Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans Export

@article{song2004, abstract = {The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage \‘awake.\’ The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of \%RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome.}, author = {Song, In-Ho and Lee, Doo-Soo and Kim, Sun I.}, citeulike-article-id = {334897}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neulet.2004.05.025}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6T0G-4CS4RYG-1/2/8905eaea0e1371e977c5aeed190bdebe}, day = {12}, doi = {10.1016/j.neulet.2004.05.025}, journal = {Neuroscience Letters}, keywords = {recurrence}, month = {August}, number = {2}, pages = {148--153}, posted-at = {2008-07-31 16:32:00}, priority = {2}, title = {Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans}, url = {http://dx.doi.org/10.1016/j.neulet.2004.05.025}, volume = {366}, year = {2004} }

TY - JOUR ID - song2004 L3 - citeulike-article-id:334897 N2 - The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage ‘awake.’ The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of %RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome. IS - 2 JF - Neuroscience Letters EP - 153 TI - Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans VL - 366 SP - 148 KW - recurrence AU - Song, In-Ho AU - Lee, Doo-Soo AU - Kim, Sun PY - 2004/08/12/ UR - http://dx.doi.org/10.1016/j.neulet.2004.05.025 ER -