Hierarchical Time-Series Representation Learning

Project Overview

This research project focuses on developing hierarchical time-series representation learning techniques for medical applications, specifically targeting multi-domain EEG modeling for cognitive-load classification and ICU outcome prediction systems.

Technical Approach

• Hierarchical Architectures: Development of multi-scale temporal feature extraction methods
• Multi-domain Adaptation: Robust learning across different EEG acquisition systems and protocols
• Transformer Integration: Application of attention mechanisms to time-series medical data
• Clinical Validation: Extensive testing on real-world clinical datasets

Key Achievements

• Significant improvements in cognitive load classification accuracy
• Enhanced ICU outcome prediction performance
• Robust performance across multiple medical centers
• Real-time processing capabilities for clinical deployment

Applications

The developed methods have been successfully applied to:

• Cognitive load assessment in clinical settings
• ICU patient monitoring and outcome prediction
• EEG-based brain-computer interface systems
• Neurological disorder classification

Impact & Publications

This work has resulted in multiple peer-reviewed publications including papers in Applied Soft Computing and Engineering Applications of Artificial Intelligence, demonstrating the clinical relevance and technical innovation of the approach.