4 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
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
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
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