A GENERATIVE MODEL FOR DEEP FAKE AUGMENTATION OF PHONOCARDIOGRAM AND ELECTROCARDIOGRAM SIGNALS USING LSGAN AND CYCLE GAN

Swarajya Madhuri Rayavarapu

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

Tammineni Shanmukha Prasanthi


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

Gottapu Santosh Kumar


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

Gottapu Sasibhushana Rao


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

Gottapu Prashanti


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

Abstract

In order to diagnose a range of cardiac conditions, it is important to conduct an accurate evaluation of either phonocardiogram (PCG) and electrocardiogram (ECG) data. Artificial intelligence and machine learning-based computer-assisted diagnostics are becoming increasingly commonplace in modern medicine, assisting clinicians in making life-or-death decisions. The requirement for an enormous amount of information for training to establish the framework for a deep learning-based technique is an empirical challenge in the field of medicine. This increases the risk of personal information being misused. As a direct result of this issue, there has been an explosion in the study of methods for creating synthetic patient data. Researchers have attempted to generate synthetic ECG or PCG readings. To balance the dataset, ECG data were first created on the MIT-BIH arrhythmia database using LS GAN and Cycle GAN. Next, using VGGNet, studies were conducted to classify arrhythmias for the synthesized ECG signals. The synthesized signals performed well and resembled the original signal and the obtained precision of 91.20%, recall of 89.52% and an F1 score of 90.35%.


Keywords:

arrhythmia, auscultation, electrocardiogram, phonocardiogram, generative networks

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Published
2023-12-20

Cited by

Rayavarapu, S. M., Prasanthi, T. S., Kumar, G. S., Rao, G. S., & Prashanti, G. (2023). A GENERATIVE MODEL FOR DEEP FAKE AUGMENTATION OF PHONOCARDIOGRAM AND ELECTROCARDIOGRAM SIGNALS USING LSGAN AND CYCLE GAN. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(4), 34–38. https://doi.org/10.35784/iapgos.3783

Authors

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

Authors

Tammineni Shanmukha Prasanthi 

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

Authors

Gottapu Santosh Kumar 

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

Authors

Gottapu Sasibhushana Rao 

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

Authors

Gottapu Prashanti 

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

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