We develop a random forest classifier to automatically classify brain waves into sleep stages
by using the publicly available data from PhysioBank. More specifically, we use the EEG …

Visual inspection of Polysomnography (PSG) recordings by sleep experts, based on
established guidelines, has been the gold standard in sleep stage classification. This …

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 work attempts to design an effective sleep staging system, making the best use of the
available signals, strategies, and features in the literature. It must not only perform well on …

Human sleep can be described as a series of transitions between distinct states. This makes
automatic sleep analysis (scoring) suitable for an automatic implementation using machine …

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

Current sleep medicine relies on the supervised analysis of polysomnographic recordings,
which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and …

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 develop a deep convolutional neural network (CNN) that performs sleep stage
classification from human sleep EEG and EOG signals. We build and compare several …

The classification of sleep–wake stages suffers from poor standardization in scoring criteria
and heterogeneous conditioning of polysomnographic signals. To improve applicability of …