Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

PeerJ

article

Adriano Carafini¹  Isabel C.N. Sacco²  Marcus Fraga Vieira¹ 

[1] Bioengineering and Biomechanics Laboratory, Universidade Federal de Goiás;Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, Universidade de São Paulo

关键词: Urinary Incontinence;    Pelvic Floor;    Pressure Distribution;    Classification;    Feature Selection;    Branch and Bound;    k-Nearest Neighbors;    Support Vector Machine;    Logistic Regression;    Recursive Feature Elimination;   

DOI  :  10.7717/peerj.8207

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundPelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women.MethodsThe available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression.ResultsOf the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.

【 授权许可】

CC BY