title: Evaluation of Karhunen-Loève expansion for feature selection in computer-assisted classification of bioprosthetic heart-valve status.
abstract: This paper analyses the performance of four different feature-selection approaches of the Karhunen-Loève expansion (KLE) method to select the most discriminant set of features for computer-assisted classification of bioprosthetic heart-valve status. First, an evaluation test reducing the number of initial features while maintaining the performance of the original classifier is developed. Secondly, the effectiveness of the classification in a simulated practical situation where a new sample has to be classified is estimated with a validation test. Results from both tests applied to a reference database show that the most efficient feature selection and classification (> or = 97% of correct classifications (CCs)) are performed by the Kittler and Young approach. For the clinical databases, this approach provides poor classification results for simulated 'new samples' (between 50 and 69% of CCs). For both the evaluation and the validation tests, only the Heydorn and Tou approach provides classification results comparable with those of the original classifier (a difference always < or = 7%). However, the degree of feature reduction is particularly variable. The study demonstrates that the KLE feature-selection approaches are highly population-dependent. It also shows that the validation method proposed is advantageous in clinical applications where the data collection is difficult to perform.
authors.affiliation: Laboratory of Biomedical Engineering, Clinical Research Institute of Montreal, Quebec, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Yazdanpanah
authors.forename: L
authors.initials: L
authors.lastname: Allard
authors.forename: L G
authors.initials: LG
authors.lastname: Durand
authors.forename: R
authors.initials: R
authors.lastname: Guardo
mesh_terms: D001705:Bioprosthesis; D005069:Evaluation Studies as Topic; D006350:Heart Valve Prosthesis; D006801:Humans; D010363:Pattern Recognition, Automated; D011475:Prosthesis Failure; D012815:Signal Processing, Computer-Assisted