Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry

Abstract

The aim of this study is to assess the capability of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear features from oxygen saturation (SaO2) signals were used as inputs. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training and test sets with 74 and 113 signals, respectively. Several classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. Statistical pattern recognition techniques evaluated in our study could be applied as an OSA screening tool and could contribute to reduce the number of polisomnographies.