知识与财富的链接
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。
For less efficiency and low accuracy of predicting on hypertensive target organ damage, this paper proposes a prediction of hypertensive pulse wave based on mel frequency ceptral coefficient （MFCC）-based feature maps to accomplish the efficient and non-invasive diagnosis on target organ damage. For low accuracy of pulse-taking classification in temporal domain, pulse wave is transformed to the MFCC-based feature maps in frequency domain via replacing angular filter with Gaussian filter, an improved EfficientNet model, EfficientNetS is employed to enhance the ability of global feature extraction via adding the improved SiMAM attention mechanism. The clinical 608 cases of hypertension target organ damage concerning pulse-taking diagnosis are used. The evaluation indicators of five-fold cross-validation classification, i.e. F1 score, accuracy, precision, sensitivity, area under the curve （AUC）, are 97.31%, 98.72%, 97.71%, 97.04%, 99.13%, respectively. Compared to the typical models, the proposed method has higher classification accuracy and generalization performance. In addition, this paper also studies the correlation between classification of pulse wave and its features, and analyzes the feature importance ranking in temporal domain and frequency domain of pulse-taking, which can help clinicians seek the occurrence mechanisms of hypertension caused by target organ damage, and find the effective measurements for timely prevention and treatment.
