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Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

机译：基于静止小波的二维二维主成分分析的肌电信号分类

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Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.
机译：离散小波变换（WT）和主成分分析（PCA）成为分析生物医学信号的有效方法。通常将各种尺度和通道的小波系数转换为一维数组，从而引起诸如维数困境的诅咒和样本量小的问题。此外，WT系数缺乏时移不变性可以建模为噪声，从而降低了分类器的性能。在这项研究中，我们提出了一种基于平稳小波的二维二维主成分分析（SW2D2PCA）方法，用于从信号中高效提取有效特征信息。第一步，构造时不变的多尺度矩阵。然后，二维二维主成分分析将在多尺度矩阵上进行操作以减小维数，而不是传统PCA中的矢量。结果来自一项实验，该实验使用健康受试者和截肢者中记录的4通道肌电图（EMG）信号对八种手部运动进行了分类，从而说明了所提出的生物医学信号分析方法的效率和有效性。

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