Mining associations over human sleep time series

We introduce an association rule mining technique for complex datasets described by both static and time-dependent attributes, and apply this technique to find associations among sleep questionnaire responses, clinical summary information, and all-night polysomnographic recordings of sleeping human subjects. Questionnaire data and clinical summaries comprised a total of 63 variables including gender, age, body mass index, Epworth and depression scores. The Rechtschaffen and Kales (R&K) sleep staging information that is standard in sleep research was extracted from the polysomnographic data, and the polysomnographic signals were discretized. The resulting preprocessed polysomnographic data attributes consist of 6 time sequences: sleep stage, airway pressure, blood oxygen potential, heart rate, apneaic episodes and desaturation events, and the patient´s body position. An extension of the Apriori association rule mining algorithm designed to deal with time-varying sequences using time windows was developed and employed to uncover statistically significant (P<0.01) and clinically meaningful associations among summary and polysomnographic time series variables.