632 research outputs found
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
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Electroencephalogram (EEG) is a common base signal used to monitor brain
activity and diagnose sleep disorders. Manual sleep stage scoring is a
time-consuming task for sleep experts and is limited by inter-rater
reliability. In this paper, we propose an automatic sleep stage annotation
method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is
composed of deep convolutional neural networks (CNNs) to extract time-invariant
features, frequency information, and a sequence to sequence model to capture
the complex and long short-term context dependencies between sleep epochs and
scores. In addition, to reduce the effect of the class imbalance problem
presented in the available sleep datasets, we applied novel loss functions to
have an equal misclassified error for each sleep stage while training the
network. We evaluated the proposed method on different single-EEG channels
(i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets
published in 2013 and 2018. The evaluation results demonstrate that the
proposed method achieved the best annotation performance compared to current
literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and
Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with
more sleep EEG signals and aid the sleep specialists to arrive at an accurate
diagnosis. The source code is available at
https://github.com/SajadMo/SleepEEGNet
Deep transfer learning for improving single-EEG arousal detection
Datasets in sleep science present challenges for machine learning algorithms
due to differences in recording setups across clinics. We investigate two deep
transfer learning strategies for overcoming the channel mismatch problem for
cases where two datasets do not contain exactly the same setup leading to
degraded performance in single-EEG models. Specifically, we train a baseline
model on multivariate polysomnography data and subsequently replace the first
two layers to prepare the architecture for single-channel
electroencephalography data. Using a fine-tuning strategy, our model yields
similar performance to the baseline model (F1=0.682 and F1=0.694,
respectively), and was significantly better than a comparable single-channel
model. Our results are promising for researchers working with small databases
who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202
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