释放人工智能在心电图生物识别方面的潜力:与年龄相关的变化,异常检测和移动健康平台的数据真实性
Kathryn E Mangold1, Rickey E Carter2, Konstantinos C Siontis1
1Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA.
European heart journal. Digital health
|May 22, 2024
概括
智能手机和手表现在可以记录心电图 (ECG),作为可穿戴健康监视器. 语神经网络可以从心电图中识别患者,确保电子健康记录中的数据准确性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 智能手机和手表等移动设备可以捕获单导电心电图 (ECG).
- 可穿戴式心电图提供了在临床环境之外远程心脏和健康监测的潜力.
- 在集成到电子健康记录之前,对心电图样本进行患者身份验证至关重要,特别是当设备共享时.
研究的目的:
- 评估姆神经网络在使用心电图样本作为诊断医学测试和生物识别标识符方面的有效性.
- 为了确定可穿戴设备的心电图数据是否可以验证患者身份.
主要方法:
- 一个语神经网络被应用来分析单导和12导心电图数据.
- 使用相似性得分来区分来自同一或不同患者的ECG对.
- 该模型使用来自心电图记录的中位数进行了测试.
主要成果:
- 西安神经网络在区分来自同一患者和不同患者的心电图方面取得了很高的准确性.
- 区分患者身份的曲线下的面积 (AUC) 为单导电心电图的0.94,为12导电心电图的0.97.
- 这些结果表明,使用ECG进行生物识别的表现强.
结论:
- 罗神经网络显示出使用移动设备的心电图数据验证患者身份的巨大潜力.
- 单和12ECG的可比性能突显了消费者可穿戴设备在心脏健康监测和识别方面的可行性.
- 这项技术可以提高远程患者监控系统的安全性和可靠性.
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An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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An ECG utilizes electrodes on the skin...
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