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释放人工智能在心电图生物识别中的潜力：移动健康平台中的年龄相关变化、异常检测和数据真实性

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

作者信息

Mangold Kathryn E, Carter Rickey E, Siontis Konstantinos C, Noseworthy Peter A, Lopez-Jimenez Francisco, Asirvatham Samuel J, Friedman Paul A, Attia Zachi I

机构信息

Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA.

Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.

出版信息

Eur Heart J Digit Health. 2024 Apr 23;5(3):314-323. doi: 10.1093/ehjdh/ztae024. eCollection 2024 May.

DOI:10.1093/ehjdh/ztae024
PMID:38774362
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11104462/
Abstract

AIMS

Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record.

METHODS AND RESULTS

We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively.

CONCLUSION

The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

摘要

目的

智能手机和手表等移动设备现在可以记录单导联心电图（ECG），使可穿戴设备成为医疗保健环境之外用于心脏和健康监测的潜在筛查工具。由于朋友和家人经常共享他们的智能手机和设备，因此在将样本添加到电子健康记录之前，确认样本来自特定患者非常重要。

方法和结果

我们试图确定连体神经网络的应用是否会使诊断性心电图样本既作为医学检测又作为生物识别标识符。当使用相似度分数来区分一对心电图是否来自同一患者或不同患者时，单导联和12导联中位数的输入分别产生了0.94和0.97的曲线下面积。

结论

单导联和12导联配置的相似性能突出了移动设备在监测心脏健康方面的潜在用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/61837083576c/ztae024f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/87d20547ff94/ztae024_ga.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/f825dcc5a5a8/ztae024f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/c4bab19b29f0/ztae024f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/e80a18069dd1/ztae024f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/5847cae067d5/ztae024f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/6d575f173b66/ztae024f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/61837083576c/ztae024f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/87d20547ff94/ztae024_ga.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/f825dcc5a5a8/ztae024f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/c4bab19b29f0/ztae024f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/e80a18069dd1/ztae024f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/5847cae067d5/ztae024f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/6d575f173b66/ztae024f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fb4/11104462/61837083576c/ztae024f6.jpg

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Artificial Intelligence-Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure.
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