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基于Bi-LSTM与状态约束的心音分割算法

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心音分割是进行准确心音分类的前提.针对心音分割,提出一种基于双向长短时记忆网络(Bi-LSTM)与状态约束的算法.该文通过网格法确定Bi-LSTM网络中的最佳参数,并训练出心音状态识别模型;统计Bi-LSTM预测的心音状态持续时间,并计算自相关参数;利用 自相关参数和心音固有状态转移规则对预测的心音状态进行约束处理.使用五折交叉验证法在PhysioNet/CinC 2016数据集上进行测试,该算法与同类算法相比,整体性能更佳.
HEART SOUND SEGMENTATION ALGORITHM BASED ON BI-LSTM AND STATE CONSTRAINTS
Heart sound segmentation is a prerequisite for accurate heart sound classification.Aimed at heart sound segmentation,an algorithm based on Bi-LSTM and state constraints is proposed.The optimal parameters of Bi-LSTM network were determined by grid method,and the heart sound state recognition model was trained.The duration of the heart sound state predicted by Bi-LSTM was counted,and the autocorrelation parameters were calculated.The autocorrelation parameters and heart sound inherent state transition rules were used to constrain the predicted heart sound state.Using the five-fold cross-validation method to test on the PhysioNet/CinC2016 data set,the algorithm has better overall performance than similar algorithms.

PhonocardiogramHeart sound segmentationBi-LSTM networkState constraintAutocorrelation

王幸之、杨宏波、宗容、潘家华、王威廉、谭贺飞

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云南大学信息学院 云南 昆明 650500

昆明医科大学 云南 昆明 650500

云南省阜外心血管病医院结构心脏病病区 云南 昆明 650102

心音图 心音分割 Bi-LSTM网络 状态约束 自相关

国家自然科学基金项目云南省重大科技专项项目

819600672018ZF017

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(10)