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相关概念视频

Pulse rhythm01:30

Pulse rhythm

783
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
783
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

933
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...
933
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

910
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
910

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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机器学习工作流程用于在探索类任务中的边缘计算失律检测.

Cyril Mani1, Tanya S Paul2, Patrick M Archambault3

  • 1Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.

NPJ microgravity
|June 22, 2024
PubMed
概括

这项研究介绍了一种新型的自优化机器学习管道,用于检测心律失常,如心房动,使用边缘设备上的心电图 (ECG) 数据. 优化的模型可以为深空任务实现精确的心力衰竭检测.

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科学领域:

  • 航空航天医学 航空航天医学
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 深空任务需要远程健康监测和宇航员病理的预测诊断.
  • 边缘计算和开放神经网络交换 (ONNX) 格式对于远程环境中的设备推断至关重要.

研究的目的:

  • 开发和验证一种自优化机器学习管道,用于在可穿戴边缘设备上对心律失常 (正常鼻腔节律,心房动,心房) 进行分类.
  • 评估在太空任务中部署ONNX优化模型用于实时点诊断的可行性.

主要方法:

  • 处理了742小时的心电图 (ECG) 记录,使用可变模式分解来消除噪音.
  • 通过峰值检测和离散波束转换提取了17个心率变化和形态心电图特征.
  • 使用了自我优化的决策树分类器,并进行了分层的三重嵌套交叉验证,通过F1评分对心脏病学家标签进行了优化.

主要成果:

  • 实现了0.899的宏F1得分,正常鼻节奏 (0.993) 和心房 (0.938) 的高得分,以及心房的0.767.
  • 确定了关键特征,包括中位P波幅,PRR20和平均心率.
  • 该ONNX翻译管道在9.2秒内处理了样本,证明了高效的运行性心跳动脉检测.

结论:

  • 自动优化训练方案和ONNX部署的结合使心律失常的准确,在设备上的分类成为可能.
  • 这种方法为深空任务中的预防性护理和实时健康监测提供了可行的解决方案.