Linked Life Data

19428545

Source:http://linkedlifedata.com/resource/pubmed/id/19428545

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2009-5-11
pubmed:abstractText
Arousals are considered one of the main causes of daytime sleepiness. They impede the proper flow of sleep cycles and cause weariness. Manual scoring of arousals is time-consuming, requires expert knowledge, and has high inter-scorer variability. A major difficulty in detecting arousals automatically is the existing variance across patients. Based on data mining techniques, we present a different approach to the automatic detection of arousals that overcomes the hurdle of differences in signal characteristics across patients. Offline we used a training-set of adult patients to define a set of general rules to detect arousals (termed meta-rules). This was done by analyzing the correlations between occurrences of arousals and the EEG, EMG, pulse and SaO2 signals as follows: (1) each signal was mathematically projected into several spaces (termed projected-signals); (2) from each such projected-signal, the algorithm extracted time points that indicated meaningful changes (termed critical-points); (3) data mining techniques were applied to all the critical-points to discover patterns of repeating behavior; (4) classes of patterns which were highly correlated with manually scored arousals were formalized as meta-rules. Online we used a test-set of adult patients from two other different sleep laboratories. Using the meta-rules, the algorithm extracted individual rules for each patient (termed actual-rules), and used them to automatically detect the patients' arousals. These arousals were significantly correlated (R=0.88, p<0.0001; sensitivity=75.2%, positive predictive value=76.5%) with those detected manually by experts. Since the total number of arousals is a measure of sleep quality, this algorithm constitutes a novel approach to automatically estimate sleep quality.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1872-678X
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
179
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
331-7
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Data mining techniques for detection of sleep arousals.
pubmed:affiliation
Department of Computer Science and Mathematics, Bar-Ilan University, Ramat-Gan 52900, Israel. oren.shmiel@live.biu.ac.il
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't