A 0.21 μJ patient-specific REM/Non-REM sleep classifier for Alzheimer patients

摘要

An electroencephalograph (EEG) based classification processor for Random Eye-movement (REM) and Non-REM (NREM) sleep detection for Alzheimer patients is presented. To enable a home-based wearable polysomnography, a single-channel-EEG, with the novel feature extraction (FE) approach and the machine-learning classifier is utilized. The 4-dimenional Feature Vector (FV) is based on the spectral edge frequency (SEF), low-to-high spectral content, and rapid-eye-movement detection. To utilize SEF efficiently on hardware, a 16-point FFT is proposed that achieves an area reduction of 43% compared to the conventional with minimal overhead in the precision loss. A Linear Support Vector Machine (LSVM) is utilized to decide between REM and NREM based on the FV. The system is synthesized using 0.18μm process and experimental verification is done using FPGA based on the subset of patients from the PhysioNet Sleep database. The proposed classification processor achieves energy-efficiency of 0.21J/classification with a sensitivity and specificity of 89.8% and 93.6% respectively.