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Critical appraisal of machine learning-based hypertension detection via single-lead electrocardiograms.

作者信息

Yousaf Muhammad, Hassan Ali

机构信息

Department of Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan.

出版信息

J Hum Hypertens. 2025 Aug 11. doi: 10.1038/s41371-025-01060-2.

DOI:10.1038/s41371-025-01060-2
PMID:40789929
Abstract
摘要

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本文引用的文献

1
Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.单导联心电图对动脉高血压诊断的诊断性能：一种机器学习方法。
J Hum Hypertens. 2025 Jan;39(1):58-65. doi: 10.1038/s41371-024-00969-4. Epub 2024 Oct 18.
2
Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms.人工智能在心电图检测低射血分数中的外部验证和亚组分析的重要性。
Eur Heart J Digit Health. 2022 Nov 2;3(4):654-657. doi: 10.1093/ehjdh/ztac065. eCollection 2022 Dec.
3
Application of artificial intelligence to the electrocardiogram.
人工智能在心电图中的应用。
Eur Heart J. 2021 Dec 7;42(46):4717-4730. doi: 10.1093/eurheartj/ehab649.
4
A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms.一种用于从原始心电图和光电容积脉搏波波形连续且无创估计血压的混合神经网络。
Comput Methods Programs Biomed. 2021 Aug;207:106191. doi: 10.1016/j.cmpb.2021.106191. Epub 2021 May 21.
5
Automated Detection of Hypertension Using Physiological Signals: A Review.利用生理信号自动检测高血压：综述
Int J Environ Res Public Health. 2021 May 29;18(11):5838. doi: 10.3390/ijerph18115838.
6
2020 International Society of Hypertension Global Hypertension Practice Guidelines.2020年国际高血压学会全球高血压实践指南
Hypertension. 2020 Jun;75(6):1334-1357. doi: 10.1161/HYPERTENSIONAHA.120.15026. Epub 2020 May 6.