This work proposes a new technique for the automatic detection of electroencephalographic (EEG) arousals in sleep polysomnographic recordings. We have developed a non-computationally complex algorithm with the idea of providing an easy integration into different software platforms. The approach combines different well-known signal analyses to identify relevant arousal patterns. Special emphasis is carried out to produce a robust, artifact tolerant algorithm. The resulting approach was tested

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... ng a database of 6 polysomnographic recordings from real patients, achieving an average kappa index of 0.77 with respect to the visual scorings made by clinical experts.