[1]罗望成,杨 湘,陈艳红.基于注意力机制的可解释心律失常分类模型[J].计算机技术与发展,2022,32(09):114-120.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 018]
　LUO Wang-cheng,YANG Xiang,CHEN Yan-hong.Interpretable Arrhythmia Classification Model Based on Attention Mechanism[J].,2022,32(09):114-120.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 018]
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基于注意力机制的可解释心律失常分类模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年09期
页码:
114-120
栏目:
人工智能
出版日期:
2022-09-10

文章信息/Info

Title:
Interpretable Arrhythmia Classification Model Based on Attention Mechanism
文章编号:
1673-629X(2022)09-0114-07
作者:
罗望成1 ; 杨 湘1 ; 陈艳红2
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065
2. 武汉亚洲心脏病医院,湖北 武汉 430022
Author(s):
LUO Wang-cheng1 ; YANG Xiang1 ; CHEN Yan-hong2
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China
2. Wuhan Asia Heart Hospital,Wuhan 430022,China
关键词:
心律失常分类; 注意力机制; 卷积神经网络; 可解释性; 长短期记忆网络
Keywords:
arrhythmia classification; attention mechanism; convolutional neural network; interpretability; long short term memory
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 09. 018
摘要:
心律失常是心血管疾病中常见的临床表现形式,实现心律失常的自动分类在医学领域具有重要意义。 在实际临床中,医生除了提供诊断结果,还要有详细的解释来支持自己的诊断,但是现有的大多数机器学习模型都忽略了结果的可解释性。 同时,之前大部分研究致力于宏观分类,实际临床意义不大。 为了解决这些问题,提出了一种可解释的基于注意力的混合深度学习模型( IAHM) 。 IAHM 通过分别提取心拍级别和心律级别的注意力特征,将医学知识和心电数据相结合,使学习的模型具有高度的可解释性。 实验在公开数据库 MIT-BIH 上进行,对五种心律失常分类以弥补宏观分类的短板。 IAHM 在准确率、特异性、敏感性和阳性预测值分别达到 94. 65% 、98. 69% 、92. 69% 和 92. 60% ,有助于临床医生对心律失常进行准确分类。
Abstract:
Automatic arrhythmia classification is of great significance in medical area with arrhythmias being common clinical manifestations of cardiovascular diseases. In the actual clinical environment,in addition to providing diagnostic results,cardiologists also need detailed explanations to support their diagnosis. However,most of the existing models ignore the interpretability. Meanwhile,mucheffort is devoted to the classification of macro-classes,which lacks of practical clinical significance. To address such is sues,a novel inter pretable attention-based hybrid deep learning model ( IAHM) is proposed. By extracting the attention features of beat-level and rhythm-level respectively,IAHM combines the medical knowledge and ECG data,making the learned model highly interpretable. The experiment is conducted on the public database MIT-BIH,and 5 categories of arrhythmia are classified to make up for the shortcomings of the macro classification. The accuracy, specificity,sensitivity and positive predictive value of IAHM have reached 94. 65% ,98. 69% ,92. 69% and92. 60% ,respectively,which can thus help clinicians to classify arrhythmias accurately.

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更新日期/Last Update: 2022-09-10