基于云计算的学习分析,用于实时健康监测
Abdi Dharma1, Poltak Sihombing1, Syahril Efendi1
1Department of Computer Science, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia.
Journal of biomedical physics & engineering
|September 4, 2025
概括
新的智能便携式支架可以准确地检测到98%的心脏事件. 这种无线设备提供方便的实时心脏监测,
科学领域:
- 心脏病和医疗器械
- 生物医学工程
- 在医疗保健中的机器学习
背景情况:
- 心血管疾病的发病率上升需要改善心脏健康监测解决方案.
- 传统的霍尔特显示器在体积和使用方便方面存在局限性.
研究的目的:
- 推出一个新的无线设备, 智能便携式支架, 实时心脏监测.
- 提高心脏不规则的早期检测,提高准确性和使用方便性.
- 借助机器学习进行先进的心脏事件分析.
主要方法:
- 通过无线,便携式设备获得持续心电图 (ECG) 信号.
- 基于云的安全数据传输和处理.
- 使用机器学习模型进行分析:随机森林和极端梯度增强 (XGBoost).
主要成果:
- 在检测心脏事件方面,智能便携式持有器实现了98%的准确性.
- 使用现实数据集识别心律失常的可靠性很高.
- 设备的紧无线设计显著提高了用户的舒适性和合规性.
结论:
- 智能便携式控制器代表着便携式心脏监测的重大进步.
- 结合便携性,实时分析和高诊断准确性,
- 让患者和医疗保健提供者获得可操作的洞察力,以便及早诊断和个性化治疗.
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