Wearable low-latency sleep stage classifier

A wearable microsystem for low-latency automatic sleep stage classification and REM sleep detection in rodents is presented. The detection algorithm is implemented digitally to achieve low latency and is optimized for low complexity and power consumption. The algorithm uses both EEG and EMG signals as inputs. Experimental results using off-line signals from nine mice show REM detection sensitivity and specificity of 81.69% and 93.83%, respectively, with a latency of 39μs. The system will be used in a non-disruptive closed loop REM sleep suppression microsystem to study the effects of REM sleep deprivation on memory consolidation.