| 小波包变换特征提取与表面肌电分类 |
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| 引用本文： | 谢洪波,王志中,黄海. 小波包变换特征提取与表面肌电分类[J]. 医疗卫生装备, 2003, 24(9): 7-8,10 |
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| 作者姓名： | 谢洪波 王志中 黄海 |
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| 作者单位： | 上海交通大学生物医学工程系,上海市,200030 |
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| 基金项目： | 国家自然科学基金项目(编号:60171006)。 |
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| 摘 要： | 针对表面肌电(SEMG)的非平稳特性，提出采用小波包变换方法对其进行分类。分析了特征提取方法并采用小波包变换各频段能量构造特征矢量，经过学习矢量量化神经网络训练能够有效地从伸肌和屈肌采集的两道肌电信号中识别伸拳，展拳，腕内旋，腕外旋4种运动模式，平均识别率为94．5％。与其它时频分析方法比较，该方法不仅识别率高，鲁棒性好，也为其他非平稳生理信号分析提供了新手段。
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| 关 键 词： | 小波包变换 表面肌电信号 学习矢量量化 时频分析 神经网络训练 |
| 文章编号： | 1003-8868(2003)09-0007-03 |
Wavelet packet transformation feature extraction and surface EMG signal classification |
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| XIE Hong-bo,WANG Zhi-zhong,HUANG Hai. Wavelet packet transformation feature extraction and surface EMG signal classification[J]. Chinese Medical Equipment Journal, 2003, 24(9): 7-8,10 |
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| Authors: | XIE Hong-bo WANG Zhi-zhong HUANG Hai |
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| Abstract: | A surface electromyography (SEMG) signal classification method based on wavelet packet transformation (WPT) is presented in this paper. The feature extraction method is analyzed. The energies in different frequency bands selected as robust feature vectors, four types of forearm movement are identified through learning vector quantization neural network. Compared with other time-frequency analysis method, this method has a higher identification rate and great potential in analyzing other non-stationary physiological signals. |
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| Keywords: | wavelet packet transformation EMG time-frequency analysis learning vector quantization neural network pattern classification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
