2,442 research outputs found
Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG
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 the effect of data set size, architecture choices, regularization, and personalization on the classification performance.Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation.Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
The brain electrical activity presents several short events during sleep that
can be observed as distinctive micro-structures in the electroencephalogram
(EEG), such as sleep spindles and K-complexes. These events have been
associated with biological processes and neurological disorders, making them a
research topic in sleep medicine. However, manual detection limits their study
because it is time-consuming and affected by significant inter-expert
variability, motivating automatic approaches. We propose a deep learning
approach based on convolutional and recurrent neural networks for sleep EEG
event detection called Recurrent Event Detector (RED). RED uses one of two
input representations: a) the time-domain EEG signal, or b) a complex
spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT).
Unlike previous approaches, a fixed time window is avoided and temporal context
is integrated to better emulate the visual criteria of experts. When evaluated
on the MASS dataset, our detectors outperform the state of the art in both
sleep spindle and K-complex detection with a mean F1-score of at least 80.9%
and 82.6%, respectively. Although the CWT-domain model obtained a similar
performance than its time-domain counterpart, the former allows in principle a
more interpretable input representation due to the use of a spectrogram. The
proposed approach is event-agnostic and can be used directly to detect other
types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint
Conference on Neural Networks (IJCNN 2020
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Much attention has been given to automatic sleep staging algorithms in past
years, but the detection of discrete events in sleep studies is also crucial
for precise characterization of sleep patterns and possible diagnosis of sleep
disorders. We propose here a deep learning model for automatic detection and
annotation of arousals and leg movements. Both of these are commonly seen
during normal sleep, while an excessive amount of either is linked to disrupted
sleep patterns, excessive daytime sleepiness impacting quality of life, and
various sleep disorders. Our model was trained on 1,485 subjects and tested on
1,000 separate recordings of sleep. We tested two different experimental setups
and found optimal arousal detection was attained by including a recurrent
neural network module in our default model with a dynamic default event window
(F1 = 0.75), while optimal leg movement detection was attained using a static
event window (F1 = 0.65). Our work show promise while still allowing for
improvements. Specifically, future research will explore the proposed model as
a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in
Medicine and Biology Conference (EMBC), July 23-27, 201
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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