  Sleep disorder is one of many neurological diseases that can affect greatly
the quality of daily life. It is very burdensome to manually classify the sleep
stages to detect sleep disorders. Therefore, the automatic sleep stage
classification techniques are needed. However, the previous automatic sleep
scoring methods using raw signals are still low classification performance. In
this study, we proposed an end-to-end automatic sleep staging framework based
on optimal spectral-temporal sleep features using a sleep-edf dataset. The
input data were modified using a bandpass filter and then applied to a
convolutional neural network model. For five sleep stage classification, the
classification performance 85.6% and 91.1% using the raw input data and the
proposed input, respectively. This result also shows the highest performance
compared to conventional studies using the same dataset. The proposed framework
has shown high performance by using optimal features associated with each sleep
stage, which may help to find new features in the automatic sleep stage method.
