549 research outputs found
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
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
Application of Recurrent Neural Network for the Prediction of Target Non-Apneic Arousal Regions in Physiological Signals
This work presents a new method for detection of target non-apneic arousals by applying a recurrent neural network architecture on the various specified polysomnographic (PSG) signals. The proposed two stage architecture uses sequences of instantaneous frequencies and spectral entropies of the chosen PSG signals as feature vectors. At the first stage, these feature vectors are used to train several long-short term memory (LSTM) models. The LSTM networks can learn long-term relationships between time steps of time-frequency based sequences obtained out of physiological signals. As a second stage, some quadratic discriminant (QD) layers are modelled and appended to the trained LSTMs in groups. Subsequently, the outputs of all the QD layers are averaged for making final prediction. The models are trained using features obtained from one minute windows of the signals. However, the decision making on test signals involves inputs of one minute windows with half minute overlapping. When evaluated with 2018 PhysioNet/CinC Challenge dataset, the experimental outcomes demonstrate overall AUROC and AUPRC scores of 0.85±0.10 and 0.50±0.15 respectively for the training data. The generated test results indicate the AUROC and AUPRC scores of 0.624 and 0.10 respectively on a random subset of the test data
Automatic detection of EEG arousals
[Abstract] Fragmented sleep is commonly caused by arousals that can be
detected with the observation of electroencephalographic (EEG) signals.
As this is a time consuming task, automatization processes are required. A
method using signal processing and machine learning models, for arousal
detection, is presented. Relevant events are identified in the EEG signals
and in the electromyography, during the signal processing phase. After
discarding those events that do not meet the required characteristics, the
resulting set is used to extract multiple parameters. Several machine learning
models — Fisher’s Linear Discriminant, Artificial Neural Networks and
Support Vector Machines — are fed with these parameters. The final proposed
model, a combination of the different individual models, was used
to conduct experiments on 26 patients, reporting a sensitivity of 0.72 and
a specificity of 0.89, while achieving an error of 0.13, in the arousal events
detection.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/035Ministerio de Economía y Competitividad;
TIN2013-40686
Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients
SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated
Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics
Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance
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