  Stimulation of target neuronal populations using optogenetic techniques
during specific sleep stages has begun to elucidate the mechanisms and effects
of sleep. To conduct closed-loop optogenetic sleep studies in untethered
animals, we designed a fully integrated, low-power system-on-chip (SoC) for
real-time sleep stage classification and stage-specific optical stimulation.
The SoC consists of a 4-channel analog front-end for recording polysomnography
signals, a mixed-signal machine-learning (ML) core, and a 16-channel optical
stimulation back-end. A novel ML algorithm and innovative circuit design
techniques improved the online classification performance while minimizing
power consumption. The SoC was designed and simulated in 180 nm CMOS
technology. In an evaluation using an expert labeled sleep database with 20
subjects, the SoC achieves a high sensitivity of 0.806 and a specificity of
0.947 in discriminating 5 sleep stages. Overall power consumption in continuous
operation is 97 uW.
