A GENERATIVE MODEL FOR DEEP FAKE AUGMENTATION OF PHONOCARDIOGRAM AND ELECTROCARDIOGRAM SIGNALS USING LSGAN AND CYCLE GAN
Swarajya Madhuri Rayavarapu
madhurirayavarapu.rs@andhrauniversity.edu.inAndhra 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 networksReferences
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Authors
Swarajya Madhuri Rayavarapumadhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0007-7559-2142
Authors
Tammineni Shanmukha PrasanthiAndhra University, Department of Electronics and Communication Engineering India
https://orcid.org/0009-0000-5352-2265
Authors
Gottapu Santosh KumarGayatri Vidya Parishad College of Engineering, Department of Civil Engineering India
https://orcid.org/0000-0002-1452-9752
Authors
Gottapu Sasibhushana RaoAndhra University, Department of Electronics and Communication Engineering India
https://orcid.org/0000-0001-6346-8274
Authors
Gottapu PrashantiAvanthi Institute of Pharmaceutical Sciences, Department of Pharmaceutical Technology India
https://orcid.org/0009-0002-7231-0377
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