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 (A(z)); the nonlinear S2SP classifier provided 0.95 in A(z) value using the Gaussian kernel, and possessed good robustness around the selected kernel parameter.
Mu
Tingting
T
Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, L69 3GJ Liverpool, UK. tingting.mu@liverpool.ac.uk
Nandi
Asoke K
AK
Rangayyan
Rangaraj M
RM
D000465:Algorithms; D006801:Humans; D007719:Knee Joint / Q000502:physiology*; D014732:Vibration