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相关概念视频

Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

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In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Air-entraining Agents01:27

Air-entraining Agents

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Air-entraining agents improve the durability and workability of concrete in climates with frequent freezing and thawing. These agents prevent cracks by introducing small air bubbles into the mix, creating spaces accommodating water expansion when temperatures drop. The air-entraining agents lower the surface tension of water, forming stable, small air bubbles. This method is more effective than having accidental large voids, as the intentional, smaller, and evenly distributed air voids improve...
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相关实验视频

Updated: Sep 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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基于深度学习的咳分类,使用应用程序记录的声音:与VGGishish的转移学习方法.

Sanghoon Han1, Yu-Rim Lee1, Ji-Ho Lee2

  • 1Waycen Inc, Seoul, 06167, Republic of Korea.

BMC medical informatics and decision making
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种深度学习模型,使用智能手机记录来检测异常咳. 该模型有助于早期诊断呼吸道疾病,改善患者和临床医生的可访问性和准确性.

关键词:
咳的分类咳的分类咳检测检测器 咳检测器深度学习是一种深度学习.医学诊断 医学诊断 医学诊断呼吸系统健康 呼吸系统健康基于智能手机的查维格格格格格格格格格格格格格格格格格格格

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科学领域:

  • 生物声学和医学信息学
  • 医疗保健中的人工智能
  • 呼吸道疾病诊断 呼吸道疾病诊断

背景情况:

  • 咳的声音包含重要的生物识别信息,用于评估呼吸系统疾病.
  • 由于并发症和死亡率高,早期诊断呼吸道疾病至关重要.
  • 建议采用深度学习方法来增强早期诊断和改善患者的治疗结果.

研究的目的:

  • 开发和评估一种深度学习模型,用于使用咳声音早期诊断呼吸系统疾病.
  • 评估临床专业知识和诊断输入对模型概括的影响.
  • 为临床医生和医疗保健服务有限的个人提供一个工具.

主要方法:

  • 一个使用VGGish进行转移学习的深度学习框架,包含检测和分类网络.
  • 咳事件通过检测模型被确定,并通过第二个模型被归类为正常或异常.
  • 模型接受了智能手机录制的咳声音的训练,由多名医学专家精心标记.

主要成果:

  • 咳检测模型实现了高精度 (0.9883).
  • 咳分类模型在三个数据集 (0.8417-0.8662) 中表现出强的性能.
  • 用Grad-CAM进行功能可视化,并使用AUROC和AUPRC验证性能.

结论:

  • 拟议的咳分类模型可以支持获得有限医疗保健的个人和经验较少的医疗专业人员.
  • 这种深度学习方法,利用智能手机记录的咳,有助于更早地检测和管理呼吸系统疾病.