远程患者监控系统结合了基于人工智能的硬件和软件
Kaan Kıvırcık1, Sibel Çimen2, Nilay Bulduk1
1Izmir Bakırçay University Faculty of Engineering and Architecture Department of Electrical and Electronics Engineering, 35660 Menemen, Izmir, Türkiye.
Biomedical physics & engineering express
|October 3, 2025
概括
这项研究引入了一种用于远程监测患者的新型人工智能系统,使用定制的深度神经网络 (DNN) 进行高度准确的心律失常检测. 该系统有助于医疗保健专业人员早期识别疾病.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 远程监测患者对于管理慢性疾病至关重要.
- 检测心律失常需要对生理数据进行准确和及时的分析.
- 现有的系统可能缺乏早期临床干预所需的精度.
研究的目的:
- 开发一种新的远程患者监控系统.
- 实现一个定制的深度神经网络 (DNN) 以提高心律失常检测.
- 为医疗保健提供者创建一个决策支持工具.
主要方法:
- 集成基于物联网的生物医学传感器,用于实时数据收集.
- 开发一个定制的深度神经网络 (DNN) 用于数据分析.
- 实施用户友好的移动和桌面接口,用于警报和摘要.
主要成果:
- 该DNN模型实现了测试准确率为99.94%,回忆率为99.92%,精度为99.57%.
- 该系统显示了非常低的虚假阳性和虚假阴性率.
- 集成到可访问的接口中的高性能分类.
结论:
- 开发的远程患者监测系统显示了临床应用的巨大潜力.
- 这种以人工智能为驱动的方法可以早期和可靠地检测心脏异常和心律失常.
- 该系统可以作为医疗保健人员的有价值的决策支持工具.
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