부산대학교 도서관

Identifying sleep stages is a fundamental step for both early detection of disease and neuroscientific exploration. Automatic sleep staging is classic research for replacing the time-consuming gold-standard manual staging procedure. Recently, promising results have been achieved on automatic staging by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the dynamic fluctuations of neurons on top of EEG features that is non-trivial for automatic sleep staging task. This paper introduces a biomimicry spiking neural network (SNN) to map the aforementioned features serving for automatic sleep staging. Such SNNs are designed as an array of encoders that converts frequency-specific features into long-term spiking coding and then a popular ANN model is used as the staging machine by absorbing the stage-dependent spiking representation. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets. The experimental results showed that the proposed method achieved a competitive stage scoring performance, especially for Wake, N2, and N3, with higher Precision of 0.94, 0.87, and 0.86. Moreover, the ablation studies prove the SNN has the potential for extracting the neuron’s variation features.