There are two main kinds of REM detection applicable with the Sleeator 2: First, REM detection using external devices and classification, such as the Zeo’s internal REM detection, and second, REM detectors using the raw data or its transformations.
As described above, advantages for the first way are that it is easy to use, needs less resources on the computer for classification, and offers a buy-in of external experience (e.g. from Zeo’s manufacturer). Benefits of the second variant are that it is individually trainable for each subject, adjustable according to one’s own wishes, understandable (“white box” instead of “black box”) and can be further developed.
A REM detector can be selected on the fourth tab. It is also possible to use multiple classifiers simultaneously. Figure to Figure demonstrate how to train an own REM classifier.
There exist numerous machine learning classification algorithms (for an overview, see for example Kotsiantis et al., 2007). In Sleeator 2, three different “standard” algorithm types can be used to build a REM classifier: support vector machines, artificial neural networks, and a k-nearest neighbor classifier. These supervised learning algorithms are programmed to make use of n-fold cross validation. Two more classifier types who simply use thresholds of specific frequency bands as REM detection criterion are also available. After training, the trained classifier and its parameters can be saved into an *.RCL file (RemCLassifier file).