RESUMO
One of the key features of rapid-eye movement (REM) sleep is the presence of bursts of REMs. Sleep studies routinely use REMs to classify sleep stages. Moreover, REM count or density has been used in studies involving learning and various psychiatric disorders. Most of these studies have been based on the visual identification of REMs, which is generally a very time-consuming task. This and the varying definitions of REMs across scorers have warranted the development of automatic REM detection methodologies. In this paper, we present a new detection scheme that combines many of the intrinsic properties of REMs and requires minimal parameter adjustments. In the proposed method, a single parameter can be used to control the REM detection sensitivity and specificity tradeoff. Manually scored training data are used to develop the method. We assess the performance of the method against manual scoring of individual REM events and present validation results using a separate data set. The ability of the method to discriminate fast horizontal ocular movement in REM sleep from other types of events is highlighted. A key advantage of the presented method is the minimal a priori information requirement. The results of training data (recordings from five subjects) show an overall sensitivity of 78.8% and specificity of 81.6%. The performance on the testing data (recording from five subjects different from the training data) showed overall sensitivity of 67.2% and specificity of 77.5%.
