Visualization deep learning model for automatic arrhythmias classification

可解释性 判别式 人工智能 计算机科学 可视化 深度学习 机器学习 支持向量机 模式识别(心理学) 数据挖掘
作者
Mingfeng Jiang,Yujie Qiu,Wei Zhang,Jucheng Zhang,Zefeng Wang,Wei Ke,Yongquan Wu,Zhikang Wang
出处
期刊:Physiological Measurement [IOP Publishing]
卷期号:43 (8): 085003-085003 被引量:3
标识
DOI:10.1088/1361-6579/ac8469
摘要

Abstract Objective. With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic arrhythmias classification has become a research hotspot. The main purpose of this paper is to improve accuracy in detecting abnormal ECG patterns. Approach. A hybrid 1D Resnet-GRU method, consisting of the Resnet and gated recurrent unit (GRU) modules, is proposed to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, we use class-discriminative visualization to improve interpretability and transparency as an additional step. In this paper, the Grad-CAM++ mechanism has been employed to the trained network model and generate thermal images superimposed on raw signals to explore underlying explanations of various ECG segments. Main results. The experimental results show that the proposed method can achieve a high score of 0.821 (F1-score) in classifying 9 kinds of arrythmias, and Grad-CAM++ not only provides insight into the predictive power of the model, but is also consistent with the diagnostic approach of the arrhythmia classification. Significance. The proposed method can effectively select and integrate ECG features to achieve the goal of end-to-end arrhythmia classification by using 12-lead ECG signals, which can serve a promising and useful way for automatic arrhythmia classification, and can provide an explainable deep leaning model for clinical diagnosis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
天天快乐应助冰美式采纳,获得10
1秒前
en发布了新的文献求助30
2秒前
2秒前
拜了个拜拜完成签到 ,获得积分10
2秒前
gaoshen发布了新的文献求助10
3秒前
不配.应助体贴的青烟采纳,获得20
4秒前
小卢同学发布了新的文献求助10
4秒前
5秒前
6秒前
yyz应助wll采纳,获得10
7秒前
8秒前
9秒前
shengxingmoning完成签到 ,获得积分10
10秒前
QQQ发布了新的文献求助10
11秒前
脑洞疼应助dichloro采纳,获得10
11秒前
科研通AI2S应助自转无风采纳,获得10
12秒前
彩色的沛白完成签到 ,获得积分10
13秒前
乐乐应助萧奕尘采纳,获得10
14秒前
冰美式发布了新的文献求助10
14秒前
14秒前
wh雨完成签到,获得积分20
15秒前
叫我江从心就好了完成签到,获得积分10
16秒前
脑洞疼应助科研通管家采纳,获得10
16秒前
16秒前
顾矜应助科研通管家采纳,获得10
16秒前
小二郎应助科研通管家采纳,获得10
17秒前
不配.应助科研通管家采纳,获得20
17秒前
FashionBoy应助科研通管家采纳,获得10
17秒前
领导范儿应助科研通管家采纳,获得10
17秒前
xiaoming应助科研通管家采纳,获得10
17秒前
研友_VZG7GZ应助科研通管家采纳,获得10
17秒前
今后应助科研通管家采纳,获得10
17秒前
思源应助科研通管家采纳,获得30
17秒前
搜集达人应助科研通管家采纳,获得10
17秒前
研友_VZG7GZ应助科研通管家采纳,获得10
17秒前
CodeCraft应助科研通管家采纳,获得10
17秒前
搜集达人应助科研通管家采纳,获得10
17秒前
传奇3应助Equby采纳,获得10
18秒前
共享精神应助你好明天采纳,获得10
18秒前
19秒前
高分求助中
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Very-high-order BVD Schemes Using β-variable THINC Method 568
Chen Hansheng: China’s Last Romantic Revolutionary 500
XAFS for Everyone 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3138255
求助须知:如何正确求助?哪些是违规求助? 2789256
关于积分的说明 7790627
捐赠科研通 2445551
什么是DOI,文献DOI怎么找? 1300583
科研通“疑难数据库(出版商)”最低求助积分说明 625969
版权声明 601053