528 research outputs found
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
We have developed an automatic sleep stage classification algorithm based on
deep residual neural networks and raw polysomnogram signals. Briefly, the raw
data is passed through 50 convolutional layers before subsequent classification
into one of five sleep stages. Three model configurations were trained on 1850
polysomnogram recordings and subsequently tested on 230 independent recordings.
Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of
0.746, improving on previous reported results by other groups also using only
raw polysomnogram data. Most errors were made on non-REM stage 1 and 3
decisions, errors likely resulting from the definition of these stages. Further
testing on independent cohorts is needed to verify performance for clinical
use
Sleep state classification using power spectral density and residual neural network with multichannel EEG signals.
This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively
Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
Study Objectives: Sleep stage scoring is performed manually by sleep experts
and is prone to subjective interpretation of scoring rules with low intra- and
interscorer reliability. Many automatic systems rely on few small-scale
databases for developing models, and generalizability to new datasets is thus
unknown. We investigated a novel deep neural network to assess the
generalizability of several large-scale cohorts.
Methods: A deep neural network model was developed using 15684
polysomnography studies from five different cohorts. We applied four different
scenarios: 1) impact of varying time-scales in the model; 2) performance of a
single cohort on other cohorts of smaller, greater or equal size relative to
the performance of other cohorts on a single cohort; 3) varying the fraction of
mixed-cohort training data compared to using single-origin data; and 4)
comparing models trained on combinations of data from 2, 3, and 4 cohorts.
Results: Overall classification accuracy improved with increasing fractions
of training data (0.25: 0.782 0.097, 95 CI [0.777-0.787];
100: 0.869 0.064, 95 CI [0.864-0.872]), and with increasing
number of data sources (2: 0.788 0.102, 95 CI [0.787-0.790]; 3: 0.808
0.092, 95 CI [0.807-0.810]; 4: 0.821 0.085, 95 CI
[0.819-0.823]). Different cohorts show varying levels of generalization to
other cohorts.
Conclusions: Automatic sleep stage scoring systems based on deep learning
algorithms should consider as much data as possible from as many sources
available to ensure proper generalization. Public datasets for benchmarking
should be made available for future research.Comment: Author's original version. This article has been accepted for
publication in SLEEP published by Oxford University Pres
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation
The classification of sleep stages plays a crucial role in understanding and
diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual
inspection by an expert that is time consuming and subjective procedure.
Recently, deep learning neural network approaches have been leveraged to
develop a generalized automated sleep staging and account for shifts in
distributions that may be caused by inherent inter/intra-subject variability,
heterogeneity across datasets, and different recording environments. However,
these networks ignore the connections among brain regions, and disregard the
sequential connections between temporally adjacent sleep epochs. To address
these issues, this work proposes an adaptive product graph learning-based graph
convolutional network, named ProductGraphSleepNet, for learning joint
spatio-temporal graphs along with a bidirectional gated recurrent unit and a
modified graph attention network to capture the attentive dynamics of sleep
stage transitions. Evaluation on two public databases: the Montreal Archive of
Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night
polysomnography recordings of 62 and 20 healthy subjects, respectively,
demonstrates performance comparable to the state-of-the-art (Accuracy:
0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database
respectively). More importantly, the proposed network makes it possible for
clinicians to comprehend and interpret the learned connectivity graphs for
sleep stages.Comment: 9 pages, 6 figure
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
In recent years, significant attention has been devoted towards integrating
deep learning technologies in the healthcare domain. However, to safely and
practically deploy deep learning models for home health monitoring, two
significant challenges must be addressed: the models should be (1) robust
against noise; and (2) compact and energy-efficient. We propose REST, a new
method that simultaneously tackles both issues via 1) adversarial training and
controlling the Lipschitz constant of the neural network through spectral
regularization while 2) enabling neural network compression through sparsity
regularization. We demonstrate that REST produces highly-robust and efficient
models that substantially outperform the original full-sized models in the
presence of noise. For the sleep staging task over single-channel
electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67
vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise
while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large,
real-world EEG datasets. By deploying these models to an Android application on
a smartphone, we quantitatively observe that REST allows models to achieve up
to 17x energy reduction and 9x faster inference. We open-source the code
repository with this paper: https://github.com/duggalrahul/REST.Comment: Accepted to WWW 202
Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness
Cortical arousals are transient events of disturbed sleep that occur
spontaneously or in response to stimuli such as apneic events. The gold
standard for arousal detection in human polysomnographic recordings (PSGs) is
manual annotation by expert human scorers, a method with significant
interscorer variability. In this study, we developed an automated method, the
Multimodal Arousal Detector (MAD), to detect arousals using deep learning
methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and
wakefulness in 1 second intervals. Furthermore, the relationship between
MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a
multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was
analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs,
the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness
was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human
expert technicians, the MAD significantly outperformed the average human scorer
for arousal detection with a difference in F1 score of 0.09. After controlling
for other known covariates, a doubling of the arousal index was associated with
an average decrease in MSL of 40 seconds ( = -0.67, p = 0.0075). The MAD
outperformed the average human expert and the MAD-predicted arousals were shown
to be significant predictors of MSL, which demonstrate clinical validity the
MAD.Comment: 40 pages, 13 figures, 9 table
Sleep Stage Classification: A Deep Learning Approach
Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed.
In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers.
For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity
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