Abstract

Sleep spindles occurrence correlates with the consolidation of recently acquired information. The memory consolidation literature supports that there are more sleep spindles after a learning task. Thus, the detection of them does not only allow the classification of the N2 sleep stage, further provides a quantification value of memory replay and memory consolidation during sleep. Event detection is an important processing step performed in the analysis of diverse kinds of waveforms. The short and long term average ratio is the most widely used event detection approach to analyze passive seismic data and trigger the storing or discarding of data. Its popularity comes from its simplicity and the usage of a fixed threshold determined by the intention of the data usage and not based on the signal dynamics. This work explores the usage of this event detection approach on the online detection of sleep spindles. The advantages of the detection performance with this feature over using the same binary classification method using other fast calculation features come from its statistical properties. The classification features compared are the root mean square amplitude, relative spindle power, and the Teager-Kayser energy operator.