21,026 research outputs found
Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring
Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods.
Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm
Dynamics of Snoring Sounds and Its Connection with Obstructive Sleep Apnea
Snoring is extremely common in the general population and when irregular may
indicate the presence of obstructive sleep apnea. We analyze the overnight
sequence of wave packets --- the snore sound --- recorded during full
polysomnography in patients referred to the sleep laboratory due to suspected
obstructive sleep apnea. We hypothesize that irregular snore, with duration in
the range between 10 and 100 seconds, correlates with respiratory obstructive
events. We find that the number of irregular snores --- easily accessible, and
quantified by what we call the snore time interval index (STII) --- is in good
agreement with the well-known apnea-hypopnea index, which expresses the
severity of obstructive sleep apnea and is extracted only from polysomnography.
In addition, the Hurst analysis of the snore sound itself, which calculates the
fluctuations in the signal as a function of time interval, is used to build a
classifier that is able to distinguish between patients with no or mild apnea
and patients with moderate or severe apnea
Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses
in breathing or by instances of abnormally low breathing.
Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed
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
Sleep apnea-hypopnea quantification by cardiovascular data analysis
Sleep apnea is the most common sleep disturbance and it is an important risk
factor for cardiovascular disorders. Its detection relies on a polysomnography,
a combination of diverse exams.
In order to detect changes due to sleep disturbances such as sleep apnea
occurrences, without the need of combined recordings, we mainly analyze
systolic blood pressure signals (maximal blood pressure value of each beat to
beat interval). Nonstationarities in the data are uncovered by a segmentation
procedure, which provides local quantities that are correlated to
apnea-hypopnea events. Those quantities are the average length and average
variance of stationary patches. By comparing them to an apnea score previously
obtained by polysomnographic exams, we propose an apnea quantifier based on
blood pressure signal.
This furnishes an alternative procedure for the detection of apnea based on a
single time series, with an accuracy of 82%
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
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