生物医学工程研究2025，Vol.44Issue(4)：221-228,237,9.DOI:10.19529/j.cnki.1672-6278.2025.04.04
基于柯氏音时相分类的血压测量方法
Blood pressure measurement based on temporal phase classification of Korotkoff sound
摘要
Abstract
To enhance blood pressure measurement accuracy,we proposed a Korotkoff sound phase classification model based on deep learning,and designed a blood pressure measurement method based on Korotkoff sound phase classification.Firstly,369 pieces of Korotkoff sound data from 102 healthy volunteers were collected,and manual auscultation was used to label different time phases.Sec-ondly,the log-mel spectrogram and Hilbert envelope features of the Korotkoff sound signal were extracted,and combined with ResNet18,convolutional block attention module(CBAM),bidirectional long short-term memory network(BiLSTM)and multi-head self-attention module,the features of the Korotkoff sound signal were fully learned.Finally,on the basis of the Korotkoff sound phase classi-fication,blood pressure measurement was completed.The experimental results showed that the average classification accuracy of the Korotkoff sound phase reached 88.9%.The blood pressure measurement method in the research met the A-level standard set by the British Society of Hypertension(BHS)under the four blood pressure measurement standards,and the intraclass correlation coefficient(ICC)was greater than 0.95,providing reference significance for the research of automatic blood pressure measurement methods.关键词
柯氏音/血压测量/多模态特征融合/深度学习/注意力机制Key words
Korotkoff sound/Blood pressure measurement/Multimodal feature fusion/Deep learning/Attention mechanism分类
医药卫生引用本文复制引用
寇文逸,李世龙,蒋志宇,李莉,赵启军,潘帆..基于柯氏音时相分类的血压测量方法[J].生物医学工程研究,2025,44(4):221-228,237,9.基金项目
国家自然科学基金项目(62066042) （62066042）
四川省重点研发项目(2024YFFK0051). （2024YFFK0051）
