Heuristics for apnea episodes recognition

Abstract

The Sleep Apnea is a respiratory disorder that affects a very significant number of patients, with different ages. One of the main consequences of suffering from apneas is the increase in the risk of stroke onsets. This study is concerned with an automatic identification of apnea episodes using a single triaxial accelerometer placed on the center of the chest. The relevance of this approach is that the devices for home recording and the analysis of the data can be highly reduced, increasing the patient comfort during the data gathering and reducing the time needed for the data analysis. A very simple heuristic has been found useful for identifying this type of episodes. For this study, normal subjects have been evaluated with this approach; it is expected that data from patients that might suffer apneas will be available soon, so the performance of this approach on real scenarios can be reported.