E. Hernndez-Pereira, B. Fernndez-Rey, M. Cabrero-Canosa, and V. Moret-Bonillo (Spain)
Artificial intelligence, hypopnea detection, signal process ing, wavelets.
The Sleep Apnea Syndrome (SAS) is characterized by repetitive episodes of breathing pauses (apnea/hypopnea) during sleep. The identification of these pauses is carried out on the airflow signal, establishing a set of intervals with a significant amplitude reduction, a plausible evidence of hypopnea. The accuracy in the definition of these inter vals will determine the success of the subsequent process of diagnosis of the sleep apnea disease. In this paper, we present a new approach in order to overcome this issue that is based on classical signal processing techniques. The re sults obtained improve the results obtained in SAMOA, an intelligent system for the SAS diagnosis.
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