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首页> 外文期刊>Future generation computer systems >Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes
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Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes

机译：基于进化的深度神经网络配置优化，用于阻塞性睡眠呼吸暂停发作分类

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摘要

Deep Neural Networks (DNNs) may be very effective for the classification over highly-sized data sets, especially in the medical domain, where the recognition of the occurrence of a specific event related to a disease is of high importance. Unfortunately, DNNs suffer from the drawback that a good set of values for their configuration hyper-parameters must be found. Currently, this is done through the use of either trial-and-error methods or sampling-based ones. In this paper we propose a new approach to find the most suitable structure for a DNN used for a classification problem in terms of achievement of the highest classification accuracy. This approach is based on a distributed version of Differential Evolution (DE), a variety of an Evolutionary Algorithm. To evaluate the approach, in this paper we investigate this issue with reference to Obstructive Sleep Apnea (OSA). OSA is an important medical problem consisting of episodes taking place during night in which a subject stops breathing due to a constriction of the upper airways. This deteriorates the quality of life and may have dangerous, and even lethal, consequences on both short and long term. An accurate classification is a very crucial step for the OSA treatment, because understanding automatically that a subject is experiencing such an episode may be decisive if prompt medical action is needed. In our experiments, classification takes place on a data set in which each item contains the values of 17 Heart Rate Variability parameters, extracted from ElectroCardiography signals, and the annotation of OSA events. We have extracted this data set from the real-world Sleep Heart Health Study database. The results obtained by the distributed DE are compared against those of the Grid Search as well as against those achieved by 13 well-known classification tools. The use of a distributed DE version turns out to be very effective in automatically obtaining DNN structures with higher classification accuracy with respect to Grid Search (72.95% versus 72.61%), and allows saving a high amount of time (three hours as opposed to 65 h and 40 min). Moreover, the proposed method outperforms in terms of higher accuracy all the other classifiers investigated, as it is evidenced also by statistical analysis. Numerically, the runner-up, i.e., JRip, achieves as its best value 72.01% and 71.50% on average over 25 runs, both values being lower than 72.95% and 72.74% obtained by our dDE.
机译：深度神经网络（DNN）对于大型数据集的分类可能非常有效，尤其是在医学领域，其中识别与疾病相关的特定事件的发生非常重要。不幸的是，DNN的缺点是必须为其配置超参数找到一组好的值。目前，这是通过使用试错法或基于采样的方法来完成的。在本文中，我们提出了一种新方法，以实现最高分类精度的方式为分类问题找到最合适的DNN结构。这种方法基于差分进化（DE）的分布式版本，该算法是多种进化算法。为了评估该方法，在本文中我们参考阻塞性睡眠呼吸暂停（OSA）对此问题进行了调查。 OSA是一个重要的医学问题，包括在夜间发生的发作，在该发作中，受试者由于上呼吸道收缩而停止呼吸。这会降低生活质量，并可能对短期和长期造成危险甚至致命的后果。准确的分类是OSA治疗的非常关键的一步，因为如果需要迅速的医疗行动，自动了解对象正在经历这种发作可能是决定性的。在我们的实验中，分类是在一个数据集上进行的，其中每个项目包含从心电图信号中提取的17个心率变异性参数的值以及OSA事件的注释。我们从现实世界睡眠心脏健康研究数据库中提取了该数据集。将分布式DE获得的结果与Grid Search的结果以及通过13种著名分类工具获得的结果进行比较。事实证明，使用分布式DE版本非常有效地自动获得相对于Grid Search具有更高分类精度的DNN结构（分别为72.95％和72.61％），并且可以节省大量时间（3小时而不是65小时）小时和40分钟）。此外，建议的方法在更高的准确性方面优于所有其他研究的分类器，这也得到了统计分析的证明。从数字上讲，亚军，即JRip，在25次测试中平均达到其最佳值72.01％和71.50％，这两个值均低于我们dDE获得的72.95％和72.74％。

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