Currently, Sleep Apnea-Hypopnea Syndrome (SAHS) is accurately diagnosed in Sleep Units. In the last decade, in order to reduce the burden for health systems and the consequent impact in patients, several home-located methods based on the binary classification of SAHS using overnight pulse oximetry (SpO2) have been proposed. Binary classifiers give rise to higher accuracies, but the cost of misclassifying leads to high penalizations in terms of either health care costs or risks in patient’s health.
This study presents a novel hierarchical classification scheme for the four-class SAHS diagnosis using a set of features extracted from SpO2 and reported in specialized literature. An accuracy of 82.6% was achieved in the assessment of the four-class classification. The proposed method could be useful in the diagnosis of SAHS in an ambulatory home-based setting and could alleviate under-diagnosis rate and the waiting lists in sleep units.
Keywords SAHS; Overnight pulse-oximetry; Sleep apnea; Binary hierarchical classifier; SpO2; Multiclass; Multivariate