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相关概念视频

Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

477
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:
477
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

419
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.
419
Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

275
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...
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Assessment of Respiration01:23

Assessment of Respiration

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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...
1.1K
Heart Sounds01:15

Heart Sounds

2.0K
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)...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

294
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Updated: Jul 8, 2025

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
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使用音频谱图视觉转换器识别异常的呼吸声.

Whenty Ariyanti, Kai-Chun Liu, Kuan-Yu Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    概括
    此摘要是机器生成的。

    一种新的人工智能方法,音频谱视觉变压器 (AS-ViT),可以准确识别呼吸声. 这种人工智能工具增强了肺部疾病的诊断,改善了患者对呼吸系统疾病的治疗结果.

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    科学领域:

    • 人工智能在医学中的应用
    • 呼吸系统医学 呼吸系统医学
    • 信号处理 信号处理

    背景情况:

    • 呼吸道疾病是全球主要的死亡原因,需要先进的诊断工具.
    • 目前用于诊断呼吸系统疾病的方法依赖于专业的专业知识,可以通过人工智能来增强.
    • 准确识别异常的肺声音对于及时诊断和治疗呼吸道疾病至关重要.

    研究的目的:

    • 开发和评估一种新的人工智能 (AI) 方法来识别异常的呼吸声.
    • 为了利用音频谱图和视觉变压器模型进行增强的呼吸声分类.
    • 将拟议方法的性能与现有的最先进技术进行比较.

    主要方法:

    • 呼吸声被转化为视觉表示 (光谱图) 使用短时间里埃变换 (STFT).
    • 使用音频谱视觉变压器 (AS-ViT) 模型来分析这些谱图以进行声音分类.
    • 采用ICBHI 2017数据库,包括多种肺声记录,用于模型培训和验证.

    主要成果:

    • AS-ViT模型在不同数据分割中在呼吸声检测方面取得了高性能.
    • 性能指标包括未加权的平均召回和整体得分,证明了该模型的有效性.
    • 拟议的AS-ViT方法在分类呼吸声方面超过了以前的最先进的结果.

    结论:

    • 开发的AS-ViT方法显示出对准确和自动检测异常呼吸道声音的显著前景.
    • 这种人工智能驱动的方法可以成为帮助医疗保健专业人员诊断肺部疾病的宝贵工具.
    • 进一步的研究和验证可能会导致人工智能在呼吸道诊断中的广泛临床应用.