基于频域特征图的脉冲波对高血压目标器官损伤的风险预测
Jingdong Yang1, Jiangtao Lü1, Zehao Qiu1
1Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Medical engineering & physics
|April 15, 2024
概括
这项研究介绍了MobileNetV2SCP,这是一种深度学习模型,用于将传统中医 (TCM) 脉冲波分类为高血压目标器官损伤 (TOD). 该模型实现了高精度,改善了这种疾病的诊断.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 对于传统中医药 (TCM) 在高血压目标器官损伤 (TOD) 中的脉冲波分析,深度学习面临着准确性和概括性的挑战.
- 现有的模型在小样本大小和捕捉复杂的脉冲波特征方面扎.
研究的目的:
- 开发一种轻量级的转移学习模型 (MobileNetV2SCP),以改进与高血压TOD相关的脉冲波的分类.
- 提高模型对小数据集的学习能力,改善全球特征相关性.
主要方法:
- 将时间域脉冲波转换为36维的频域特征图.
- 使用这些特征地图开发了一个专门的预培训网络.
- 整合了一种新的聚变注意力机制 (SAS) 和卷积层,以减轻过度拟合和改善特征相关性.
主要成果:
- 在805例高血压TOD病例中,MobileNetV2SCP模型实现了高性能指标:准确性 (92.74%),F1得分 (91.47%) 和曲线下的面积 (AUC) (97.12%).
- 与最先进的模型相比,证明了更高的分类准确性和概括性.
- 确定了影响高血压TOD脉冲波分类的关键因素.
结论:
- 移动NetV2SCP模型为高血压TOD脉冲波的准确和可概括的分类提供了显著的进步.
- 通过分析脉冲波特征,为TOD的临床诊断提供了宝贵的见解.
- 强调深度学习和注意力机制在基于TCM的诊断中的潜力.
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