LAUSR

OBJECTIVE To design a diagnostic support system for sleep apnoea and hypopnoea syndrome (SAHS) using moving average based on knowledge, able to identify SAHS episodes from a respiratory polygraphy (RP) database. METHODS An analysis was made of data obtained from a public database, that included the RP signals, nasobucal airflow, thoracoabdominal movement, and pulse oximetry of 23 patients between 28 and 68 years with suspected SAHS, and with a body mass index (BMI) from 25.1 to 42.5. RESULTS The identification and classification of episodes of apnoea and hypopnoea was obtained. CONCLUSIONS The algorithm designed identified episodes of SAHS using polygraphy signals, which by implementating in a graphical interface allows visualisation of onset, duration, type, oxygen saturation, and pulse oximetry of each episode, and can be used as a support tool for the diagnosis of sleep disorders.