Screening of knee-joint vibroarthrographic signals using the strict 2-surface proximal classifier and genetic algorithm

We propose the strict 2-surface proximal (S2SP) classifier, by seeking two cross proximal planes to fit the distribution of the given samples in a corresponding feature space. The method is applied to screen knee-joint vibration or vibroarthrographic (VAG) signals based on statistical parameters derived from signals and selected by the genetic algorithm. A database of 89 VAG signals was studied. With the leave-one-out procedure, the linear S2SP classifier provided an efficiency of 0.82 in terms of the area under the receiver operating characteristics curve (AzAz); the nonlinear S2SP classifier provided 0.95 in AzAz value using the Gaussian kernel, and possessed good robustness around the selected kernel parameter.