On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes

Obstructive Sleep Apnea (OSA) is a breathing disorder that takes place during sleep, and has both short -- as well as long -- term consequences on patient's health. Real -- time monitoring for a patient can be carried out by making use of ElectroCardioGraphy (ECG) recordings. This paper introduces a methodology to forecast OSA events in the minutes following the current time instant. This is accomplished by using a tool based on Differential Evolution that is able to automatically extract offline knowledge about the monitored patient as a form of a set of IF -- THEN rules. These rules connect the values of some ECG-related parameters recorded in the last minutes the occurrence of an apnea episode in the following minute. This approach has been tested on a literature database with 35 OSA patients. A comparison against six well-known classifiers has been performed.