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相关概念视频

Disturbances in Heart Rhythm01:28

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
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基于人工智能的心房动识别方法用于运动人工物污染的心电图信号,通过自适应过算法进行预处理.

Huanqian Zhang1, Hantao Zhao2, Zhang Guo2,3

  • 1Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
概括

适应性过显著改善了人工智能检测心电图 (ECG) 信号中的心房动 (AF) 被运动工件 (MA) 损坏的信号. 这种方法提高了AF识别的准确性,这对于可穿戴的长期心电图监测至关重要.

关键词:
适应性过是一种自适应性过.人工智能的人工智能是人工智能.心房动是心房动的一种.电心电图 (ECG) 是一种心电图.运动工艺品 运动工艺品

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科学领域:

  • 生物医学工程 生物医学工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 前庭动 (AF) 的检测依赖于长期的心电图监测.
  • 电脑心电图信号中的运动器件 (MA) 阻碍了准确的AF诊断.
  • 现有的AI算法在清洁的ECG数据中有效检测AF.

研究的目的:

  • 评估自适应过 (ADF) 对基于AI的AF识别准确性的影响.
  • 确定ADF是否可以增强AI性能,以在噪音高的心电图信号中检测AF.

主要方法:

  • 人工引入了13种不同信号与噪声比的MA信号类型到AF ECG数据集中.
  • 评估了AI AF在MAs的ECG上识别准确度.
  • 应用ADF去除MA并重新评估AI AF识别准确性.

主要成果:

  • 由于MA的存在,AI AF识别的准确性最初会降低.
  • 在ADF后处理,AI AF识别精度在所有MA强度中得到改善.
  • 在ADF应用后,最大的精度改进达到60%.

结论:

  • 适应性过是有效的缓解运动工件干扰在心电图信号.
  • ADF预处理提高了用于检测心房动的AI算法的准确性.
  • 这种方法有望改善AF的可靠,长期可穿戴的ECG监测.