13,911 research outputs found
Metric Learning for Automatic Sleep Stage Classification
We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step
Metric Learning for Automatic Sleep Stage Classification
We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage from 53 participants with RBD and 53 age-matched
healthy controls. Sleep stage classification was achieved using a Random Forest
(RF) classifier and 156 features extracted from electroencephalogram (EEG),
electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a
RF classifier was trained combining established techniques to quantify muscle
atonia with additional features that incorporate sleep architecture and the EMG
fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's
Kappa score. RBD detection accuracy improved by 10% to 96% (compared to
individual established metrics) when using manually annotated sleep staging.
Accuracy remained high (92%) when using automated sleep staging. This study
outperforms established metrics and demonstrates that incorporating sleep
architecture and sleep stage transitions can benefit RBD detection. This study
also achieved automated sleep staging with a level of accuracy comparable to
manual annotation. This study validates a tractable, fully-automated, and
sensitive pipeline for RBD identification that could be translated to wearable
take-home technology.Comment: 20 pages, 3 figure
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
automatic stage scoring based on machine learning techniques have been carried
out over the last years. In this work, we resort to multitaper spectral
analysis to create visually interpretable images of sleep patterns from EEG
signals as inputs to a deep convolutional network trained to solve visual
recognition tasks. As a working example of transfer learning, a system able to
accurately classify sleep stages in new unseen patients is presented.
Evaluations in a widely-used publicly available dataset favourably compare to
state-of-the-art results, while providing a framework for visual interpretation
of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on
Machine Learning for Signal Processin
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