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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics
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Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics

机译：迈向在线癫痫发作咨询系统-使用主动学习启发式方法的自适应性癫痫发作预测框架

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In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.
机译：在过去的十年中，癫痫发作预测系统因其巨大的潜力在很大程度上改善了癫痫患者的生活质量而备受关注。在实际应用中检测癫痫发作的预测算法的准确性受到很大的限制，因为大脑信号固有地不确定，并且会受到内部假象的影响，并且受各种因素（例如环境，年龄，药物摄入等）的影响在记录大脑信号的过程中。为了解决这种歧义，研究人员过渡使用主动学习，主动学习选择要由专家注释的模糊数据，并动态更新分类模型。但是，从大型歧义数据集库中选择要由专家标记的特定数据仍然是一个难题。在本文中，我们提出了一种基于主动学习的预测框架，旨在以最少数量的标记数据提高预测的准确性。我们框架的核心技术是使用Bernoulli-Gaussian混合模型（BGMM）来确定最不明确的特征样本，以供专家注释。通过这样做，我们的方法可促进专家干预并提高医疗可靠性。我们根据所需的分类时间和内存评估七个分类器。建立在性能最佳分类器之上的主动学习框架会根据所需的注释工作进行评估，以实现较高的预测准确性。结果表明，我们的方法仅使用20％的标记数据即可达到与支持向量机（SVM）分类器相同的精度，并且即使在嘈杂的条件下也可以提高预测精度。

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