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相关概念视频

Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

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

Updated: Jun 14, 2026

International Expert Consensus and Recommendations for Neonatal Pneumothorax Ultrasound Diagnosis and Ultrasound-guided Thoracentesis Procedure
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NeoSSNet:使用深度学习实时新生儿胸部声音分离.

Yang Yi Poh1, Ethan Grooby1,2, Kenneth Tan3

  • 1Department of Electrical and Computer Systems EngineeringMonash University, Melbourne Clayton VIC 3800 Australia.

IEEE open journal of engineering in medicine and biology
|June 20, 2024
PubMed
概括

一个新的深度学习模型,NeoSSNet,有效地分离新生儿的心脏和肺部声音,提高诊断准确度. 这种更快的方法通过隔离特定的胸部声音来增强健康监测系统.

关键词:
深度学习是一种深度学习.心脏的声音,心脏的声音.肺部的声音 肺部的声音听力心电图 (PCG) 是一种心电图.单通道声音分离器

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 新生儿健康 新生儿健康

背景情况:

  • 听觉对于诊断新生儿心血管和呼吸系统疾病至关重要.
  • 从新生儿胸部录音中获取清晰,孤立的心脏或肺部声音是一项挑战.

研究的目的:

  • 介绍NeoSSNet,一种用于新生儿胸部声音分离的新型深度学习模型.
  • 评估NeoSSNet的性能与现有方法对比,以提高诊断准确度.

主要方法:

  • 一个基于面具的深度学习架构 (NeoSSNet) 使用1D卷积和面具生成的变压器.
  • 将胸部声音编码为令牌,应用生成的心脏和肺部声音口罩,并将其解码回波形.

主要成果:

  • 与之前的方法相比,NeoSSNet表现出优越的性能,客观扭曲的改进范围从2.01dB到5.06dB.
  • 该模型实现了显著的速度改进,至少比现有技术快17倍.

结论:

  • NeoSSNet为准确的新生儿胸部声音分离提供了一个有前途的解决方案.
  • 该模型可以作为健康监测系统的有效预处理步骤,需要隔离的心脏或肺部声音.