapu, Aruna Singam, PRZEGLĄD GENERATYWNYCH SIECI PRZECIWSTAWNYCH DLA ZASTOSOWAŃ BEZPIECZEŃSTWA , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Tom 14 Nr 2 (2024)