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少样本学习在心律失常检测中的应用综述.

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Název: 少样本学习在心律失常检测中的应用综述. (Chinese)
Alternate Title: Review of few-shot learning in arrhythmia detection. (English)
Autoři: 甘戴楠, 詹晓林, 黄 丽, 李 嘉
Zdroj: Chinese Medical Equipment Journal; Aug2025, Vol. 46 Issue 8, p104-112, 9p
Abstract (English): The advantages of few-shot learning in arrhythmia detection were introduced. The research progress of few-shot learning strategies, including metric learning, transfer learning and data augmentation, was reviewed when applied toarrhythmia detection. The limitations of few-shot learning in arrhythmia detection were analyzed. It was pointed out that the graph neural network and new incremental learning techniques would be involved in the future development of few-shot learning in arrhythmia detection. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 介绍了少样本学习在检测心律失常中的应用优势, 综述了度量学习、迁移学习及数据增强等少样本学习策 略在心律失常检测中的应用现状, 分析了少样本学习应用于心律失常检测的局限性, 指出了探索图神经网络和开 发新型增量学习技术是未来的发展方向。 [ABSTRACT FROM AUTHOR]
Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Biomedical Index
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PubType: Academic Journal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: 少样本学习在心律失常检测中的应用综述. (Chinese)
– Name: TitleAlt
  Label: Alternate Title
  Group: TiAlt
  Data: Review of few-shot learning in arrhythmia detection. (English)
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22甘戴楠%22">甘戴楠</searchLink><br /><searchLink fieldCode="AR" term="%22詹晓林%22">詹晓林</searchLink><br /><searchLink fieldCode="AR" term="%22黄+丽%22">黄 丽</searchLink><br /><searchLink fieldCode="AR" term="%22李+嘉%22">李 嘉</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Chinese Medical Equipment Journal; Aug2025, Vol. 46 Issue 8, p104-112, 9p
– Name: AbstractNonEng
  Label: Abstract (English)
  Group: Ab
  Data: The advantages of few-shot learning in arrhythmia detection were introduced. The research progress of few-shot learning strategies, including metric learning, transfer learning and data augmentation, was reviewed when applied toarrhythmia detection. The limitations of few-shot learning in arrhythmia detection were analyzed. It was pointed out that the graph neural network and new incremental learning techniques would be involved in the future development of few-shot learning in arrhythmia detection. [ABSTRACT FROM AUTHOR]
– Name: AbstractNonEng
  Label: Abstract (Chinese)
  Group: Ab
  Data: 介绍了少样本学习在检测心律失常中的应用优势, 综述了度量学习、迁移学习及数据增强等少样本学习策 略在心律失常检测中的应用现状, 分析了少样本学习应用于心律失常检测的局限性, 指出了探索图神经网络和开 发新型增量学习技术是未来的发展方向。 [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.19745/j.1003-8868.2025151
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      – Code: chi
        Text: Chinese
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      – TitleFull: 少样本学习在心律失常检测中的应用综述.
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            NameFull: 甘戴楠
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            NameFull: 詹晓林
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            NameFull: 黄 丽
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            NameFull: 李 嘉
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              Text: Aug2025
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              Y: 2025
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