DeepSleepNet is a deep learning model that classifies sleep stages from raw single-channel EEG recordings. It uses a convolutional neural network for feature extraction followed by a long short-term memory network for sequence modeling. The model was developed for sleep stage scoring research in healthcare applications and achieves competitive performance on standard datasets like MASS and Sleep-EDF. The architecture learned interpretable temporal patterns corresponding to sleep onset transitions.