Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry.
The aim of this study is to assess the utility 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 input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve 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. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.
Marcos
J Víctor
JV
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain. jvmarcos@gmail.es
Hornero
Roberto
R
Alvarez
Daniel
D
del Campo
Félix
F
Zamarrón
Carlos
C
D000465:Algorithms; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010092:Oximetry / Q000706:statistics & numerical data*; D010100:Oxygen / Q000378:metabolism; D010363:Pattern Recognition, Automated; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000378:metabolism; D013997:Time Factors
D010100:Oxygen