2,397 research outputs found
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device
The quality of sleep can be affected by the occurrence of a sleep related disorder and, among
these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be
the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable
to a large group of the world population. To address these issues, the main goal of this work was to
develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing
a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The
method employs a cross-spectral coherence technique which produces a spectrographic image that fed three
one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of
sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two
methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were
combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified
that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model,
advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple
to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive
sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore,
increasing the accessibility of the population to sleep analysis.info:eu-repo/semantics/publishedVersio
Resting state functional thalamic connectivity abnormalities in patients with post-stroke sleep apnoea: a pilot case-control study
OBJECTIVE: Sleep apnoea is common
after stroke, and has adverse effects on the
clinical outcome of affected cases. Its pathophysiological
mechanisms are only partially known. Increases
in brain connectivity after stroke might influence
networks involved in arousal modulation
and breathing control. The aim of this study was to
investigate the resting state functional MRI thalamic
hyper connectivity of stroke patients affected
by sleep apnoea (SA) with respect to cases not
affected, and to healthy controls (HC).
PATIENTS AND METHODS: A series of stabilized
strokes were submitted to 3T resting state
functional MRI imaging and full polysomnography.
The ventral-posterior-lateral thalamic nucleus was
used as seed.
RESULTS: At the between groups comparison
analysis, in SA cases versus HC, the regions significantly
hyper-connected with the seed were
those encoding noxious threats (frontal eye
field, somatosensory association, secondary visual
cortices). Comparisons between SA cases
versus those without SA, revealed in the former
group significantly increased connectivity with
regions modulating the response to stimuli independently
to their potentiality of threat (prefrontal,
primary and somatosensory association, superolateral
and medial-inferior temporal, associative
and secondary occipital ones). Further
significantly functionally hyper connections were
documented with regions involved also in the modulation
of breathing during sleep (pons, midbrain,
cerebellum, posterior cingulate cortices), and in
the modulation of breathing response to chemical
variations (anterior, posterior and para-hippocampal
cingulate cortices).
CONCLUSIONS: Our preliminary data support
the presence of functional hyper connectivity in
thalamic circuits modulating sensorial stimuli, in
patients with post-stroke sleep apnoea, possibly
influencing both their arousal ability and breathing
modulation during sleep
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
Functional Imaging of Autonomic Regulation: Methods and Key Findings.
Central nervous system processing of autonomic function involves a network of regions throughout the brain which can be visualized and measured with neuroimaging techniques, notably functional magnetic resonance imaging (fMRI). The development of fMRI procedures has both confirmed and extended earlier findings from animal models, and human stroke and lesion studies. Assessments with fMRI can elucidate interactions between different central sites in regulating normal autonomic patterning, and demonstrate how disturbed systems can interact to produce aberrant regulation during autonomic challenges. Understanding autonomic dysfunction in various illnesses reveals mechanisms that potentially lead to interventions in the impairments. The objectives here are to: (1) describe the fMRI neuroimaging methodology for assessment of autonomic neural control, (2) outline the widespread, lateralized distribution of function in autonomic sites in the normal brain which includes structures from the neocortex through the medulla and cerebellum, (3) illustrate the importance of the time course of neural changes when coordinating responses, and how those patterns are impacted in conditions of sleep-disordered breathing, and (4) highlight opportunities for future research studies with emerging methodologies. Methodological considerations specific to autonomic testing include timing of challenges relative to the underlying fMRI signal, spatial resolution sufficient to identify autonomic brainstem nuclei, blood pressure, and blood oxygenation influences on the fMRI signal, and the sustained timing, often measured in minutes of challenge periods and recovery. Key findings include the lateralized nature of autonomic organization, which is reminiscent of asymmetric motor, sensory, and language pathways. Testing brain function during autonomic challenges demonstrate closely-integrated timing of responses in connected brain areas during autonomic challenges, and the involvement with brain regions mediating postural and motoric actions, including respiration, and cardiac output. The study of pathological processes associated with autonomic disruption shows susceptibilities of different brain structures to altered timing of neural function, notably in sleep disordered breathing, such as obstructive sleep apnea and congenital central hypoventilation syndrome. The cerebellum, in particular, serves coordination roles for vestibular stimuli and blood pressure changes, and shows both injury and substantially altered timing of responses to pressor challenges in sleep-disordered breathing conditions. The insights into central autonomic processing provided by neuroimaging have assisted understanding of such regulation, and may lead to new treatment options for conditions with disrupted autonomic function
Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome
Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed
Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review
Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio
A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert
technician is needed to score. Numerous researchers have proposed and implemented automatic
scoring processes to address these issues, based on fewer sensors and automatic classification
algorithms. Deep learning is gaining higher interest due to database availability, newly developed
techniques, the possibility of producing machine created features and higher computing power that
allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep
apnea research has currently gained significant interest in deep learning. The goal of this work is to
analyze the published research in the last decade, providing an answer to the research questions such
as how to implement the different deep networks, what kind of pre-processing or feature extraction is
needed, and the advantages and disadvantages of different kinds of networks. The employed signals,
sensors, databases and implementation challenges were also considered. A systematic search was
conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were
selected by considering the inclusion and exclusion criteria, using the preferred reporting items for
systematic reviews and meta-analyses (PRISMA) approach.info:eu-repo/semantics/publishedVersio
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