预算约束下的个性化：面向资源高效癫痫检测的最小标注持续学习
Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection
AI总结：
本文提出持续学习框架EpiSMART，通过受限回放缓冲区和知情样本选择策略，在最小标注数据下实现患者个性化癫痫检测，有效避免灾难性遗忘，适用于可穿戴系统实时部署。
摘要：
目的：癫痫是一种常见的神经系统疾病，需要仔细的诊断和持续的护理。癫痫检测仍然具有挑战性，因为当前的临床实践依赖于对脑电图（EEG）的专家分析，这是一个耗时的过程且需要专业知识。为了应对这一挑战，本文探索使用深度学习进行自动癫痫发作检测，重点关注个性化的持续学习模型，该模型能够适应每个患者独特的脑电信号特征，这些特征会随时间演变。方法：在此背景下，我们的方法解决了将新数据集成到现有模型中而不发生灾难性遗忘的挑战，这是静态深度学习模型中的常见问题。我们提出了EpiSMART，这是一个用于癫痫检测的持续学习框架，它使用大小受限的回放缓冲区和知情的样本选择策略，以增量方式适应患者特定的脑电信号。通过选择性地保留高熵和预测为癫痫发作的样本，我们的方法保留了关键的过去信息，同时在最小的内存和计算需求下保持高性能。结果：在CHB-MIT数据集上的验证表明，与在所有其他患者中未经更新的已训练基线相比，EpiSMART的F1分数提高了21%。平均而言，EpiSMART每天仅需6.46分钟的标注数据和6.28次更新，使其适用于可穿戴系统的实时部署。结论：EpiSMART通过有效地将新数据集成到现有模型中而不降低过去知识，在现实和资源受限的条件下实现了稳健且个性化的癫痫检测。意义：该框架通过提供支持患者特定适应和可穿戴医疗系统中实际部署的持续学习方法，推进了自动癫痫检测的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
