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Wavelet Neural Network as EMG classifier

机译：小波神经网络作为EMG分类器

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This paper presents the use of a Wavelet Neural Network (WNN) as an efficient classifier of Electromyographic (EMG) signals. Generally, an EMG signal requires advanced methods for detection, decomposition, processing and classification. In this paper a WNN model will relate the firing frequency of motor unit action potentials (MUAPs) and three different muscle force levels, in order to improve the classification process showed by other common processing techniques. Adequate EMG classification provides an important source of information in fields such as the diagnosis of neuromuscular disorders, management rehabilitation and prosthesis control were identify and classify MUAPs is a priority task. Accurate and computational efficient EMG classifier was obtained employing a WNN model; the success classification rate was greater than 90% for original registers and 83.33% in adding 50% of noise. WNN allow the feature extraction of EMG signals while creating a classification model, all in a single step, becoming an innovative data processing tool.
机译：本文介绍了小波神经网络（WNN）作为电拍摄（EMG）信号的有效分类器。通常，EMG信号需要用于检测，分解，处理和分类的高级方法。在本文中，WNN模型将涉及电动机单元动作电位（MUAP）和三种不同肌肉力水平的烧制频率，以改善其他常见处理技术所显示的分类过程。充足的EMG分类提供了诸如神经肌肉障碍的诊断等领域的重要信息来源，管理康复和假肢控制是识别和分类Muaps是一个优先任务。使用WNN模型获得了准确和计算有效的EMG分类器;原始寄存器的成功分类率大于90％，增加了83.33％，增加了50％的噪声。 Wnn允许EMG信号的特征提取在创建分类模型时，所有内容都在一步中成为一个创新的数据处理工具。

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