A Single Channel EEG-based All AASM Sleep Stages Classifier for Neurodegenerative Disorder

Abstract

Sleep stages classification is an effective tool for the diagnosis and treatment of neurodegenerative disorders. This paper presents the first non-invasive electroencephalograph (EEG)-based processor for classifying all the sleep stages implemented on hardware. It utilizes a single EEG channel and multi-machine-learning classifiers to form a home-based polysomnography. These multiple one-vs-one binary Linear Support Vector Machine (LSVM) classifiers are combined to classify all the sleep stages using two features only. To implement the desired features efficiently on hardware, an exponent-eliminate (EE) Split-Radix 256-point FFT is proposed that decreases the area by 60% compared to the conventional design by avoiding the majority of complex floating-point multiplications and divisions. The proposed all sleep stages classification system is implemented using 180nm CMOS process and experimentally verified using FPGA based on the EEG recordings of 197 patients from Physionet Sleep database. It utilizes a miniaturized active area of 0.32mm2 and achieves a Cohen Kappa score of 0.847 while consuming 0.7µJ/classification.