使用农田生育算法与物联网环境中的混合深度学习模型进行自动化心律失常分类
Ahmed S Almasoud1, Hanan Abdullah Mengash2, Majdy M Eltahir3
1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了在物联网平台内使用混合深度学习和农田生育算法的自动心律失常分类系统. 这种方法增强了远程患者监测和早期发现异常心律的方法.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 能够实现集中式的医疗记录管理.
- 电心电图 (ECG) 对于诊断心脏病至关重要.
- 手动ECG分析心律失常是耗时且具有挑战性的.
研究的目的:
- 在物联网平台上展示一个使用混合深度学习和农田生育算法 (AAC-FFAHDL) 的自动化心律失常分类系统.
- 提高心律失常检测和分类的效率和准确性.
- 为了实现远程患者护理和持续监测心脏病状况.
主要方法:
- 数据预处理以标准化心电图信号.
- 混合深度学习 (HDL) 用于心律失常的检测和分类.
- 农田生育算法 (FFA) 用于对HDL模型进行超参数调整.
- 使用基准ECG数据库进行验证.
主要成果:
- AAC-FFAHDL系统在自动心律失常分类方面表现出了有希望的表现.
- 与其他模型相比,在各种评估指标上取得了优异的结果.
- 通过超参数调整的深度学习模型有效诊断心律失常.
结论:
- 拟议的AAC-FFAHDL方法为物联网环境中的自动心律失常分类提供了有效的解决方案.
- 突出了将先进的人工智能算法与物联网集成的潜力,以改善心脏护理.
- 支持远程患者监测和早期检测心律异常.
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