基于深度学习的心脏疾病分类和智能医疗保健系统的用户身份验证,使用心电图信号
Tong Ding1, Chenhe Liu2, Jiasheng Zhang3
1School of Engineering, University of New South Wales, Sydney, Australia.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一个智能远程医疗系统,使用深度学习进行远程心脏监测. 它准确地检测心脏病并通过心电图验证用户,改善患者护理和数据安全.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 及时诊断心脏活动异常对于预防严重的健康并发症和拯救生命至关重要.
- 智能远程医疗系统提供心脏病的远程,非侵入性监测,改变了医疗保健的提供.
- 支持物联网 (IoT) 的心电图 (ECG) 监视器是收集和分析心脏数据以进行早期检测的关键.
研究的目的:
- 开发一种高效的深度学习模型,使用心电图数据对心血管问题进行分类.
- 为了提高心脏疾病检测的精度和分类准确性.
- 实施一个安全的远程医疗系统,使用ECG信号实现用户识别功能.
主要方法:
- 使用了一个基于云的远程医疗系统,与支持物联网的心电图监视器集成.
- 深度学习模型,特别是卷积神经网络 (CNN) 和长期短期记忆 (LSTM),用于ECG信号分析.
- 应用了数据预处理和增强技术,以提高模型训练的数据质量和数量.
主要成果:
- 拟议的LSTM模型在分类心脏病方面实现了99.5%的准确性.
- 该LSTM模型在使用心电图信号的用户身份验证中显示了98.6%的准确性.
- 与传统的机器学习和CNN模型相比,开发的系统显示出更高的性能.
结论:
- 智能远程医疗系统有效地对心血管问题进行分类和检测,准确度高.
- 该系统通过ECG数据可靠的用户识别来增强安全性和隐私.
- 这种方法意味着心脏远程监测和诊断的重大进步.
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