Artificial Neural Network Based Sleep Disordered Breathing Screening Tool

摘要

The present disclosure provides systems and methods for determining the presence and severity of sleep disordered breathing in a patient based on the output of a low-cost at-home diagnostic and the results of a health questionnaire. The low-cost at-home diagnostic is a simple photoplethysmographic survey to detect oxygen saturation overnight. Minimum oxygen saturation and other metrics are determined from the photoplethysmographic survey and applied, in combination with the health questionnaire data, to a set of artificial neural networks. Each artificial neural network corresponds to a respective degree of severity of sleep disordered breathing, according to rate of occurrence of apnea and hypopnea events during sleep. Each artificial neural network is trained with a respective subset of clinical data generated from a large population of individuals, to reduce both the false positive and false negative rate of the classifier.