生物医学信号分类的双重特征提取技术

A. Hazarika, L. Dutta, M. Barthakur, M. Bhuyan
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引用次数: 14

摘要

提出了一种用于生物医学信号分类的双重特征提取技术。在这项工作中,信号被均匀分解成一组均匀矩阵。然后利用典型相关分析(CCA)将每对矩阵映射到正交空间。然后对原始特征矩阵进行小波变换,并将其映射到正交空间;这两个域在统计上是独立的。在每个域中提取相同维度的特征向量,并将它们拼接成单个嵌入向量。将嵌入向量输入到分类器中识别健康对照和病理信号模式。为了证明这一点,我们考虑了肌萎缩侧索硬化(ALS)、肌病(Myo)和健康对照(Nor)的三组肌电图信号。结果表明,采用特征提取技术和特征同步技术可以有效地提高特征模式的质量。采用特征技术对Myo-Nor和ALS-Nor的最佳识别率分别为95.91%±3.6%和95.58%±1.5%。所提出的特征提取方案不仅在精度上保持一致,而且与其他质量评价参数保持一致。因此,它有望为信号分类提供更好的策略工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-fold feature extraction technique for biomedical signals classification
This presents a two-fold feature extraction technique for biomedical signals classification. In this work, signals are uniformly decomposed to form a set of uniform matrices. Then using Canonical Correlation Analysis (CCA) each pair matrices are mapped to orthogonal space. Next, Wavelet transformation is performed on original feature matrices and then mapped to orthogonal space. Both domains are statistically independent. From each domain, same dimensional feature vectors are extracted and concatenated them to form single embedding vectors. The embedding vectors are fed to classifier to recognize the healthy control and pathological signal patterns. For demonstration, we consider three groups of EMG signal vis Amyotrophic lateral sclerosis (ALS), Myopathy (Myo) and healthy control (Nor). Results indicate that adopted feature extraction technique and synchronization of features strongly enhances the quality of feature pattern. The optimum recognition rate under adopted feature technique are obtained 95.91%±3.6% and 95.58%±1.5 % in Myo-Nor and ALS-Nor respectively. The proposed feature extraction scheme is consistent not only in accuracies but also other quality assessment parameters. Hence it promises to provide a better strategic tool for signal classification.
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