释放人工智能在心电图生物统计中的潜力:移动医疗平台中与年龄相关的变化、异常检测和数据真实性。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2024-04-23 eCollection Date: 2024-05-01 DOI:10.1093/ehjdh/ztae024
Kathryn E Mangold, Rickey E Carter, Konstantinos C Siontis, Peter A Noseworthy, Francisco Lopez-Jimenez, Samuel J Asirvatham, Paul A Friedman, Zachi I Attia
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引用次数: 0

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),这使得可穿戴设备成为医疗机构以外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,确认样本是否来自特定患者非常重要:我们试图确定连体神经网络的应用是否允许诊断性心电图样本同时作为医疗测试和生物识别标志。当使用相似性分数来区分一对心电图是来自同一患者还是不同患者时,输入单导联和 12 导联中位数的曲线下面积分别为 0.94 和 0.97:单导联和 12 导联配置的相似性突出了移动设备在监测心脏健康方面的潜在用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

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.

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