The last decades the explosion in diverse domains of IT technology so in hardware as in software set new horizons for further research in many fields or defined from the ground whole new scientific disciplines. Bioinformatics and biomedicine definitely took advantage of tech explosion. As a consequence, sleep medicine is one of those new research areas that arose. This study tries to reproduce, extend and optimize state of the art technics for automatic sleep microstructure analysis. Especially focuses on the Cycling Alternating Pattern (CAP) and the detection of a CAP’s prominent feature, the A-phase. An algorithm is reproduced according to state of the art techniques and experimental approaches are tested to classify records between Non A-phase and A-phase events achieving 91% accuracy, 75% sensitivity and 91% specificity. As the results are competitive compared to other studies employing the same dataset (Physionet CAP Sleep database), it is believed that the applied techniques could contribute to sleep research.
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