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相关概念视频

Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

195
Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
195
Assessment of Respiration01:23

Assessment of Respiration

1.0K
The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
1.0K
Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

253
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:
253
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

255
Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
255
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

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Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

1.3K
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: May 24, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

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边缘计算系统用于使用音频分析自动检测慢性呼吸道疾病.

José Antonio Rivas-Navarrete1, Humberto Pérez-Espinosa2, A L Padilla-Ortiz3,4

  • 1CICESE-UAT, CICESE, Andador 10 # 109, Tepic, 63173, Nayarit, México. jrivas@cicese.edu.mx.

Journal of medical systems
|March 4, 2025
PubMed
概括

这项研究开发了一种人工智能驱动的边缘计算系统,用于使用咳和呼吸声音检测慢性呼吸系统疾病 (CRD). 该系统实现了高精度,为偏远地区提供了潜在的低成本选工具.

关键词:
这是一个CDRCDR.慢性慢性肺炎是一种慢性慢性肺炎,COPD是一种慢性肺炎.边缘计算是一种边缘计算.机器学习是机器学习.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能
  • 呼吸系统医学 呼吸系统医学

背景情况:

  • 慢性呼吸道疾病 (CRD) 构成了全球卫生挑战,在偏远地区存在诊断局限性.
  • 呼吸系统的声音包含声学特征,表明CRDs.
  • 人工智能 (AI) 和边缘计算提供了新的诊断途径.

研究的目的:

  • 开发和评估基于音频的边缘计算系统,用于自主检测CRD.
  • 使用机器学习 (ML) 来分析呼吸声 (咳和呼吸).
  • 评估系统在智能手机和树派 (Raspberry Pi) 等边缘设备上的性能.

主要方法:

  • 开发一个采用ML算法的边缘计算系统.
  • 呼吸声特征的分析,包括Mel频率塞普斯特拉系数 (MFCC) 和染色图.
  • 在86名个体的数据集上进行培训和测试 (53名有CRD,33名健康),最终对35名个体进行评估.

主要成果:

  • 该系统在CRD检测方面表现出高灵敏度 (90.0%) 和特异性 (93.55%).
  • 在分类健康和患病的个体中达到91.75%的平衡准确度.
  • 在边缘设备上成功对呼吸道声音进行分类.

结论:

  • 边缘计算和ML系统显示了呼吸系统疾病检测的巨大潜力.
  • 开发的系统可以作为一个高效和具有成本效益的选工具.
  • 这项技术可以提高CRD诊断的可访问性,特别是在服务不足的地区.