Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection

2025-09-17

AI Summary

Epileptic seizure detection urgently requires lightweight, personalized continual learning methods tailored for wearable devices to address both inter- and intra-patient nonstationarity in EEG features and severe scarcity of labeled data. To this end, we propose EpiSMART: a framework featuring a capacity-constrained replay buffer coupled with a high-entropy prediction–driven sample selection strategy to mitigate catastrophic forgetting under extremely low annotation cost; and a lightweight model update mechanism enabling real-time, resource-efficient on-device deployment. Evaluated on the CHB-MIT dataset, EpiSMART achieves a 21% improvement in F1-score, requiring only 6.46 minutes of daily annotation and 6.28 model updates on average—substantially enhancing clinical deployability. Its core innovation lies in the synergistic design of entropy-aware replay and efficient personalized continual learning.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Objective: Epilepsy, a prevalent neurological disease, demands careful diagnosis and continuous care. Seizure detection remains challenging, as current clinical practice relies on expert analysis of electroencephalography, which is a time-consuming process and requires specialized knowledge. Addressing this challenge, this paper explores automated epileptic seizure detection using deep learning, focusing on personalized continual learning models that adapt to each patient's unique electroencephalography signal features, which evolve over time. Methods: In this context, our approach addresses the challenge of integrating new data into existing models without catastrophic forgetting, a common issue in static deep learning models. We propose EpiSMART, a continual learning framework for seizure detection that uses a size-constrained replay buffer and an informed sample selection strategy to incrementally adapt to patient-specific electroencephalography signals. By selectively retaining high-entropy and seizure-predicted samples, our method preserves critical past information while maintaining high performance with minimal memory and computational requirements. Results: Validation on the CHB-MIT dataset, shows that EpiSMART achieves a 21% improvement in the F1 score over a trained baseline without updates in all other patients. On average, EpiSMART requires only 6.46 minutes of labeled data and 6.28 updates per day, making it suitable for real-time deployment in wearable systems. Conclusion:EpiSMART enables robust and personalized seizure detection under realistic and resource-constrained conditions by effectively integrating new data into existing models without degrading past knowledge. Significance: This framework advances automated seizure detection by providing a continual learning approach that supports patient-specific adaptation and practical deployment in wearable healthcare systems.

Problem

• Personalized seizure detection with minimal labeled data
• Preventing catastrophic forgetting in continual learning models
• Resource-efficient adaptation for wearable healthcare systems

Innovation

• Continual learning framework with replay buffer
• Selective retention of high-entropy samples
• Minimal labeled data for patient adaptation