Authors:
A. P. Lemos
;
C. J. Tierra-Criollo
and
W. M. Caminhas
Affiliation:
Universidade Federal de Minas Gerais, Brazil
Keyword(s):
Obstructive sleep apnea, RR interval time series, Time series novelty detection.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This work proposes a methodology to screen obstructive sleep apnea (OSA) based on RR interval time series using a time series novelty detection technique. Initially, the RR interval is modeled using an autoregressive model. Next, for each data point of the time series, the model output, xˆ(t ), is compared with the observed value, xt , and the prediction error is generated. The prediction error is then processed in order to detect novelties. Finally, the novelties detected are associated with apnea events. This methodology was applied to the Computers in Cardiology sleep apnea test data and correctly classified 29 out of 30 cases (96.67%) of both OSA and normal subjects, and correctly identified the presence of apnea events in 14078 out of 17268 minutes (81.53%) of the test data set.