Learnable Feature Alignment with Attention-Based Data Augmentation for Medical Time Series

This work presents a novel approach to medical time series analysis through learnable feature alignment combined with attention-based data augmentation techniques. The method addresses the challenges of limited data and temporal dependencies in medical time series applications.

Key Contributions

• Learnable feature alignment mechanism for time series data
• Attention-based data augmentation strategies
• Improved performance on medical time series prediction tasks
• Novel approach to handling temporal dependencies in medical data

Applications

The proposed method has been successfully applied to:

• EEG signal processing and cognitive load classification
• ICU outcome prediction
• Multi-domain time series analysis
• Clinical decision support systems

Technical Innovation

The work introduces attention mechanisms that can adaptively focus on relevant temporal patterns while simultaneously learning optimal feature alignment strategies for improved generalization in medical time series tasks.

Recommended citation: Jalali, A. et al. (2024). "Learnable Feature Alignment with Attention-Based Data Augmentation for Medical Time Series." Applied Soft Computing.
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