Comparative analysis between SVM & KNN classifier for EMG signal classification on elementary time domain features

Yogesh Paul, Vibha Goyal, R. Jaswal
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引用次数: 35

Abstract

The extraction of the feature is a significant method to extract the useful information which is hidden in the signal acquired form the types of different. These signals may be speech, EEG, EMG, ECG, EOG etc. Here, within this paper, we carry on further with EMG signal to discuss the comparative analysis in between linear SVM and KNN classifier using time domain features. For the purpose of successful classification of EMG signal, careful selection of feature is required. Within this paper, seven elementary time domain features are realized as they are frequently used for the same.
基于初等时域特征的SVM与KNN分类器肌电信号分类的比较分析
特征提取是提取隐藏在不同类型信号中的有用信息的重要方法。这些信号可以是语音、EEG、EMG、ECG、EOG等。在本文中,我们进一步对肌电信号进行了时域特征的线性支持向量机和KNN分类器的对比分析。为了成功地对肌电信号进行分类,需要仔细选择特征。本文实现了常用的7个基本时域特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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