Spectral distance for ARMA models applied to electroencephalogram for early detection of hypoxia
Journal article, 2006
A novel measure of spectral distance is presented, which is inspired by the prediction residual parameter presented by Itakura in 1975, but derived from frequency domain data and extended to include autoregressive moving average (ARMA) models. This new algorithm is applied to electroencephalogram (EEG) data from newborn piglets exposed to hypoxia for the purpose of early detection of hypoxia. The performance is evaluated using parameters relevant for potential clinical use, and is found to outperform the Itakura distance, which has proved to be useful for this application. Additionally, we compare the performance with various algorithms previously used for the detection of hypoxia from EEG. Our results based on EEG from newborn piglets show that some detector statistics divert significantly from a reference period less than 2 min after the start of general hypoxia. Among these successful detectors, the proposed spectral distance is the only spectral-based parameter. It therefore appears that spectral changes due to hypoxia are best described by use of an ARMA- model-based spectral estimate, but the drawback of the presented method is high computational effort.
Swine
*Algorithms
Sensitivity and Specificity
Computer-Assisted/*methods
Electroencephalography/*methods
Newborn
Animals
Automated/methods
Artificial Intelligence
Animals
Pattern Recognition
Reproducibility of Results
Regression Analysis
Diagnosis
Hypoxia
Brain/*diagnosis/*physiopathology
Author
Nils Löfgren
Chalmers, Signals and Systems
Kaj Lindecrantz
University of Borås
Anders Flisberg
University of Gothenburg
Ralph Bågenholm
University of Gothenburg
Ingemar Kjellmer
University of Gothenburg
M. Thordstein
Sahlgrenska University Hospital
Journal of Neural Engineering
1741-2560 (ISSN) 17412552 (eISSN)
Vol. 3 3 227-34 005Subject Categories
MEDICAL AND HEALTH SCIENCES
DOI
10.1088/1741-2560/3/3/005