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

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 …

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

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 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 …

Deep learning has become popular for automatic sleep stage scoring due to its capability to
extract useful features from raw signals. Most of the existing models, however, have been …

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-
consuming process. Various existing state-of-the-art approaches rely on hand-crafted …

Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In
order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a …