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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Convolution Properties I01:20

Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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Insufficient Sleep and Sleep Deprivation01:13

Insufficient Sleep and Sleep Deprivation

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Insufficient sleep refers to not getting the recommended amount of sleep for optimal functioning, even if it's just slightly less than needed. Sleep insufficiency may occur due to lifestyle choices, such as staying up late for social events or work, resulting in routinely getting less sleep than required. For example, consistently sleeping 6 hours when the body needs 7-9 hours can lead to cumulative effects on health and well-being.
Sleep deprivation is a more severe form of sleep loss...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Automatic System for Obstructive Sleep Apnea Events Detection Using Convolutional Neural Network.

Ling Cen, Zhu Liang Yu, Tilmann Kluge

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary
    This summary is machine-generated.

    Deep learning accurately detects Obstructive Sleep Apnea (OSA) events using physiological signals. This automated method identifies hypopnea and apnea, improving sleep disorder diagnosis and management.

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    Multi-Modal Home Sleep Monitoring in Older Adults
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    Area of Science:

    • Biomedical Engineering
    • Sleep Medicine
    • Artificial Intelligence

    Background:

    • Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by repeated breathing cessations or reductions.
    • Untreated OSA can lead to significant health issues including daytime fatigue, memory impairment, and depression.

    Purpose of the Study:

    • To develop an automated system for detecting Obstructive Sleep Apnea events using 1-second annotations.
    • To investigate the efficacy of Deep Learning, specifically Convolutional Neural Networks (CNNs), for OSA detection.

    Main Methods:

    • Utilized physiological data including blood oxygen saturation, oronasal airflow, and respiratory movements (ribcage and abdomen).
    • Employed Convolutional Neural Networks (CNNs) for feature extraction and classification of sleep apnea events.
    • Performed leave-one-out cross-validation on the PhysioNet Sleep Database.

    Main Results:

    • Achieved an average accuracy of 79.61% in classifying normal, hypopnea, and apnea events.
    • Demonstrated the capability of CNNs to learn high-order correlations within physiological data for accurate detection.

    Conclusions:

    • Deep learning models, particularly CNNs, show significant promise for the automatic and accurate detection of Obstructive Sleep Apnea.
    • The developed method provides a foundation for improved diagnostic tools in sleep medicine.