Why the study?
Data imbalance in gathered ECG signals hinders deep learning networks from recognizing minority abnormalities, prompting the evaluation of GAN strategies to balance data and enhance classification.
The use of GANs to synthesize ECG data and balance datasets significantly improves the accuracy of deep learning classification models.
May support GAN-augmented CNNs for ECG classification; leaves open prospective validation before clinical use.
The prompt detection of irregularities on Electrocardiograms can be crucial in patient monitoring, deep learning-based strategy that combines segmentation and classification as one procedure has recently been effectively applied to ECG signal analysis. In the gathered ECG signals, it frequently results in an unbalanced ratio of small and large cases of numerous abnormalities. The deep network is unable to learn how to recognize minority groups because of this data imbalance. Various physiological signal analyses, medical imaging datasets have all been balanced using GANs. GAN and Deep Learning integrate well together in the healthcare, particularly for the recognition and characterization of ECG patterns. In order to improve categorization and efficiency, this study presents a comprehensive overview of the GAN strategies for data imbalance and deep learning strategies for physiological signal classification. With Cardio GAN, CNN obtained accuracy greater than 99.0 percent and performs superior classification.
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Janbhasha et al. (2023) studied this question.
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