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相关概念视频

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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Breathing01:05

Breathing

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The process of breathing, inhaling and exhaling, involves the coordinated movement of the chest wall, the lungs, and the muscles that move them. Two muscle groups with important roles in breathing are the diaphragm, located directly below the lungs, and the intercostal muscles, which lie between the ribs. When the diaphragm contracts, it moves downward, increasing the volume of the thoracic cavity and creating more room for the lungs to expand. When the intercostal muscles contract, the ribs...
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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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Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
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Heart Sounds01:15

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Soundness of Cement01:17

Soundness of Cement

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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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相关实验视频

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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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对于阻塞性睡眠呼吸暂停的觉醒见解:使用气管呼吸声和元模型分析检测严重程度.

Ali Mohammad Alqudah1, Zahra Moussavi1,2

  • 1Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.

Diagnostics (Basel, Switzerland)
|February 13, 2026
PubMed
概括

本研究提出了一种机器学习模型,使用气管呼吸声音来评估阻塞性睡眠呼吸暂停 (OSA) 的严重程度. 非侵入性系统为快速查提供了可扩展的多睡眠学替代方案.

关键词:
预测OSA严重程度的预测超级模型是什么阻塞性睡眠呼吸暂停症是什么气管呼吸的声音.清醒的查 清醒的查

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习应用 机器学习应用
  • 睡眠医学 睡眠医学

背景情况:

  • 阻塞性睡眠呼吸暂停 (OSA) 是一种常见的疾病,与显著的心血管和认知风险有关.
  • 目前的诊断方法,如多睡眠学 (PSG) 是资源密集型,不适合广泛,快速查.
  • 需要有效的,非侵入性的工具来准确评估OSA的严重程度.

研究的目的:

  • 开发和验证一个机器学习 (ML) 的元建模框架,用于多类预测阻塞性睡眠呼吸暂停 (OSA) 的严重程度.
  • 使用气管呼吸声 (TBS) 和人体测量数据进行非侵入性OSA评估.
  • 为了在OSA严重程度分类中纳入可靠,校准的信心集的合规预测.

主要方法:

  • 开发了一个元建模框架,汇总了六个一对一分类器,用于多类OSA严重性预测.
  • 气管呼吸声音 (TBS) 和人体测量数据被用作输入特征.
  • 用于评估概括性和可靠性,使用了包装式估计 (OOB),三重交叉验证和符合性预测.

主要成果:

  • 在三类设置 (非,轻度,中度/重度) 中,该模型实现了76.7%的测试准确度和77.7%的灵敏度.
  • 袋外的性能显示出高精度 (91.1%),灵敏度 (91.6%) 和特异性 (95.3%).
  • 合规预测提供了完全覆盖的校准置信集,表明可靠的不确定性量化.

结论:

  • 开发的非侵入性系统显示了在清醒状态下有效和快速评估阻塞性睡眠呼吸暂停 (OSA) 严重性的潜力.
  • 这种基于ML的方法为大规模查和临床分类提供了可扩展的多睡眠学 (PSG) 替代方案.
  • 这些发现支持气管呼吸声音对客观OSA严重程度分类的有用性.