We present a novel method for automatic sleep scoring based on single-channel EEG. We
introduce the use of a deep convolutional neural network (CNN) on raw EEG samples for …

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep
stage scoring based on raw single-channelEEG. Most of the existing methods rely on hand …

We developed a machine learning methodology for automatic sleep stage scoring. Our time-
frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific …

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on
single-channel electroencephalography (EEG) to learn task-specific filters for classification …

Background and objective: This paper presents a new framework for automatic classification
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm
that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods …

Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …

Convolutional neural networks (CNN) have demonstrated state-of-the-art classification
results in image categorization, but have received comparatively little attention for …

Objective: We investigate the design of deep recurrent neural networks for detecting sleep
stages from single channel EEG signals recorded at home by non-expert users. We report …

We propose in this work a feature learning approach using deep bidirectional recurrent
neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage …