Mining statistically significant associations for exploratory analysis of human sleep data.
We introduce a specialized association rule mining technique that can extract patterns from complex sleep data comprising polysomnographic recordings, clinical summaries, and sleep questionnaire responses. The rules mined can describe associations among temporally annotated events and questionnaire or summary data; e.g., the likelihood that an occurrence of a rapid eye movement (REM) sleep stage during the second 100 sleep epochs of the night is associated with moderate caffeine intake. We use chi2 analysis to ensure statistical significance of the mined rules at the level P < 0.05. Our results, obtained by mining sleep-related data from 242 human subjects, reveal clinically interesting associations among the polysomnographic and summary variables. Our experience suggests that association mining may also be useful for selection of variables prior to using logistic regression.
Laxminarayan
Parameshvyas
P
iProspect.com, Watertown, MA 02472, USA. parameshl@yahoo.com
Alvarez
Sergio A
SA
Ruiz
Carolina
C
Moonis
Majaz
M
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012890:Sleep; D012893:Sleep Wake Disorders / Q000175:diagnosis* / Q000503:physiopathology*; D013997:Time Factors