Blood pressure measurement based on temporal phase classification of Korotkoff sound
KOU Wenyi
LI Shilong
JIANG Zhiyu
LI Li
ZHAO Qijun
PAN Fan
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.
Keywords：Korotkoff soundBlood pressure measurementMultimodal feature fusionDeep learningAttention mechanism
Publication Date：2025-08-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：9( 221-228,237 )