2015 IEEE International Conference on Information Reuse and Integration (IRI) (2015)
San Francisco, CA, USA
Aug. 13, 2015 to Aug. 15, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IRI.2015.57
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.
Databases, Electrocardiography, Forecasting, Monitoring, Time-frequency analysis, Testing
I. De Falco, G. De Pietro and G. Sannino, "On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes," 2015 IEEE International Conference on Information Reuse and Integration (IRI), San Francisco, CA, USA, 2015, pp. 326-333.