This was based on the assumption that this minimum value was a good estimate of the local level of noise that affects all values within such a 60-s period. The noise reduction correction made the REM sleep atonia index more easily applicable and suited for the analysis of larger number of recordings. Analysis of the corrected index in a large group of 89 subjects (young controls, aged controls, untreated and treated iRBD, MSA, obstructive sleep apnea syndrome (OSAS)) showed that the corrected index was numerically higher compared with the original index but distinguished reliably between patients and controls, affording the establishment of clinical cut-off values. A further automatic method was proposed by Mayer et?al.6 They computed a smoothed PI3K Inhibitor Library cell line
upper and lower amplitude envelope of the chin EMG Fossariinae
over NREM and REM epochs and defined amplitude as the difference between these two amplitudes. A threshold curve was constructed as twice the amplitude curve smoothed over 200?s and muscle activity during REM was defined as all activity with amplitudes above the threshold curve. From these computations mean amplitudes per 1-s mini-epochs and per sleep stage were computed for descriptive purposes. In addition, short (<0.5?s) and long (��0.5?s) muscle activity was distinguished. The results of the <a href="http://www.selleckchem.com/products/Sunitinib-Malate-
(Sutent).html">Sunitinib clinical trial automated quantification were not compared with visual scoring but the number of short and long muscle activities was higher in patients with RBD than controls. A very different approach to the analysis of chin EMG during REM was published by Shokrollahi et?al.7 In contrast to the other algorithms, they did not aim to quantify muscle activity during REM sleep directly but were interested in separating fixed-length segments of chin EMG during REM sleep of four RBD patients from those of four subjects without RBD. To that end they used linear discriminant analysis of the coefficients of a continuous wavelet transform applied to the approximation coefficients of a one-level discrete wavelet transform (for details see Table?4). They report that 97.5% of the segments from control subjects were classified as ��normal�� whereas 91.3% of segments of RBD patients were classified as ��abnormal�� with an overall accuracy of 94.3%. Besides the lack of direct quantification of loss of atonia, also the assumption that all segments of REM sleep in RBD will contain muscle activity makes this approach difficult to apply in clinical practice and research. A further approach aiming at classifying rather than quantifying muscle activity during REM sleep was proposed by Kempfner et?al.