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

Authors:
Tingting Mu;Asoke K. Nandi;Rangaraj M. Rangayyan
Affiliations:
Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, L69 3GJ Liverpool, UK;Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, L69 3GJ Liverpool, UK;Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada T2N 1N4
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

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.