Discovering metric temporal constraint networks on temporal databases.

Objective: In this paper, we propose the ASTPminer algorithm for mining collections of time-stamped sequences to discover frequent temporal patterns, as represented in the simple temporal problem (STP) formalism: a representation of temporal knowledge as a set of event types and a set of metric temporal constraints among them. To focus the mining process, some initial knowledge can be provided by the user, also expressed as an STP, that acts as a seed pattern for the searching procedure. In this manner, the mining algorithm will search for those frequent temporal patterns consistent with the initial knowledge.

The mean value of the descriptors of the pathological events recorded on the polysomnogram as a support tool in the diagnosis of SAHS.

Commercial polysomnographs used in the test to diagnose Sleep Apnea-Hypopnea Syndrome (SAHS) usually generate a report summarizing the test. This report helps clinicians in the task of diagnosing the patient. Some of the information presented in these reports has been carefully studied and standardized -for example, the apnea-hypopnea index.

Fuzzy structural algorithms to identify and characterize apnea and hypopnea episodes.

We present a new automated method to identify apneas and hypopneas and to relate them to the drops in blood oxyhemoglobin saturation that they produce. The algorithm takes advantage of the fuzzy set theory for the representation and manipulation of the vagueness of the medical knowledge on which it is based. Its structural nature allows us to easily modify the morphological detection criteria, and to perform a detailed characterization of the identified events.