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相关概念视频

Heart Sounds01:15

Heart Sounds

3.2K
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)...
3.2K

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相关实验视频

Updated: Jan 9, 2026

Whole Neonatal Cochlear Explants as an In vitro Model
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Whole Neonatal Cochlear Explants as an In vitro Model

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基于深度学习的耳内心脏声音的细分.

Jordan Waters, Jake Stuchbury-Wass, Yang Liu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    这项研究引入了一种使用耳塞麦克风对心脏声音进行细分的新方法,达到84%的准确性. 这一创新使得在医院之外可以进行持续的心血管监测.

    科学领域:

    • 生物医学工程 生物医学工程
    • 心脏病学 心脏病学
    • 信号处理 信号处理

    背景情况:

    • 心血管疾病是全球主要的健康问题.
    • 心脏声音细分对于诊断门异常至关重要,但需要专业知识.
    • 目前的自动化方法依赖于专门的医疗设备,限制了连续使用.

    研究的目的:

    • 探索基于耳朵的设备在持续的心脏声音细分方面的潜力.
    • 为了解决入耳麦克风 (IEM) 和传统心电图 (PCG) 之间的信号差异.
    • 开发和评估基于IEM的心声细分的深度学习模型.

    主要方法:

    • 对区分IEM和PCG信号的时间和频率特征的分析.
    • 开发U-Net深度学习模型,专门用于内耳心声细分.
    • 实施严格的评估指标来评估细分精度.

    主要成果:

    • 拟议的U-Net模型使用IEM数据实现了84%的心声细分精度.
    • 该模型显著超过了现有的基线方法.
    • 这项研究强调了使用随时可用的可听设备进行心脏监测的可行性.

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    结论:

    • 耳机设备为便携式,持续的心血管监测提供了一个有希望的途径.
    • 开发的U-Net模型是有效的心脏声音细分从耳内录音.
    • 这种方法可以促进更广泛的心脏诊断和远程患者监测.