Ritanserin, a selective and potent serotonin-2 antagonist, is effective in the treatment of a variety of syndromes related to anxiety and depression, including dysthymic disorder. In animals and healthy volunteers, ritanserin specifically increases slow-wave sleep and the hypothesis arises that this effect on sleep may contribute to its therapeutic properties. Therefore, we studied the effects of ritanserin on sleep in a group of dysthymic patients (DSM-III). Polygraphic recording as well as subjective evaluations of the quality of sleep were performed before and at the end of a 4-week period of double-blind medication with either ritanserin (10 mg o.d. in the morning) or placebo. At baseline, patients showed at fragmented and superficial sleep, with low amounts of slow wave sleep. Ritanserin significantly increased Slow Wave Sleep and changed the frequency and distribution of some stage transitions during the night. No other sleep parameters were modified by ritanserin treatment.
This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.
Cyclic alternating pattern (CAP) is a neurophysiological pattern that can be visually scored by international criteria. The aim of this study was to verify the feasibility of visual CAP scoring using only one channel of sleep electroencephalogram (EEG) to evaluate the inter-scorer agreement in a variety of recordings, and to compare agreement between visual scoring and automatic scoring systems. Sixteen hours of single-channel European data format recordings from four different sleep laboratories with either C4-A1 or C3-A2 channels and with different sampling frequencies were used in this study. Seven independent scorers applied visual scoring according to international criteria. Two automatic blind scorings were also evaluated. Event-based inter-scorer agreement analysis was performed. The pairwise inter-scorer agreement (PWISA) was between 55.5 and 84.3%. The average PWISA was above 60% for all scorers and the global average was 69.9%. Automatic scoring systems showed similar results to those of visual scoring. The study showed that CAP could be scored using only one EEG channel. Therefore, CAP scoring might also be integrated in sleep scoring features and automatic scoring systems having similar performances to visual sleep scoring systems.
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