Rocha T, Paredes S, Carvalho P, Henriques J, Harris M
Departamento de Engenharia Informática e de Sistemas, Instituto Superior de Engenharia de Coimbra, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2403-6. doi: 10.1109/IEMBS.2010.5626115.
This paper presents a generic methodology for time series prediction, based on a wavelet decomposition/ reconstruction technique, together with a feedforward neural networks structure. The proposed methodology combines the flexibility and learning abilities of neural networks with a compact description of the signals, inherent to wavelets. In a first phase a wavelet decomposition of the signal is performed, providing a small number of coefficients that summarizes signal time evolution dynamics. The prediction problem is then effectively addressed by means of a neural networks model, previously trained using coefficients of the training dataset. The particular problem of forecasting acute hypotensive episodes (AHE) occurring in intensive care units was used to prove the effectiveness of the proposed strategy. The dataset, extracted from MIMIC-II, was made available in the context of the PhysioNet-Computers in Cardiology Challenge 2009. Results attained in this work were similar to the best ones achieved under that challenge.
本文提出了一种基于小波分解/重构技术以及前馈神经网络结构的时间序列预测通用方法。所提出的方法将神经网络的灵活性和学习能力与小波固有的信号紧凑描述相结合。在第一阶段，对信号进行小波分解，得到少量概括信号时间演变动态的系数。然后通过一个先前使用训练数据集的系数进行训练的神经网络模型有效地解决预测问题。利用重症监护病房中发生的急性低血压发作（AHE）预测这一特定问题来证明所提策略的有效性。从MIMIC-II中提取的数据集在2009年心脏病学挑战生理网-计算机竞赛的背景下提供。这项工作所取得的结果与该竞赛中取得的最佳结果相似。
