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

机译：基于静止的小波的双向二维主成分分析，用于EMG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），然后是主成分分析（PCA）是对生物医学信号分析的强大方法。各种刻度和通道的小波系数通常被转换为一维阵列，导致诸如诸如维度困境和小样本尺寸问题的诅咒之类的问题。此外，WT系数的缺乏时间转移不变性可以被建模为噪声并降低分类器性能。在这项研究中，我们介绍了一种基于静止的小波的双向二维主成分分析（SW2D2PCA）方法，用于从信号中有效和有效地提取基本特征信息。在第一步中构建了时间不变的多尺度矩阵。然后，双向二维主成分分析在多尺度矩阵上操作以减小维度，而不是传统PCA中的vircor。结果由实验中提出，使用在健康受试者和术语中记录的4通道电焦（EMG）信号进行分类八个手动动作，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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