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

Journal of Human Hypertension volume 39, pages 735–736 (2025)Cite this article

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We read with interest the article by Angelaki et al. titled “Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach” [1]. Although the authors provide a unique method for hypertension detection based on machine learning with a single-lead ECG, a few limitations in the method and applications need to be addressed.

The definition of hypertension in the study seems to raise concerns. Hypertension is defined by the authors as an office BP greater than 140/90 mmHg on three consecutive visits or confirmed through out-of-office measurements. However, they do not specify the protocols used for these measurements, the time intervals between visits, or the specific out-of-office methodologies implemented. The 2020 International Society of Hypertension guidelines highlight standardized measurement protocols as fundamental to the accurate diagnosis of hypertension [2]. This methodological uncertainty may erode confidence in the ground truth labeling upon which the algorithm is trained.

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References

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Authors and Affiliations

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

    Muhammad Yousaf & Ali Hassan

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  1. Muhammad Yousaf
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  2. Ali Hassan
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Contributions

Muhammad Yousaf conceptualized the idea, critically appraised the original article, and contributed to manuscript revision. Ali Hassan conducted the literature review and drafted the manuscript. Both authors approved the final version and are accountable for all aspects of the work.

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Correspondence to Ali Hassan.

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Yousaf, M., Hassan, A. Critical appraisal of machine learning-based hypertension detection via single-lead electrocardiograms. J Hum Hypertens 39, 735–736 (2025). https://doi.org/10.1038/s41371-025-01060-2

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