Dickhaus H, Heinrich H
University of Heidelberg/Fachhochschule Heilbronn.
Stud Health Technol Inform. 1997;43 Pt B:541-5.
The purpose of this paper is to outline the fundamental concept of wavelet networks (WNs) and to demonstrate its specific advantages in a clinical discrimination task. A group of 25 boys with attention deficit hyperactivity disorder (ADHD) should be separated from a control group of 25 healthy boys by auditory evoked potentials. Because of the high variability of the recorded EP signals quantification of the averaged sweeps by peak latencies and amplitudes failed. However, with wavelet networks a maximum classification rate of 80% was achieved by crossvalidation. A WN is basically described as a multilayer perceptron which consists of two parts for feature extraction/parametrization and classification. These essential steps of a pattern recognition task are not separated in different tasks but linked together by the clamp of the learning algorithm. Because no user interaction is necessary we call this procedure a self-learning method.
本文的目的是概述小波网络（WNs）的基本概念，并展示其在临床鉴别任务中的具体优势。应通过听觉诱发电位将一组25名患有注意力缺陷多动障碍（ADHD）的男孩与25名健康男孩的对照组区分开来。由于记录的诱发电位（EP）信号变化很大，通过峰值潜伏期和幅度对平均扫描进行量化的方法失败了。然而，利用小波网络，通过交叉验证实现了80%的最高分类率。小波网络基本上被描述为一个多层感知器，它由用于特征提取/参数化和分类的两部分组成。模式识别任务的这些基本步骤在不同任务中不是分开的，而是通过学习算法的钳位联系在一起。由于不需要用户交互，我们将此过程称为自学习方法。
