混合深度学习框架用于使用心电图信号图像预测心脏病
Sivabalaselvamani Dhandapani1, Hemalatha Somasundaram2, Tamilarasi Angamuthu2
1School of Information Science, Presidency University, Bengaluru, India. sivabalaselvamani@presidencyuniversity.in.
Scientific reports
|October 1, 2025
概括
这项研究引入了一种新的深度学习方法,使用人工神经网络 (ANN) 来从心电图 (ECG) 数据中准确检测心脏病. 该模型实现了高精度,改善了患者的治疗结果,并降低了医疗保健成本.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心血管疾病是全球主要的死亡原因,需要准确的诊断工具.
- 人类专业人员对心电图 (ECG) 的解释容易发生变化和错误.
- 深度学习为自动化和精确的心脏病识别提供了潜力.
研究的目的:
- 开发一种混合深度学习框架,用于使用人工神经网络 (ANN) 预测心脏病.
- 为了减少基于心电图的心脏疾病诊断中的计算复杂性.
- 使用人工神经网络 (DLA-ANNs) 创建深度学习方法,用于自动心脏疾病诊断.
主要方法:
- 使用Kaggle的心电图心跳分类数据集.
- 开发了一个混合深度学习框架,结合了人工神经网络模型.
- 提出了使用人工神经网络 (DLA-ANNs) 的深度学习方法.
主要成果:
- 与最先进的方法相比,基于ANN的设计显示出更高的准确性.
- 实现了高性能指标:93.6%的准确性,97.4%的灵敏性,98.2%的适应性,97.9%的性能和96.8%的可扩展性.
- 提出的方法是有效的,适合在医疗环境中实施.
结论:
- 开发的DLA-ANNs框架有效地通过ECG数据诊断心脏疾病.
- 混合深度学习方法在准确性,灵敏性和可扩展性方面提供了显著的改进.
- 这项技术有可能提高患者的治疗成果,降低医疗保健费用.
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