Enhancing Medical Signal Processing and Diagnosis With AI-Generated Content Techniques.

Area of Science:

• Medical Diagnostics
• Artificial Intelligence
• Biomedical Signal Processing

Background:

• Accurate classification of biomedical signals, like electroencephalogram (EEG), is vital for diagnosing neurological disorders such as epilepsy.
• Data scarcity and imbalance in EEG datasets pose significant challenges for developing robust diagnostic models.
• Existing methods struggle to effectively process and classify complex EEG signals for reliable epilepsy detection.

Purpose of the Study:

• To enhance medical signal processing and epilepsy diagnosis using AI-generated content.
• To address data scarcity and imbalance issues in EEG datasets for improved classification accuracy.
• To introduce a novel framework combining generative adversarial networks (GANs) and attention-based temporal convolutional networks (TCNs).

Main Methods:

• Utilized generative adversarial networks (GANs) to synthesize realistic EEG signals for data augmentation.
• Developed an attention-based temporal convolutional network (TCN) model for efficient EEG signal processing and classification.
• Evaluated the framework on the Bonn Epilepsy Data, performing comprehensive ablation studies.

Main Results:

• Achieved a high classification accuracy of 98.89% for epilepsy detection.
• Obtained an F1 score of 98.91%, indicating excellent performance in identifying epileptic seizures.
• Demonstrated significant improvements in diagnostic model robustness and accuracy through AI-generated data augmentation.

Conclusions:

• AI-generated content, specifically synthetic EEG data, effectively mitigates data scarcity and imbalance challenges.
• The proposed framework integrating GANs and attention-based TCNs shows significant potential for advancing medical signal processing and epilepsy diagnosis.
• This approach offers a promising direction for developing more accurate and reliable diagnostic tools for neurological disorders.