癫痫患者的自动标注早期预警预测
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
AI总结：
本文提出一种基于元学习的双层优化框架，利用癫痫患者EEG数据自动标注早期发作期噪声数据并提升骨干模型训练精度，实验表明其预测准确率显著优于LSTM和ResNet基线，且预测概率可作为高效早期预警指标。
摘要：
对癫痫患者进行早期预警对其安全和健康至关重要，尤其是为了预防或尽量减轻癫痫发作的严重程度。基于患者的脑电图（EEG）数据，我们提出了一种元学习框架，以改进对早期发作期信号的预测。所提出的双层优化框架能够帮助自动标注早期发作阶段的噪声数据，同时优化骨干模型的训练精度。为验证我们的方法，我们开展了一系列实验，在不同的长期时间窗口中预测癫痫发作起始，并以LSTM和ResNet作为基线模型。我们的研究表明，元学习所获得的发作期预测准确率不仅显著提升，而且所得模型能够捕捉到单一骨干模型无法学习的噪声数据中的某些内在模式。因此，元网络生成的预测概率可作为一种高效的早期预警指标。
英文摘要：
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we propose a meta learning framework to improve the prediction of early ictal signals. The proposed bi-level optimization framework can help automatically label noisy data at the early ictal stage, as well as optimize the training accuracy of the backbone model. To validate our approach, we conduct a series of experiments to predict seizure onset in various long-term windows, with LSTM and ResNet implemented as the baseline models. Our study demonstrates that not only the ictal prediction accuracy obtained by meta learning is significantly improved, but also the resulting model captures some intrinsic patterns of the noisy data that a single backbone model could not learn. As a result, the predicted probability generated by the meta network serves as a highly effective early warning indicator.
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