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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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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
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相关实验视频

Updated: Jul 17, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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使用混合1D-2D卷积神经网络自动检测打.

Ruixue Li1, Wenjun Li2, Keqiang Yue1

  • 1Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.

Scientific reports
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

一个自动算法准确地检测出打,有助于查和诊断阻塞性睡眠呼吸暂停 (OSA). 这种非接触式方法为在日常生活中识别OSA患者提供了重大进展.

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科学领域:

  • 生物医学工程 生物医学工程
  • 睡眠医学 睡眠医学
  • 人工智能的人工智能

背景情况:

  • 打严重影响睡眠呼吸障碍患者及其伴侣的生活质量.
  • 打是查和诊断阻塞性睡眠呼吸暂停 (OSA) 的关键指标.
  • 从夜间呼吸系统音频中准确检测打是必不可少的,但具有挑战性.

研究的目的:

  • 开发一个高精度,自动的打检测算法.
  • 设计一种非接触式系统,用于在自然环境中记录睡眠呼吸声.
  • 评估混合卷积神经网络 (CNN) 模型用于打检测.

主要方法:

  • 使用非接触式数据采集系统记录夜间睡眠呼吸声.
  • 开发了一种混合CNN模型,其中包括1DCNN用于原始信号处理和2DCNN用于可见度图表表示.
  • 采用混合CNN架构,从音频录音中自动检测打.

主要成果:

  • 拟议的混合CNN模型实现了平均分类准确率为89.3%.
  • 该算法表现出高性能,平均灵敏度为89.7%,特异性为88.5%.
  • 平均曲线下面积 (AUC) 为0.947表示强大的检测能力,优于现有模型.

结论:

  • 开发的方法对于阻塞性睡眠呼吸暂停 (OSA) 患者的大规模查是有效和重要的.
  • 这种方法为睡眠研究中的时间序列分析提供了一个有价值的替代框架.
  • 无接触的自动化系统在日常环境中促进了OSA查.