基于频域特征图的高血压靶器官损伤脉搏波预测模型
TP181; 针对高血压靶器官损伤时域脉搏波预测模型效率较低和分类精度较差的问题,本文提出了一种基于频域脉搏波特征图预测模型,实现高效无创辅助诊断.本文采用高斯滤波替换三角滤波,将脉搏波时域特征转换为频域矩阵特征图,并采用一种改进的SiMAM注意力机制模型EfficientNetS,提高脉搏波全局特征提取能力.608例临床高血压靶器官损伤脉搏波样本经5-Fold交叉验证后分类模型评估指标F1 score、Accuracy、Precision、Sensitivity、曲线下面积(Area under the curve,AUC)值分别为:97.31%、98.72%、97.71%、97.04%、9...
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| Published in | 数据采集与处理 Vol. 38; no. 4; pp. 898 - 914 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
上海理工大学光电信息与计算机工程学院,上海 200093%上海中医药大学基础医学院,上海 201203
01.07.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1004-9037 |
| DOI | 10.16337/j.1004-9037.2023.04.013 |
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| Abstract | TP181; 针对高血压靶器官损伤时域脉搏波预测模型效率较低和分类精度较差的问题,本文提出了一种基于频域脉搏波特征图预测模型,实现高效无创辅助诊断.本文采用高斯滤波替换三角滤波,将脉搏波时域特征转换为频域矩阵特征图,并采用一种改进的SiMAM注意力机制模型EfficientNetS,提高脉搏波全局特征提取能力.608例临床高血压靶器官损伤脉搏波样本经5-Fold交叉验证后分类模型评估指标F1 score、Accuracy、Precision、Sensitivity、曲线下面积(Area under the curve,AUC)值分别为:97.31%、98.72%、97.71%、97.04%、99.13%.与典型模型相比,本文方法具有较高的分类精度和泛化性能.此外,本文采用随机森林算法研究时域和频域特征与脉搏波分类相关性,深入挖掘潜在的影响高血压靶器官损伤分类的关键因素,发现高血压靶器官损伤的发病机理,为临床诊断提供有效支持. |
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| AbstractList | TP181; 针对高血压靶器官损伤时域脉搏波预测模型效率较低和分类精度较差的问题,本文提出了一种基于频域脉搏波特征图预测模型,实现高效无创辅助诊断.本文采用高斯滤波替换三角滤波,将脉搏波时域特征转换为频域矩阵特征图,并采用一种改进的SiMAM注意力机制模型EfficientNetS,提高脉搏波全局特征提取能力.608例临床高血压靶器官损伤脉搏波样本经5-Fold交叉验证后分类模型评估指标F1 score、Accuracy、Precision、Sensitivity、曲线下面积(Area under the curve,AUC)值分别为:97.31%、98.72%、97.71%、97.04%、99.13%.与典型模型相比,本文方法具有较高的分类精度和泛化性能.此外,本文采用随机森林算法研究时域和频域特征与脉搏波分类相关性,深入挖掘潜在的影响高血压靶器官损伤分类的关键因素,发现高血压靶器官损伤的发病机理,为临床诊断提供有效支持. |
| Author | 翁雯浩 齐晨浩 蔡书琛 燕海霞 尧明慧 杨晶东 |
| AuthorAffiliation | 上海理工大学光电信息与计算机工程学院,上海 200093%上海中医药大学基础医学院,上海 201203 |
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| Author_FL | YAO Minghui QI Chenhao YAN Haixia YANG Jingdong WENG Wenhao CAI Shuchen |
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| DocumentTitle_FL | Prediction of Pulse Wave for Target Organ Damage in Hypertension Based on Fre-quency-Domain Feature Maps |
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| Keywords | 高血压靶器官损伤 transfer learning 频域特征图 frequency-domain feature map hypertensive target organ damage pulse wave 迁移学习 脉搏波 |
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| Title | 基于频域特征图的高血压靶器官损伤脉搏波预测模型 |
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