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.