Wavelet Singularity Analysis for CAP Sleep Delineation Sleep is an essential process in our life, which covers 13 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (Aphases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of Aphases. The results showed that EEG signal presents significant differences between Aphases and activity of background when the average singularity is considered. This finding can help both in better delineating the Aphases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.

Automated Sleep Spindle Detection System using PeriodAmplitude Analysis Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of nonrapid eye movement (NREM) sleep. In the present study a periodamplitude analysis method was applied for the automated detection of sleep spindles in allnight sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual halfwaves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each halfwave depending on where it lies in the amplitudefrequency plane. The grading is followed by the detection system, checking consecutive halfwave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.