Provided is an automatic determination apparatus using a deep learning, the automatic determination apparatus including a signal data processor configured to collect signal data detected through polysomnography, to extract feature data by analyzing a feature of the collected signal data, and to transform the extracted feature data to time series data; and a sleep stage classification model processor configured to input the processed signal data to a pre-generated sleep stage classification model, and to classify a sleep stage corresponding to the signal data. The sleep stage classification model processor is configured to classify the sleep stage based on at least one of an American Academy of Sleep Medicine (AASM) standard and a Rechtschaffen and Kales (R&K) standard using an epoch unit of time series data as the processed signal data.
APPARATUS FOR AUTOMATICALLY DETERMINING SLEEP DISORDER USING DEEP RUNNING AND OPERATION METHOD OF THE APPARATUS