3 research outputs found
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals
The regulation of the autonomic nervous system changes with the sleep stages
causing variations in the physiological variables. We exploit these changes
with the aim of classifying the sleep stages in awake or asleep using pulse
oximeter signals. We applied a recurrent neural network to heart rate and
peripheral oxygen saturation signals to classify the sleep stage every 30
seconds. The network architecture consists of two stacked layers of
bidirectional gated recurrent units (GRUs) and a softmax layer to classify the
output. In this paper, we used 5000 patients from the Sleep Heart Health Study
dataset. 2500 patients were used to train the network, and two subsets of 1250
were used to validate and test the trained models. In the test stage, the best
result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity,
92.05% precision, and 84.68% negative predictive value. Further, the Cohen's
Kappa coefficient was 0.74 and the average absolute error percentage to the
actual sleep time was 8.9%. The performance of the proposed network is
comparable with the state-of-the-art algorithms when they use much more
informative signals (except those with EEG).Comment: 12 pages, 4 figures, 2 table
Screening for REM Sleep Behaviour Disorder with Minimal Sensors
Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor
of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy.
This study investigates a minimal set of sensors to achieve effective screening
for RBD in the population, integrating automated sleep staging (three state)
followed by RBD detection without the need for cumbersome electroencephalogram
(EEG) sensors. Polysomnography signals from 50 participants with RBD and 50
age-matched healthy controls were used to evaluate this study. Three stage
sleep classification was achieved using a Random Forest (RF) classifier and
features derived from a combination of cost-effective and easy to use sensors,
namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram
(EMG) channels. Subsequently, RBD detection was achieved using established and
new metrics derived from ECG and EMG metrics. The EOG and EMG combination
provided the best minimalist fully automated performance, achieving
kappa (3 stage) for sleep staging and an RBD detection accuracy
of , (sensitivity, and specificity , and
). A single ECG sensor allowed three state sleep staging with
kappa and RBD detection accuracy of . This study
demonstrated the feasibility of using signals from a single EOG and EMG sensor
to detect RBD using fully-automated techniques. This study proposes a
cost-effective, practical, and simple RBD identification support tool using
only two sensors (EMG and EOG), ideal for screening purposes.Comment: 21 pages, 6 figures, and 6 tables. arXiv admin note: text overlap
with arXiv:1811.0466
A review of automated sleep stage scoring based on physiological signals for the new millennia
Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost