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相关概念视频

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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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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Sleep-Wake Cycles

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Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
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相关实验视频

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A Model to Simulate Clinically Relevant Hypoxia in Humans
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SCM-4-OSA:一个端到端可解释的深度学习模型,用于基于心电图中心率周期变化的可解释阻断性睡眠呼吸暂停检测.

Weinan Wang, Kevin Kilgore, Laleh Najafizadeh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    本研究介绍了SCM-4-OSA,这是一个可解释的深度学习模型,用于检测阻塞性睡眠呼吸暂停 (OSA),使用心率变化在心电图信号中. 它实现了高精度,同时为临床使用提供可解释的可视化图像.

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    科学领域:

    • 心脏病学 心脏病学
    • 医疗成像医学成像
    • 人工智能的人工智能

    背景情况:

    • 阻塞性睡眠呼吸暂停 (OSA) 诊断通常依赖于多睡眠学 (PSG),这是一个资源密集的方法.
    • 从单导电心电图 (ECG) 信号检测心率周期变化 (CVHR) 为OSA查提供了更简单,更具成本效益的替代方案.
    • 目前用于CVHR检测的深度学习方法缺乏透明度,阻碍了临床信任和采用.

    研究的目的:

    • 开发第一个可解释的端到端深度学习架构SCM-4-OSA,用于可解释的阻塞性睡眠呼吸暂停 (OSA) 检测.
    • 解决当前黑子深度学习模型在识别OSA诊断中CVHR方面的局限性.
    • 通过增强模型解释性,使人工智能驱动的OSA检测能够在临床上被采用.

    主要方法:

    • 自适应的自我对比掩盖 (SCM) 来创建SCM-4-OSA,一个可解释的深度学习模型.
    • 员工监督培训,并对ECG信号间隔进行时间比较,以学习互补的面具.
    • 通过使用CVHR识别对OSA检测的公开数据集进行模型验证.

    主要成果:

    • 对于OSA检测,SCM-4-OSA实现了86.9%的任务级准确性,与最先进的非可解释模型相比.
    • 该模型成功生成了可视化图像,清楚地突出了ECG信号中的CVHR模式.
    • 证明了基于SCM的模型能够为OSA检测提供可解释的见解的能力.

    结论:

    • SCM-4-OSA代表了对OSA检测的可解释深度学习的重大进展.
    • 该模型的可解释性有助于理解与OSA相关的CVHR模式,促进临床信任.
    • 这种方法对人工智能在睡眠呼吸暂停诊断中的广泛临床采用充满希望.