DEEP LEARNING BASED SLEEP APNEA SYNDROME PORTABLE DIAGNOSTIC SYSTEM AND METHOD

摘要

A system and method for detection of sleep apnea in a subject comprises first and second sensors providing, to the processor of a mobile device, biological data from among pulse oximeter data, heart rate data, electrocardiogram data and nasal pressure data. The processor is configured to detect sleep apnea in a subject by receiving the biological data, converting the biological data into a scalogram, and determining a sleep apnea diagnosis via a classification unit based on the scalogram and based on a diagnostic model that was built based on deep learning of the biological data and corresponding diagnosis in the training dataset. The processor is configured to output, by an output device, the sleep apnea diagnosis, facilitating home diagnosis of sleep apnea.