Advanced Epileptic Seizure Detection Model Using Adaptive Deep Learning Technique With Coordinate Attention-Based Feature Fusion Approach DOI
Neeraj Kumari, Rajeev Kumar,

Rajiv Kumar Sharma

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 451 - 464

Published: Feb. 21, 2025

Epileptic seizures are neurological disruptions that can severely impact an individual's quality of life if left undetected or misdiagnosed. Accurate and timely detection is crucial for effective intervention management. This paper proposes Advanced Seizure Detection Model leveraging Adaptive Deep Learning Technique integrated with a Coordinate Attention-based Feature Fusion Approach. The model designed to analyze electroencephalogram (EEG) signals, which serve as critical diagnostic tool epilepsy. adaptive deep learning framework dynamically optimizes network parameters accommodate the variability in EEG signals across different patients. Attention Mechanism enhances model's ability focus on spatial temporal features, ensuring more robust representation seizure patterns.

Language: Английский

Advanced Epileptic Seizure Detection Model Using Adaptive Deep Learning Technique With Coordinate Attention-Based Feature Fusion Approach DOI
Neeraj Kumari, Rajeev Kumar,

Rajiv Kumar Sharma

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 451 - 464

Published: Feb. 21, 2025

Epileptic seizures are neurological disruptions that can severely impact an individual's quality of life if left undetected or misdiagnosed. Accurate and timely detection is crucial for effective intervention management. This paper proposes Advanced Seizure Detection Model leveraging Adaptive Deep Learning Technique integrated with a Coordinate Attention-based Feature Fusion Approach. The model designed to analyze electroencephalogram (EEG) signals, which serve as critical diagnostic tool epilepsy. adaptive deep learning framework dynamically optimizes network parameters accommodate the variability in EEG signals across different patients. Attention Mechanism enhances model's ability focus on spatial temporal features, ensuring more robust representation seizure patterns.

Language: Английский

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